User has approved your plan. You can now start coding. Start with updating your todo list if applicable

Your plan has been saved to: /root/.claude/plans/scalable-doodling-peach.md
You can refer back to it if needed during implementation.

## Approved Plan:
# Delta Engineering Deep Analysis — Prompt Strategy Guide

## Context

You have ~296 email threads with Delta Engineering (delta-engineering.be) involving the full team and CEO. Goal: deep analysis to understand what went wrong, identify business mistakes, detect pressure tactics, profile key people psychologically, and develop a strategy to re-gain them as clients.

## Pre-Requisites: Clean Up First

1. **Delete junk threads first** — Search "Address not matched by Google GeoCoding" in dashboard threads → "Delete All 121 Found" to remove noise
2. **Set content mode to "stripped"** — saves tokens, removes redundant quoted replies (the system already strips quotes for additional threads via `thread_for_ai`)

---

## Recommended Analysis Strategy: 5-Round Deep Dive

### Step 1: Pick a Key Thread & Add ALL Delta Threads as Context

1. Open the most important/recent Delta thread in thread_view
2. Open Context Builder → search "delta-engineering.be" → click **"+ Add All 296 Threads"**
3. This gives the AI the entire relationship history

**Important:** With 296 threads, even stripped, you'll be near token limits. Use models with large context windows:
- **Gemini 2.5 Pro** (1M tokens) or **GPT-4.1** (1M tokens) — best for massive context
- **Claude Opus 4.6** (200K) — best reasoning but smaller window
- Recommendation: Select **Gemini 2.5 Pro + Claude Opus 4.6** together, enable **Two-Round mode** so Round 2 synthesizes both perspectives

---

### Step 2: Run 5 Analysis Passes (one per agent)

#### Pass 1: General Agent — Business Relationship Timeline
```
Analyze the COMPLETE email history between us and Delta Engineering chronologically. Build a detailed timeline of the business relationship:

1. KEY MILESTONES: When did the relationship start? What were the major projects/deals? When did things start going wrong? When did we lose them?
2. TURNING POINTS: Identify the exact moments where the relationship shifted negatively. Quote specific emails.
3. OUR MISTAKES: What did we do wrong? Late deliveries, missed expectations, poor communication, pricing issues? Be brutally honest.
4. THEIR SATISFACTION CURVE: Rate their satisfaction (1-10) at each major point in the timeline.
5. UNRESOLVED ISSUES: What was never properly addressed or closed?
6. LAST INTERACTION: What was the tone and content of our last exchange? Did we leave the door open?
```

#### Pass 2: Deception Agent — Pressure & Manipulation Detection
```
Analyze ALL emails from Delta Engineering team members for:

1. PRESSURE TACTICS: Where did they push unreasonable deadlines, scope changes, or price reductions? Quote specific examples.
2. BROKEN PROMISES from their side: Did they commit to volumes, timelines, payments that didn't materialize?
3. DOUBLE STANDARDS: Did they hold us to standards they didn't meet themselves?
4. ESCALATION PATTERNS: When and why did they escalate to the CEO? Was it justified or a power play?
5. BLAME SHIFTING: Did they blame us for problems that were partially/fully their responsibility?
6. GOOD FAITH SIGNALS: To be fair — where were they genuinely reasonable or helpful?

Rate each Delta team member's negotiation tactics: FAIR / AGGRESSIVE / MANIPULATIVE with evidence.
```

#### Pass 3: Profiler Agent — Psychological Profiles of Key People
```
Build detailed psychological and business profiles for EACH Delta Engineering person in these emails. For each person provide:

1. NAME & ROLE (infer from email signatures and context)
2. COMMUNICATION STYLE: Formal/casual, direct/indirect, emotional/analytical
3. DECISION-MAKING: Do they decide alone or need approval? Fast or slow? Risk-averse?
4. WHAT MOTIVATES THEM: Price? Quality? Relationships? Status? Control?
5. HOW THEY HANDLE CONFLICT: Confrontational, avoidant, diplomatic, passive-aggressive?
6. THEIR INFLUENCE: Who has real power vs who is just executing?
7. THEIR ATTITUDE TOWARD US: Positive, neutral, negative? Did it change over time?
8. APPROACH STRATEGY: How should we approach THIS specific person to rebuild the relationship?

Pay special attention to the CEO — what drives them, what frustrates them, what would win them back?
```

#### Pass 4: Quote Analyst — Financial & Business Pattern Analysis
```
Analyze the complete financial and business history with Delta Engineering:

1. ALL PRICES QUOTED: Every amount, every project, every change order — create a table
2. PRICING TRENDS: Did our prices go up/down? Did they push for discounts? How much did we concede?
3. SCOPE CREEP: Where did work expand beyond original quotes without matching price increases?
4. PAYMENT BEHAVIOR: Were they prompt payers? Any disputes over invoices?
5. VOLUME vs COMMITMENT: Did they promise volume that didn't materialize?
6. COMPETITOR MENTIONS: Did they mention other suppliers? Use competitor pricing as leverage?
7. PROFITABILITY ASSESSMENT: Based on the patterns, were they a profitable client or were we losing money?
8. RE-ENGAGEMENT PRICING: What pricing strategy should we use to win them back without repeating past mistakes?
```

#### Pass 5: General Agent — Win-Back Strategy (final synthesis)
```
Based on everything in these emails, create a CONCRETE win-back strategy for Delta Engineering:

1. ROOT CAUSE SUMMARY: In 3 sentences, why did we lose them?
2. WHAT THEY VALUED: What did they actually appreciate about working with us?
3. WHAT THEY HATED: What were their consistent pain points?
4. COMPETITIVE POSITION: What would make them choose us over their current supplier?
5. APPROACH PLAN:
   - WHO to contact first (and why that person)
   - WHEN (timing considerations based on their business cycle)
   - HOW (email, call, in-person meeting?)
   - WHAT to say (draft an opening message)
   - WHAT TO OFFER (specific value proposition addressing their known pain points)
6. RED LINES: What should we NOT agree to if they come back? What terms caused problems before?
7. SUCCESS METRICS: How will we know the re-engagement is working?

Be specific. Reference actual names, actual issues, actual quotes from the emails.
```

---

### Step 3: Use Two-Round Mode for Each Pass

Enable Two-Round AI for each pass:
- **Round 1**: Gemini 2.5 Pro + Claude Opus 4.6 analyze independently
- **Round 2**: Both models synthesize, highlighting where they agree and disagree
- This gives you the most thorough analysis — two different AI architectures catching different patterns

---

### Step 4: Generate Relationship Chart

After the Pass 1 (timeline) analysis, click **"Generate Chart"** to create an interactive D3.js visualization of:
- Key people and their connections
- Major events/turning points
- Relationship dynamics

---

### Step 5: Save Contact Memories

After analysis, add the most important findings as Contact Memories for each Delta person. This way, every future analysis automatically includes these insights.

---

## Pro Tips

| Tip | Why |
|-----|-----|
| Run passes in order (1→5) | Each builds on conversation history — the AI remembers previous answers |
| Don't clear conversation between passes | The ChatGPT-like memory (up to 20 exchanges) carries context forward |
| Use "Additional Context" field | Add your own notes like "We lost this client in [month]. I think [X] was the main issue" — gives the AI your perspective to validate/challenge |
| Content mode: "stripped" | Essential with 296 threads — "full" mode would blow past any model's limit |
| Start with the longest/most complex thread | This gives the richest base context before adding the other 295 as additional |
| Save good responses | Click "Save" on responses you want to reference later |

## Model Selection Guide for This Task

| Model | Best For | Context |
|-------|----------|---------|
| Gemini 2.5 Pro | Massive context (1M), good at patterns across many emails | Use for all passes |
| Claude Opus 4.6 | Deepest reasoning, best psychological analysis | Use for Profiler & Deception passes |
| GPT-4.1 | 1M context, strong at structured extraction | Good alternative to Gemini |
| GPT o3 | Deep reasoning like Opus | Good for Win-Back strategy pass |

**Recommended combo per pass:** Gemini 2.5 Pro + Claude Opus 4.6, Two-Round enabled


This is a strategy guide, not code changes — nothing to implement. Here's the summary:

## Quick Start

**Before analyzing:** Delete the 121 "Address not matched by Google GeoCoding" junk threads first (search in dashboard, use "Delete All Found").

## 5-Pass Analysis Strategy

Run these in order (conversation history carries between passes):

| Pass | Agent | Focus |
|------|-------|-------|
| 1 | General | Business timeline — milestones, turning points, your mistakes, their satisfaction curve |
| 2 | Deception | Pressure tactics, broken promises, blame shifting, escalation patterns |
| 3 | Profiler | Psychological profiles of every Delta person — CEO focus, motivations, approach strategy |
| 4 | Quotes | Financial patterns — pricing trends, scope creep, payment behavior, competitor mentions |
| 5 | General | Win-back strategy — who to contact, when, how, what to offer, red lines |

## Setup for Each Pass

1. Open a key Delta thread → Context Builder → search "delta-engineering.be" → **"+ Add All" threads**
2. Select **Gemini 2.5 Pro + Claude Opus 4.6** (1M + 200K context)
3. Enable **Two-Round mode** — Round 2 synthesizes both AI perspectives
4. Set content mode to **"stripped"** — essential with 296 threads
5. Paste each prompt from the plan above into the question field

The full prompts for all 5 passes are in the plan file — each one is crafted to extract maximum insight from your specific agents. After Pass 1, use **"Generate Chart"** for a visual relationship map.


I need to understand the AI agent system in thread_view.php to help the user craft the best analysis prompts. Find:

1. All AI_AGENTS defined — their names, system prompts, and quick prompts
2. The content modes available (stripped/smart/full)
3. How the two-round AI feature works — what happens in round 1 vs round 2
4. Any existing quick prompt buttons and what they contain
5. The available AI providers and models (MODEL_LIMITS)
6. How context builder threads get included in the AI request
7. Any token limits or truncation logic

Focus on /var/www/mssql.danmarcrm.com/dev1/extension2v1/public/thread_view.php

Return the full text of each agent's systemPrompt and quickPrompts so I can see exactly what analysis capabilities exist.

Prompt:
I need to understand the AI agent system in thread_view.php to help the user craft the best analysis prompts. Find:

1. All AI_AGENTS defined — their names, system prompts, and quick prompts
2. The content modes available (stripped/smart/full)
3. How the two-round AI feature works — what happens in round 1 vs round 2
4. Any existing quick prompt buttons and what they contain
5. The available AI providers and models (MODEL_LIMITS)
6. How context builder threads get included in the AI request
7. Any token limits or truncation logic

Focus on /var/www/mssql.danmarcrm.com/dev1/extension2v1/public/thread_view.php

Return the full text of each agent's systemPrompt and quickPrompts so I can see exactly what analysis capabilities exist.


Prompt:
I need to understand the AI agent system in thread_view.php to help the user craft the best analysis prompts. Find:

1. All AI_AGENTS defined — their names, system prompts, and quick prompts
2. The content modes available (stripped/smart/full)
3. How the two-round AI feature works — what happens in round 1 vs round 2
4. Any existing quick prompt buttons and what they contain
5. The available AI providers and models (MODEL_LIMITS)
6. How context builder threads get included in the AI request
7. Any token limits or truncation logic

Focus on /var/www/mssql.danmarcrm.com/dev1/extension2v1/public/thread_view.php

Return the full text of each agent's systemPrompt and quickPrompts so I can see exactly what analysis capabilities exist.


Prompt:
I need to understand the AI agent system in thread_view.php to help the user craft the best analysis prompts. Find:

1. All AI_AGENTS defined — their names, system prompts, and quick prompts
2. The content modes available (stripped/smart/full)
3. How the two-round AI feature works — what happens in round 1 vs round 2
4. Any existing quick prompt buttons and what they contain
5. The available AI providers and models (MODEL_LIMITS)
6. How context builder threads get included in the AI request
7. Any token limits or truncation logic

Focus on /var/www/mssql.danmarcrm.com/dev1/extension2v1/public/thread_view.php

Return the full text of each agent's systemPrompt and quickPrompts so I can see exactly what analysis capabilities exist.


Error: File content (40273 tokens) exceeds maximum allowed tokens (25000). Please use offset and limit parameters to read specific portions of the file, or use the GrepTool to search for specific content.

Prompt:
I need to understand the AI agent system in thread_view.php to help the user craft the best analysis prompts. Find:

1. All AI_AGENTS defined — their names, system prompts, and quick prompts
2. The content modes available (stripped/smart/full)
3. How the two-round AI feature works — what happens in round 1 vs round 2
4. Any existing quick prompt buttons and what they contain
5. The available AI providers and models (MODEL_LIMITS)
6. How context builder threads get included in the AI request
7. Any token limits or truncation logic

Focus on /var/www/mssql.danmarcrm.com/dev1/extension2v1/public/thread_view.php

Return the full text of each agent's systemPrompt and quickPrompts so I can see exactly what analysis capabilities exist.


<tool_use_error>Sibling tool call errored</tool_use_error>

Prompt:
I need to understand the AI agent system in thread_view.php to help the user craft the best analysis prompts. Find:

1. All AI_AGENTS defined — their names, system prompts, and quick prompts
2. The content modes available (stripped/smart/full)
3. How the two-round AI feature works — what happens in round 1 vs round 2
4. Any existing quick prompt buttons and what they contain
5. The available AI providers and models (MODEL_LIMITS)
6. How context builder threads get included in the AI request
7. Any token limits or truncation logic

Focus on /var/www/mssql.danmarcrm.com/dev1/extension2v1/public/thread_view.php

Return the full text of each agent's systemPrompt and quickPrompts so I can see exactly what analysis capabilities exist.


Prompt:
I need to understand the AI agent system in thread_view.php to help the user craft the best analysis prompts. Find:

1. All AI_AGENTS defined — their names, system prompts, and quick prompts
2. The content modes available (stripped/smart/full)
3. How the two-round AI feature works — what happens in round 1 vs round 2
4. Any existing quick prompt buttons and what they contain
5. The available AI providers and models (MODEL_LIMITS)
6. How context builder threads get included in the AI request
7. Any token limits or truncation logic

Focus on /var/www/mssql.danmarcrm.com/dev1/extension2v1/public/thread_view.php

Return the full text of each agent's systemPrompt and quickPrompts so I can see exactly what analysis capabilities exist.


Prompt:
I need to understand the AI agent system in thread_view.php to help the user craft the best analysis prompts. Find:

1. All AI_AGENTS defined — their names, system prompts, and quick prompts
2. The content modes available (stripped/smart/full)
3. How the two-round AI feature works — what happens in round 1 vs round 2
4. Any existing quick prompt buttons and what they contain
5. The available AI providers and models (MODEL_LIMITS)
6. How context builder threads get included in the AI request
7. Any token limits or truncation logic

Focus on /var/www/mssql.danmarcrm.com/dev1/extension2v1/public/thread_view.php

Return the full text of each agent's systemPrompt and quickPrompts so I can see exactly what analysis capabilities exist.


1461-        const primaryContactEmail = '<?= addslashes($primaryContactEmail ?: '') ?>';
1462-
1463-        // ============================================
1464-        // AI AGENTS
1465-        // ============================================
1466:        const AI_AGENTS = {
1467-            general: {
1468-                name: 'General',
1469-                icon: '\uD83D\uDCAC',
1470:                systemPrompt: 'You are a highly capable business intelligence AI assistant. Analyze the provided email conversations thoroughly. Provide clear, actionable insights. Be direct and specific.',
1471:                quickPrompts: [
1472-                    'Summarize the key points',
1473-                    'What are the action items?',
1474-                    'What is the overall sentiment?',
1475-                    'What should I reply?'
1476-                ]
1477-            },
1478-            quote_analyst: {
1479-                name: 'Quotes',
1480-                icon: '\uD83D\uDCB0',
1481:                systemPrompt: 'You are a pricing and quotation analyst. Focus on:\n- All quoted prices, amounts, and financial figures\n- Price changes over time and negotiation patterns\n- Scope of work vs pricing alignment\n- Competitor pricing if mentioned\n- Negotiation leverage points\n- Budget signals and willingness to pay\nAlways present findings with specific numbers and dates.',
1482:                quickPrompts: [
1483-                    'What prices have been quoted?',
1484-                    'Show the negotiation history',
1485-                    'What is the pricing trend?',
1486-                    'What should we quote next?'
1487-                ]
1488-            },
1489-            deception_detector: {
1490-                name: 'Deception',
1491-                icon: '\uD83D\uDD0D',
1492:                systemPrompt: 'You are a deception and inconsistency detection specialist. Analyze the emails for:\n- Contradictions between different messages\n- Promises made but not kept\n- Vague or evasive responses to direct questions\n- Sudden changes in tone or position\n- Pressure tactics or urgency manipulation\n- Information that doesn\'t add up\n- Omissions of relevant details\nRate the overall deception risk as LOW/MEDIUM/HIGH with evidence.',
1493:                quickPrompts: [
1494-                    'Any contradictions detected?',
1495-                    'Rate the deception risk level',
1496-                    'Are there broken promises?',
1497-                    'Any pressure tactics used?'
1498-                ]
1499-            },
1500-            contact_profiler: {
1501-                name: 'Profiler',
1502-                icon: '\uD83D\uDC64',
1503:                systemPrompt: 'You are a contact profiling specialist. Build a psychological and business profile of the contact based on their emails:\n- Communication style (formal/casual, verbose/concise)\n- Decision-making patterns\n- Negotiation style and tendencies\n- Responsiveness and reliability\n- Priorities and motivations\n- Relationship health indicators\n- Loyalty signals\nProvide specific examples from the emails to support your assessment.',
1504:                quickPrompts: [
1505-                    'Build a full profile',
1506-                    'Communication style analysis',
1507-                    'How loyal is this contact?',
1508-                    'What motivates them?'
1509-                ]
1510-            },
1511-            email_composer: {
1512-                name: 'Composer',
1513-                icon: '\u270D\uFE0F',
1514:                systemPrompt: 'You are a professional email composer. Based on the conversation context:\n- Draft a professional, well-structured reply\n- Match the appropriate tone (formal/casual based on the thread)\n- Address all open questions or action items\n- Be concise but thorough\n- Include appropriate greetings and sign-offs\nOutput ONLY the email draft, ready to send.',
1515:                quickPrompts: [
1516-                    'Draft a reply',
1517-                    'Write a follow-up email',
1518-                    'Compose a polite decline',
1519-                    'Draft a meeting request'
1520-                ]
--
1570-
1571-        function estimateCurrentTokens() {
1572-            var content = buildEmailContent();
1573-            var question = (document.getElementById('ai-question').value || '').trim();
1574-            var context = (document.getElementById('ai-context').value || '').trim();
1575:            var agent = AI_AGENTS[selectedAgent];
1576-            var total = content.length;
1577:            if (agent) total += agent.systemPrompt.length;
1578-            if (question) total += question.length;
1579-            if (context) total += context.length;
1580-            // Add conversation history estimate
1581-            total += buildConversationContext().length;
1582-            // Add context threads + checkbox threads estimate
--
1916-        // ============================================
1917-        function initAgentButtons() {
1918-            var container = document.getElementById('agent-buttons');
1919-            if (!container) return;
1920-            var html = '';
1921:            for (var id in AI_AGENTS) {
1922:                var agent = AI_AGENTS[id];
1923-                html += '<button class="agent-btn' + (id === selectedAgent ? ' active' : '') + '" data-agent="' + id + '" onclick="selectAgent(\'' + id + '\')">';
1924-                html += agent.icon + ' ' + agent.name;
1925-                html += '</button>';
1926-            }
1927-            container.innerHTML = html;
--
1939-        }
1940-
1941-        function updateAgentQuickPrompts() {
1942-            var container = document.getElementById('agent-quick-prompts');
1943-            if (!container) return;
1944:            var agent = AI_AGENTS[selectedAgent];
1945:            if (!agent || !agent.quickPrompts || !agent.quickPrompts.length) {
1946-                container.style.display = 'none';
1947-                return;
1948-            }
1949-            container.style.display = 'flex';
1950-            var html = '';
1951:            agent.quickPrompts.forEach(function(prompt) {
1952-                html += '<button class="quick-prompt-btn" onclick="useQuickPrompt(\'' + prompt.replace(/'/g, "\\'") + '\')">' + escapeHtml(prompt) + '</button>';
1953-            });
1954-            container.innerHTML = html;
1955-        }
1956-
--
3037-            document.getElementById('submit-ai-btn').disabled = true;
3038-
3039-            try {
3040-                // Build the full content - start with agent system prompt
3041-                var agentPrompt = '';
3042:                if (selectedAgent && AI_AGENTS[selectedAgent]) {
3043:                    agentPrompt = '=== AI AGENT INSTRUCTIONS ===\n' + AI_AGENTS[selectedAgent].systemPrompt + '\n=== END AGENT INSTRUCTIONS ===\n\n';
3044-                }
3045-
3046-                // Inject contact memories if available
3047-                var memoryBlock = '';
3048-                if (contactMemories.length > 0 && primaryContactName) {

Prompt:
I need to understand the AI agent system in thread_view.php to help the user craft the best analysis prompts. Find:

1. All AI_AGENTS defined — their names, system prompts, and quick prompts
2. The content modes available (stripped/smart/full)
3. How the two-round AI feature works — what happens in round 1 vs round 2
4. Any existing quick prompt buttons and what they contain
5. The available AI providers and models (MODEL_LIMITS)
6. How context builder threads get included in the AI request
7. Any token limits or truncation logic

Focus on /var/www/mssql.danmarcrm.com/dev1/extension2v1/public/thread_view.php

Return the full text of each agent's systemPrompt and quickPrompts so I can see exactly what analysis capabilities exist.


Prompt:
I need to understand the AI agent system in thread_view.php to help the user craft the best analysis prompts. Find:

1. All AI_AGENTS defined — their names, system prompts, and quick prompts
2. The content modes available (stripped/smart/full)
3. How the two-round AI feature works — what happens in round 1 vs round 2
4. Any existing quick prompt buttons and what they contain
5. The available AI providers and models (MODEL_LIMITS)
6. How context builder threads get included in the AI request
7. Any token limits or truncation logic

Focus on /var/www/mssql.danmarcrm.com/dev1/extension2v1/public/thread_view.php

Return the full text of each agent's systemPrompt and quickPrompts so I can see exactly what analysis capabilities exist.


1524-        let selectedAgent = 'general';
1525-
1526-        // ============================================
1527-        // TOKEN ESTIMATION
1528-        // ============================================
1529:        const MODEL_LIMITS = {
1530-            'claude-opus-4-6': 200000,
1531-            'claude-sonnet-4-6': 200000,
1532-            'claude-sonnet-4-5-20250929': 200000,
1533-            'claude-3-5-sonnet-20241022': 200000,
1534-            'claude-opus-4-5-20250929': 200000,
--
1560-            var selectedProviders = getSelectedProviders();
1561-            var minLimit = Infinity;
1562-            selectedProviders.forEach(function(prov) {
1563-                var modelSelect = document.getElementById('model_' + prov);
1564-                var model = modelSelect ? modelSelect.value : '';
1565:                var limit = MODEL_LIMITS[model] || 128000;
1566-                if (limit < minLimit) minLimit = limit;
1567-            });
1568-            return minLimit === Infinity ? 128000 : minLimit;
1569-        }
1570-

Prompt:
I need to understand the AI agent system in thread_view.php to help the user craft the best analysis prompts. Find:

1. All AI_AGENTS defined — their names, system prompts, and quick prompts
2. The content modes available (stripped/smart/full)
3. How the two-round AI feature works — what happens in round 1 vs round 2
4. Any existing quick prompt buttons and what they contain
5. The available AI providers and models (MODEL_LIMITS)
6. How context builder threads get included in the AI request
7. Any token limits or truncation logic

Focus on /var/www/mssql.danmarcrm.com/dev1/extension2v1/public/thread_view.php

Return the full text of each agent's systemPrompt and quickPrompts so I can see exactly what analysis capabilities exist.


1090-            margin-left: 6px;
1091-        }
1092-
1093:        .round-badge.round-1 {
1094-            background: #dbeafe;
1095-            color: #1e40af;
1096-        }
1097-
1098:        .round-badge.round-2 {
1099-            background: #d1fae5;
1100-            color: #065f46;
1101-        }
--
1168-
1169-

1170-

1171:
AI Response Round 1

1172-                        <button class="btn btn-secondary" onclick="closeResponse()">Close</button>
1173-

1174-

--
1184-

1185-
1186-
1187:

1188-

1189:
AI Response Round 2 - Summary

1190:                        <button class="btn btn-secondary" onclick="document.getElementById('ai-response-round2-container').style.display='none'">Close</button>
1191-

1192:

1193:

1194-

1195-
1196-
--
1380-
1381-

1382-
1383:                            <input type="checkbox" id="two-round-mode">
1384-

1385-                                Auto Two-Round AI
1386-
Round 1: all providers answer independently. Round 2: all providers receive everyone's Round 1 answers and produce a unified summary.

--
1984-        var lastFullContent = '';
1985-
1986-        // Send Round 2 with all Round 1 responses as context
1987:        function sendRound2(round1Responses, providersList, modelsMap, originalQuestion) {
1988-            var successfulResponses = [];
1989:            for (var prov in round1Responses) {
1990:                var r = round1Responses[prov];
1991-                if (r && r.success && r.response) {
1992-                    successfulResponses.push({
1993-                        provider: prov,
--
2003-            }
2004-
2005-            // Build Round 2 prompt
2006:            var round2Content = '=== ROUND 2: UNIFIED ANALYSIS ===\n';
2007:            round2Content += 'You previously analyzed an email conversation. Below are the analyses from ' + successfulResponses.length + ' different AI models.\n';
2008:            round2Content += 'Your task: Synthesize ALL analyses into a single comprehensive summary. Highlight where models AGREE and where they DISAGREE. Provide a final unified assessment.\n\n';
2009:            round2Content += 'Original question: ' + originalQuestion + '\n\n';
2010-
2011-            successfulResponses.forEach(function(sr, idx) {
2012:                round2Content += '--- ANALYSIS FROM ' + sr.provider.toUpperCase() + ' (' + sr.model + ') ---\n';
2013-                // Cap each response to 4000 chars to stay within limits
2014-                var respText = sr.response;
2015-                if (respText.length > 4000) {
2016-                    respText = respText.substring(0, 4000) + '\n...(truncated)';
2017-                }
2018:                round2Content += respText + '\n\n';
2019-            });
2020-
2021:            round2Content += '=== END OF ROUND 1 ANALYSES ===\n\n';
2022:            round2Content += 'Please provide a unified, comprehensive summary that synthesizes all the above analyses. Note agreements, disagreements, and your overall assessment.';
2023-
2024-            // Check token budget
2025:            var r2Tokens = Math.ceil(round2Content.length / 3.5);
2026-            var modelLimit = getSmallestModelLimit();
2027-            var maxChars = Math.floor(modelLimit * 3.5 * 0.85);
2028:            if (round2Content.length > maxChars) {
2029:                round2Content = round2Content.substring(0, maxChars) + '\n\n...(Content truncated to fit model limit)';
2030:                r2Tokens = Math.ceil(round2Content.length / 3.5);
2031-            }
2032-
2033:            console.log('Round 2: ' + round2Content.length + ' chars, ~' + r2Tokens + ' tokens, ' + providersList.length + ' providers');
2034-
2035-            document.getElementById('loading-overlay').classList.add('active');
2036-            document.getElementById('loading-message').textContent = 'Round 2: Synthesizing with ' + providersList.length + ' AI(s)...';
2037-
2038-            var requestBody = {
2039:                user_content: round2Content,
2040-                providers: providersList
2041-            };
2042-            for (var p in modelsMap) {
--
2046-            var timeoutSec = parseInt(document.getElementById('provider-timeout').value) || 120;
2047-
2048-            logAiDebug({
2049:                type: 'request_round2',
2050-                thread_id: currentThreadId,
2051-                agent: selectedAgent,
2052-                providers: providersList,
2053:                content_length: round2Content.length,
2054-                estimated_tokens: r2Tokens,
2055-                question: 'Round 2 synthesis of ' + successfulResponses.length + ' responses'
2056-            });
--
2080-                                contact_id: primaryContactId,
2081-                                question: 'Round 2 Synthesis: ' + originalQuestion,
2082-                                agent: selectedAgent,
2083:                                content_mode: 'round2',
2084:                                content_length: round2Content.length,
2085-                                estimated_tokens: r2Tokens,
2086-                                provider: prov,
2087-                                model: modelsMap[prov],
--
2095-                    });
2096-
2097-                    logAiDebug({
2098:                        type: 'response_round2',
2099-                        thread_id: currentThreadId,
2100-                        providers: providersList,
2101-                        question: 'Round 2 synthesis'
--
2116-
2117-        // Display Round 2 responses in separate container
2118-        function displayRound2Response(data) {
2119:            var container = document.getElementById('ai-response-round2-container');
2120:            var tabsContainer = document.getElementById('response-tabs-round2');
2121:            var contentContainer = document.getElementById('ai-responses-content-round2');
2122-
2123-            container.style.display = 'block';
2124-            tabsContainer.innerHTML = '';
--
2146-                if (response.success) {
2147-                    content.innerHTML = '
' +
2148-                        '
' +
2149:                        '' + escapeHtml(providerConfig.name) + ' Round 2' +
2150-                        'Model: ' + escapeHtml(response.model || '?') + ' | Time: ' + (response.request_time ? response.request_time.toFixed(2) : '?') + 's | Cost: $' + (response.cost ? response.cost.toFixed(6) : '0') + '' +
2151-                        '
' +
2152-                        '
' + escapeHtml(response.response) + '
' +
--
2165-        }
2166-
2167-        function switchRound2Tab(index) {
2168:            var tabs = document.getElementById('response-tabs-round2');
2169:            var content = document.getElementById('ai-responses-content-round2');
2170-            if (!tabs || !content) return;
2171-            tabs.querySelectorAll('.response-tab').forEach(function(t) {
2172-                t.classList.toggle('active', parseInt(t.dataset.index) === index);
--
2351-        // ============================================
2352-        async function generateChart() {
2353-            // Collect AI response text from the displayed response
2354:            var responseEls = document.querySelectorAll('#ai-responses-content .ai-response-content, #ai-responses-content-round2 .ai-response-content');
2355-            var responseText = '';
2356-            responseEls.forEach(function(el) {
2357-                if (el.innerText) responseText += el.innerText + '\n\n';
--
3273-                    displayAIResponse(data, 'Round 1');
3274-
3275-                    // Check if Two-Round mode is enabled → auto-trigger Round 2
3276:                    var twoRoundCheckbox = document.getElementById('two-round-mode');
3277-                    if (twoRoundCheckbox && twoRoundCheckbox.checked) {
3278-                        // Count successful responses
3279-                        var r1SuccessCount = 0;
--
3326-            // Update badge
3327-            if (badge) {
3328-                badge.textContent = roundLabel || 'Round 1';
3329:                badge.className = 'round-badge round-1';
3330-            }
3331-
3332-            // Hide Round 2 container (fresh start)
3333:            document.getElementById('ai-response-round2-container').style.display = 'none';
3334-
3335-            // Store responses for follow-up
3336-            lastRound1Responses = {};

Prompt:
I need to understand the AI agent system in thread_view.php to help the user craft the best analysis prompts. Find:

1. All AI_AGENTS defined — their names, system prompts, and quick prompts
2. The content modes available (stripped/smart/full)
3. How the two-round AI feature works — what happens in round 1 vs round 2
4. Any existing quick prompt buttons and what they contain
5. The available AI providers and models (MODEL_LIMITS)
6. How context builder threads get included in the AI request
7. Any token limits or truncation logic

Focus on /var/www/mssql.danmarcrm.com/dev1/extension2v1/public/thread_view.php

Return the full text of each agent's systemPrompt and quickPrompts so I can see exactly what analysis capabilities exist.


Prompt:
I need to understand the AI agent system in thread_view.php to help the user craft the best analysis prompts. Find:

1. All AI_AGENTS defined — their names, system prompts, and quick prompts
2. The content modes available (stripped/smart/full)
3. How the two-round AI feature works — what happens in round 1 vs round 2
4. Any existing quick prompt buttons and what they contain
5. The available AI providers and models (MODEL_LIMITS)
6. How context builder threads get included in the AI request
7. Any token limits or truncation logic

Focus on /var/www/mssql.danmarcrm.com/dev1/extension2v1/public/thread_view.php

Return the full text of each agent's systemPrompt and quickPrompts so I can see exactly what analysis capabilities exist.


1352-
1353-
1354-

1355-                        Email Content Processing
1356-                        <select id="content-mode" style="width: 100%;" onchange="updateTokenEstimate()">
1357:                            <option value="full">Full Content (include all quoted replies)</option>
1358:                            <option value="stripped" selected>Stripped (remove quoted replies - saves tokens)</option>
1359:                            <option value="smart">Smart Deduplication (quotes only in last email)</option>
1360-                        </select>
1361-

1362-
1363-
1364-

--
1500-            contact_profiler: {
1501-                name: 'Profiler',
1502-                icon: '\uD83D\uDC64',
1503-                systemPrompt: 'You are a contact profiling specialist. Build a psychological and business profile of the contact based on their emails:\n- Communication style (formal/casual, verbose/concise)\n- Decision-making patterns\n- Negotiation style and tendencies\n- Responsiveness and reliability\n- Priorities and motivations\n- Relationship health indicators\n- Loyalty signals\nProvide specific examples from the emails to support your assessment.',
1504-                quickPrompts: [
1505:                    'Build a full profile',
1506-                    'Communication style analysis',
1507-                    'How loyal is this contact?',
1508-                    'What motivates them?'
1509-                ]
1510-            },
--
1567-            });
1568-            return minLimit === Infinity ? 128000 : minLimit;
1569-        }
1570-
1571-        function estimateCurrentTokens() {
1572:            var content = buildEmailContent();
1573-            var question = (document.getElementById('ai-question').value || '').trim();
1574-            var context = (document.getElementById('ai-context').value || '').trim();
1575-            var agent = AI_AGENTS[selectedAgent];
1576-            var total = content.length;
1577-            if (agent) total += agent.systemPrompt.length;
1578-            if (question) total += question.length;
1579-            if (context) total += context.length;
1580-            // Add conversation history estimate
1581-            total += buildConversationContext().length;
1582-            // Add context threads + checkbox threads estimate
1583:            // Use emailCount * 500 chars per stripped email (realistic average)
1584-            var checkboxThreads = getSelectedThreads();
1585-            contextThreads.forEach(function(ct) { total += ct.emailCount * 500; });
1586-            total += checkboxThreads.length * 5000; // rough estimate for checkbox threads
1587-            return Math.ceil(total / 3.5);
1588-        }
--
2923-        }
2924-
2925-        /**
2926-         * Build email content string for AI
2927-         * Supports different processing modes:
2928:         * - full: Include all content as-is
2929:         * - stripped: Remove quoted replies from all emails
2930:         * - smart: Keep quotes only in the last email
2931-         */
2932:        function buildEmailContent() {
2933-            const mode = document.getElementById('content-mode').value;
2934-            let content = 'EMAIL THREAD CONVERSATION:\n';
2935-            content += `(Content mode: ${mode})\n\n`;
2936-
2937-            const totalEmails = emailContent.length;
--
2942-                content += `Date: ${email.date}\n`;
2943-                content += `Direction: ${email.direction}\n\n`;
2944-
2945-                let emailBody = email.body;
2946-
2947:                if (mode === 'stripped') {
2948-                    // Strip quoted content from all emails
2949-                    emailBody = stripQuotedContent(emailBody);
2950-                    if (!emailBody.trim()) {
2951-                        emailBody = '(Only quoted content - no new text in this email)';
2952-                    }
2953:                } else if (mode === 'smart') {
2954-                    // Strip quotes from all emails except the last one
2955-                    if (index < totalEmails - 1) {
2956-                        emailBody = stripQuotedContent(emailBody);
2957-                        if (!emailBody.trim()) {
2958-                            emailBody = '(Only quoted content - no new text in this email)';
2959-                        }
2960-                    }
2961:                    // Last email keeps full content for context
2962-                }
2963:                // mode === 'full' keeps everything as-is
2964-
2965-                content += emailBody + '\n\n';
2966-            });
2967-
2968-            return content;
--
2979-                const senderName = email.from_name || email.from_email || 'Unknown';
2980-                content += `Email ${idx + 1} from ${senderName}:\n`;
2981-
2982-                let emailBody = email.body_text || email.body_html || '';
2983-
2984:                if (mode === 'stripped') {
2985-                    emailBody = stripQuotedContent(emailBody);
2986-                    if (!emailBody.trim()) {
2987-                        emailBody = '(Only quoted content)';
2988-                    }
2989:                } else if (mode === 'smart' && idx < totalEmails - 1) {
2990-                    emailBody = stripQuotedContent(emailBody);
2991-                    if (!emailBody.trim()) {
2992-                        emailBody = '(Only quoted content)';
2993-                    }
2994-                }
--
3035-            // Show loading
3036-            document.getElementById('loading-overlay').classList.add('active');
3037-            document.getElementById('submit-ai-btn').disabled = true;
3038-
3039-            try {
3040:                // Build the full content - start with agent system prompt
3041-                var agentPrompt = '';
3042-                if (selectedAgent && AI_AGENTS[selectedAgent]) {
3043-                    agentPrompt = '=== AI AGENT INSTRUCTIONS ===\n' + AI_AGENTS[selectedAgent].systemPrompt + '\n=== END AGENT INSTRUCTIONS ===\n\n';
3044-                }
3045-
--
3051-                        memoryBlock += '- ' + (m.fact || m) + '\n';
3052-                    });
3053-                    memoryBlock += '=== END KNOWN FACTS ===\n\n';
3054-                }
3055-
3056:                var fullContent = agentPrompt + memoryBlock + buildEmailContent();
3057-
3058-                // ADD CONVERSATION HISTORY FOR CONTEXT (ChatGPT-like memory)
3059-                var conversationContext = buildConversationContext();
3060-                if (conversationContext) {
3061:                    fullContent += conversationContext;
3062-                    console.log('Including conversation history:', conversationHistory.length, 'previous exchanges');
3063-                }
3064-
3065-                // Add question if provided
3066-                var actualQuestion = question || 'Please analyze this email conversation and provide insights on:\n1. Key points and summary\n2. Sentiment and tone analysis\n3. Any potential concerns or red flags\n4. Recommended next steps';
3067-
3068-                if (question) {
3069:                    fullContent += '\n\nCURRENT USER QUESTION:\n' + question;
3070-                } else {
3071:                    fullContent += '\n\n' + actualQuestion;
3072-                }
3073-
3074-                // Add context if provided
3075-                if (context) {
3076:                    fullContent += '\n\nADDITIONAL CONTEXT:\n' + context;
3077-                }
3078-
3079-                // Fetch additional thread content (checkboxes + context builder threads)
3080:                // Uses thread_for_ai endpoint (lightweight, stripped bodies, no analysis)
3081-                if (allAdditionalThreads.length > 0) {
3082-                    document.getElementById('loading-message').textContent = 'Loading additional threads (0/' + allAdditionalThreads.length + ')...';
3083-
3084-                    for (var i = 0; i < allAdditionalThreads.length; i++) {
3085-                        var tid = allAdditionalThreads[i];
3086-                        document.getElementById('loading-message').textContent = 'Loading thread ' + (i + 1) + '/' + allAdditionalThreads.length + '...';
3087-                        try {
3088-                            var threadResp = await fetch('api.php?action=thread_for_ai&thread_id=' + tid);
3089-                            if (!threadResp.ok) {
3090-                                console.warn('Failed to fetch thread ' + tid + ': HTTP ' + threadResp.status);
3091:                                fullContent += '\n\n--- THREAD ' + (i + 1) + ': FAILED TO LOAD (HTTP ' + threadResp.status + ') ---\n';
3092-                                continue;
3093-                            }
3094-                            var thread = await threadResp.json();
3095-
3096-                            if (thread.emails && thread.emails.length > 0) {
3097:                                fullContent += '\n\n--- ADDITIONAL THREAD ' + (i + 1) + ' OF ' + allAdditionalThreads.length + ' ---\n';
3098:                                fullContent += 'Subject: ' + (thread.emails[0].subject || '(No Subject)') + '\n\n';
3099:                                // Bodies already stripped by thread_for_ai endpoint
3100:                                fullContent += processAdditionalThreadContent(thread.emails, 'full');
3101-                            }
3102-                        } catch (fetchErr) {
3103-                            console.warn('Error fetching thread ' + tid + ':', fetchErr);
3104:                            fullContent += '\n\n--- THREAD ' + (i + 1) + ': FETCH ERROR ---\n';
3105-                        }
3106-                    }
3107-                }
3108-
3109-                / Enforce content budget: truncate to fit smallest model limit
3110-                var maxChars = Math.floor(modelLimit * 3.5 * 0.85); // 85% of model limit in chars
3111-                var wasTruncated = false;
3112:                if (fullContent.length > maxChars) {
3113-                    wasTruncated = true;
3114:                    fullContent = fullContent.substring(0, maxChars) + '\n\n...(Content truncated to fit model context limit of ' + modelLimit.toLocaleString() + ' tokens)';
3115-                }
3116-
3117:                var finalTokens = Math.ceil(fullContent.length / 3.5);
3118-
3119-                // Log to debug file BEFORE sending
3120-                logAiDebug({
3121-                    type: 'request',
3122-                    thread_id: currentThreadId,
3123-                    agent: selectedAgent,
3124-                    providers: selectedProvidersList,
3125-                    models: modelsMap,
3126:                    content_length: fullContent.length,
3127-                    estimated_tokens: finalTokens,
3128-                    question: actualQuestion,
3129-                    context_threads: allAdditionalThreads,
3130-                    truncated: wasTruncated,
3131:                    full_content: fullContent.substring(0, 10000) + (fullContent.length > 10000 ? '\n...(truncated in log, full was ' + fullContent.length + ' chars)' : '')
3132-                });
3133-
3134-                // Console log
3135-                console.log('=== AI REQUEST LOG ===');
3136-                console.log('Agent:', selectedAgent);
3137-                console.log('Providers:', selectedProvidersList);
3138-                console.log('Models:', modelsMap);
3139-                console.log('Content Mode:', contentMode);
3140-                console.log('Context Threads:', allAdditionalThreads.length);
3141-                console.log('Conversation History:', conversationHistory.length);
3142:                console.log('Content Length:', fullContent.length, 'chars');
3143-                console.log('Estimated Tokens:', finalTokens);
3144-                console.log('======================');
3145-
3146-                document.getElementById('loading-message').textContent = 'Analyzing with ' + selectedProvidersList.length + ' AI provider(s)...';
3147-
3148-                // Build request body with all selected providers and their models
3149-                var requestBody = {
3150:                    user_content: fullContent,
3151-                    providers: selectedProvidersList
3152-                };
3153-
3154-                // Add model for each provider
3155-                for (var provider in modelsMap) {
--
3232-                                contact_id: primaryContactId,
3233-                                question: actualQuestion,
3234-                                context: context,
3235-                                agent: selectedAgent,
3236-                                content_mode: contentMode,
3237:                                content_sent: fullContent,
3238:                                content_length: fullContent.length,
3239-                                estimated_tokens: finalTokens,
3240-                                provider: prov,
3241-                                model: modelsMap[prov],
3242-                                query_id: data.query_id,
3243-                                response: providerResponse,
--
3265-
3266-                    // Store Round 1 info for potential Round 2 / follow-ups
3267-                    lastRound1Providers = selectedProvidersList.slice();
3268-                    lastRound1Models = Object.assign({}, modelsMap);
3269-                    lastRound1Question = actualQuestion;
3270:                    lastFullContent = fullContent;
3271-
3272-                    // Display Round 1 response
3273-                    displayAIResponse(data, 'Round 1');
3274-
3275-                    // Check if Two-Round mode is enabled → auto-trigger Round 2

Prompt:
I need to understand the AI agent system in thread_view.php to help the user craft the best analysis prompts. Find:

1. All AI_AGENTS defined — their names, system prompts, and quick prompts
2. The content modes available (stripped/smart/full)
3. How the two-round AI feature works — what happens in round 1 vs round 2
4. Any existing quick prompt buttons and what they contain
5. The available AI providers and models (MODEL_LIMITS)
6. How context builder threads get included in the AI request
7. Any token limits or truncation logic

Focus on /var/www/mssql.danmarcrm.com/dev1/extension2v1/public/thread_view.php

Return the full text of each agent's systemPrompt and quickPrompts so I can see exactly what analysis capabilities exist.


Prompt:
I need to understand the AI agent system in thread_view.php to help the user craft the best analysis prompts. Find:

1. All AI_AGENTS defined — their names, system prompts, and quick prompts
2. The content modes available (stripped/smart/full)
3. How the two-round AI feature works — what happens in round 1 vs round 2
4. Any existing quick prompt buttons and what they contain
5. The available AI providers and models (MODEL_LIMITS)
6. How context builder threads get included in the AI request
7. Any token limits or truncation logic

Focus on /var/www/mssql.danmarcrm.com/dev1/extension2v1/public/thread_view.php

Return the full text of each agent's systemPrompt and quickPrompts so I can see exactly what analysis capabilities exist.


3- * Thread View Page
4- *
5- * Dedicated page for viewing email threads and sending to AI API.
6- * Features:
7- * - View thread emails with actual sender names
8: * - Select additional threads from the same contact
9- * - Send to AI API with provider/model selection
10- * - Add custom questions and context
11- * - Save and load AI responses
12- *
13- * @package EmailIntelligence
--
1289-

1290-

1291-
Include Other Threads

1292-

1293-

1294:                        Select additional threads from <?= htmlspecialchars($primaryContactName ?: 'this contact') ?> to include in the AI analysis:
1295-

1296-

1297-                        <?php foreach (array_slice($otherThreads, 0, 10) as $thread): ?>
1298-
1299:                            <input type="checkbox" name="additional_threads[]" value="<?= htmlspecialchars($thread['thread_id']) ?>">
1300-

1301-
<?= htmlspecialchars($thread['subject'] ?? '(No Subject)') ?>

1302-

1303-                                    <?= $thread['email_count'] ?? 0 ?> emails |
1304-                                    <?= date('M j, Y', strtotime($thread['last_date'] ?? 'now')) ?>
--
1870-                el.innerHTML = '';
1871-                return;
1872-            }
1873-            var totalEmails = contextThreads.reduce(function(sum, ct) { return sum + ct.emailCount; }, 0);
1874-            var html = '
';
1875:            html += '
' + contextThreads.length + ' additional thread(s), ~' + totalEmails + ' emails
';
1876-            contextThreads.forEach(function(ct) {
1877-                html += '
';
1878-                html += '' + escapeHtml(ct.subject) + ' (' + ct.emailCount + ')';
1879-                html += '<button class="context-cart-remove" onclick="removeThreadFromContext(\'' + ct.threadId + '\')">×</button>';
1880-                html += '
';
--
2845-        function getSelectedProviders() {
2846-            const checkboxes = document.querySelectorAll('input[name="providers[]"]:checked');
2847-            return Array.from(checkboxes).map(cb => cb.value);
2848-        }
2849-
2850:        // Get selected additional threads
2851-        function getSelectedThreads() {
2852:            const checkboxes = document.querySelectorAll('input[name="additional_threads[]"]:checked');
2853-            return Array.from(checkboxes).map(cb => cb.value);
2854-        }
2855-
2856-        /**
2857-         * Strip quoted content from email body
--
2967-
2968-            return content;
2969-        }
2970-
2971-        /**
2972:         * Process additional thread content with the selected mode
2973-         */
2974-        function processAdditionalThreadContent(emails, mode) {
2975-            let content = '';
2976-            const totalEmails = emails.length;
2977-
--
3074-                // Add context if provided
3075-                if (context) {
3076-                    fullContent += '\n\nADDITIONAL CONTEXT:\n' + context;
3077-                }
3078-
3079:                // Fetch additional thread content (checkboxes + context builder threads)
3080-                // Uses thread_for_ai endpoint (lightweight, stripped bodies, no analysis)
3081-                if (allAdditionalThreads.length > 0) {
3082:                    document.getElementById('loading-message').textContent = 'Loading additional threads (0/' + allAdditionalThreads.length + ')...';
3083-
3084-                    for (var i = 0; i < allAdditionalThreads.length; i++) {
3085-                        var tid = allAdditionalThreads[i];
3086-                        document.getElementById('loading-message').textContent = 'Loading thread ' + (i + 1) + '/' + allAdditionalThreads.length + '...';
3087-                        try {
--
3124-                    providers: selectedProvidersList,
3125-                    models: modelsMap,
3126-                    content_length: fullContent.length,
3127-                    estimated_tokens: finalTokens,
3128-                    question: actualQuestion,
3129:                    context_threads: allAdditionalThreads,
3130-                    truncated: wasTruncated,
3131-                    full_content: fullContent.substring(0, 10000) + (fullContent.length > 10000 ? '\n...(truncated in log, full was ' + fullContent.length + ' chars)' : '')
3132-                });
3133-
3134-                // Console log

Prompt:
I need to understand the AI agent system in thread_view.php to help the user craft the best analysis prompts. Find:

1. All AI_AGENTS defined — their names, system prompts, and quick prompts
2. The content modes available (stripped/smart/full)
3. How the two-round AI feature works — what happens in round 1 vs round 2
4. Any existing quick prompt buttons and what they contain
5. The available AI providers and models (MODEL_LIMITS)
6. How context builder threads get included in the AI request
7. Any token limits or truncation logic

Focus on /var/www/mssql.danmarcrm.com/dev1/extension2v1/public/thread_view.php

Return the full text of each agent's systemPrompt and quickPrompts so I can see exactly what analysis capabilities exist.


Prompt:
I need to understand the AI agent system in thread_view.php to help the user craft the best analysis prompts. Find:

1. All AI_AGENTS defined — their names, system prompts, and quick prompts
2. The content modes available (stripped/smart/full)
3. How the two-round AI feature works — what happens in round 1 vs round 2
4. Any existing quick prompt buttons and what they contain
5. The available AI providers and models (MODEL_LIMITS)
6. How context builder threads get included in the AI request
7. Any token limits or truncation logic

Focus on /var/www/mssql.danmarcrm.com/dev1/extension2v1/public/thread_view.php

Return the full text of each agent's systemPrompt and quickPrompts so I can see exactly what analysis capabilities exist.


1576-            var total = content.length;
1577-            if (agent) total += agent.systemPrompt.length;
1578-            if (question) total += question.length;
1579-            if (context) total += context.length;
1580-            // Add conversation history estimate
1581:            total += buildConversationContext().length;
1582-            // Add context threads + checkbox threads estimate
1583-            // Use emailCount * 500 chars per stripped email (realistic average)
1584-            var checkboxThreads = getSelectedThreads();
1585-            contextThreads.forEach(function(ct) { total += ct.emailCount * 500; });
1586-            total += checkboxThreads.length * 5000; // rough estimate for checkbox threads
--
1885-
1886-        // ============================================
1887-        // CONTACT MEMORY
1888-        // ============================================
1889-        function loadContactMemories(contactId) {
1890:            fetch('api.php?action=ai_contact_memories&contact_id=' + contactId)
1891-                .then(function(r) { return r.json(); })
1892-                .then(function(data) {
1893-                    contactMemories = data.memories || [];
1894-                    renderContactMemories();
1895-                })
--
2627-                updateConversationDisplay();
2628-            }
2629-        }
2630-
2631-        // Build conversation context string for AI
2632:        function buildConversationContext() {
2633-            if (conversationHistory.length === 0) return '';
2634-
2635-            let context = '\n\n=== PREVIOUS CONVERSATION HISTORY ===\n';
2636-            context += '(The user has asked previous questions about this email thread. Use this context for continuity.)\n\n';
2637-
--
3054-                }
3055-
3056-                var fullContent = agentPrompt + memoryBlock + buildEmailContent();
3057-
3058-                // ADD CONVERSATION HISTORY FOR CONTEXT (ChatGPT-like memory)
3059:                var conversationContext = buildConversationContext();
3060-                if (conversationContext) {
3061-                    fullContent += conversationContext;
3062-                    console.log('Including conversation history:', conversationHistory.length, 'previous exchanges');
3063-                }
3064-

Prompt:
I need to understand the AI agent system in thread_view.php to help the user craft the best analysis prompts. Find:

1. All AI_AGENTS defined — their names, system prompts, and quick prompts
2. The content modes available (stripped/smart/full)
3. How the two-round AI feature works — what happens in round 1 vs round 2
4. Any existing quick prompt buttons and what they contain
5. The available AI providers and models (MODEL_LIMITS)
6. How context builder threads get included in the AI request
7. Any token limits or truncation logic

Focus on /var/www/mssql.danmarcrm.com/dev1/extension2v1/public/thread_view.php

Return the full text of each agent's systemPrompt and quickPrompts so I can see exactly what analysis capabilities exist.


Prompt:
I need to understand the AI agent system in thread_view.php to help the user craft the best analysis prompts. Find:

1. All AI_AGENTS defined — their names, system prompts, and quick prompts
2. The content modes available (stripped/smart/full)
3. How the two-round AI feature works — what happens in round 1 vs round 2
4. Any existing quick prompt buttons and what they contain
5. The available AI providers and models (MODEL_LIMITS)
6. How context builder threads get included in the AI request
7. Any token limits or truncation logic

Focus on /var/www/mssql.danmarcrm.com/dev1/extension2v1/public/thread_view.php

Return the full text of each agent's systemPrompt and quickPrompts so I can see exactly what analysis capabilities exist.


Prompt:
I need to understand the AI agent system in thread_view.php to help the user craft the best analysis prompts. Find:

1. All AI_AGENTS defined — their names, system prompts, and quick prompts
2. The content modes available (stripped/smart/full)
3. How the two-round AI feature works — what happens in round 1 vs round 2
4. Any existing quick prompt buttons and what they contain
5. The available AI providers and models (MODEL_LIMITS)
6. How context builder threads get included in the AI request
7. Any token limits or truncation logic

Focus on /var/www/mssql.danmarcrm.com/dev1/extension2v1/public/thread_view.php

Return the full text of each agent's systemPrompt and quickPrompts so I can see exactly what analysis capabilities exist.


  1529→        const MODEL_LIMITS = {
  1530→            'claude-opus-4-6': 200000,
  1531→            'claude-sonnet-4-6': 200000,
  1532→            'claude-sonnet-4-5-20250929': 200000,
  1533→            'claude-3-5-sonnet-20241022': 200000,
  1534→            'claude-opus-4-5-20250929': 200000,
  1535→            'claude-3-opus-20240229': 200000,
  1536→            'claude-3-5-haiku-20241022': 200000,
  1537→            'gpt-4o': 128000,
  1538→            'gpt-4o-mini': 128000,
  1539→            'gpt-4-turbo': 128000,
  1540→            'gpt-4.1': 1000000,
  1541→            'gpt-4.1-mini': 1000000,
  1542→            'o3': 200000,
  1543→            'o3-mini': 200000,
  1544→            'o4-mini': 200000,
  1545→            'gemini-2.0-flash': 1048576,
  1546→            'gemini-2.5-pro-preview-05-06': 1048576,
  1547→            'gemini-2.5-flash-preview-04-17': 1048576,
  1548→            'grok-3': 131072,
  1549→            'grok-3-mini': 131072,
  1550→            'deepseek-chat': 64000,
  1551→            'deepseek-reasoner': 64000,
  1552→            'qwen-max': 32000,
  1553→            'qwen-plus': 131000,
  1554→            'mistral-large-latest': 128000,
  1555→            'command-a-03-2025': 256000,
  1556→            'command-r-plus': 128000
  1557→        };
  1558→
  1559→        function getSmallestModelLimit() {
  1560→            var selectedProviders = getSelectedProviders();
  1561→            var minLimit = Infinity;
  1562→            selectedProviders.forEach(function(prov) {
  1563→                var modelSelect = document.getElementById('model_' + prov);
  1564→                var model = modelSelect ? modelSelect.value : '';
  1565→                var limit = MODEL_LIMITS[model] || 128000;
  1566→                if (limit < minLimit) minLimit = limit;
  1567→            });
  1568→            return minLimit === Infinity ? 128000 : minLimit;
  1569→        }
  1570→
  1571→        function estimateCurrentTokens() {
  1572→            var content = buildEmailContent();
  1573→            var question = (document.getElementById('ai-question').value || '').trim();
  1574→            var context = (document.getElementById('ai-context').value || '').trim();
  1575→            var agent = AI_AGENTS[selectedAgent];
  1576→            var total = content.length;
  1577→            if (agent) total += agent.systemPrompt.length;
  1578→            if (question) total += question.length;
  1579→            if (context) total += context.length;
  1580→            // Add conversation history estimate
  1581→            total += buildConversationContext().length;
  1582→            // Add context threads + checkbox threads estimate
  1583→            // Use emailCount * 500 chars per stripped email (realistic average)
  1584→            var checkboxThreads = getSelectedThreads();
  1585→            contextThreads.forEach(function(ct) { total += ct.emailCount * 500; });
  1586→            total += checkboxThreads.length * 5000; // rough estimate for checkbox threads
  1587→            return Math.ceil(total / 3.5);
  1588→        }

Prompt:
I need to understand the AI agent system in thread_view.php to help the user craft the best analysis prompts. Find:

1. All AI_AGENTS defined — their names, system prompts, and quick prompts
2. The content modes available (stripped/smart/full)
3. How the two-round AI feature works — what happens in round 1 vs round 2
4. Any existing quick prompt buttons and what they contain
5. The available AI providers and models (MODEL_LIMITS)
6. How context builder threads get included in the AI request
7. Any token limits or truncation logic

Focus on /var/www/mssql.danmarcrm.com/dev1/extension2v1/public/thread_view.php

Return the full text of each agent's systemPrompt and quickPrompts so I can see exactly what analysis capabilities exist.


409-            margin-right: 8px;
410-        }
411-
412:        .provider-option .provider-name {
413-            font-weight: 500;
414-            font-size: 0.9em;
415-        }
--
2055-                question: 'Round 2 synthesis of ' + successfulResponses.length + ' responses'
2056-            });
2057-
2058:            fetchWithTimeout('../aiapi/api_opus/api/query.php', {
2059-                method: 'POST',
2060-                headers: { 'Content-Type': 'application/json' },
2061-                body: JSON.stringify(requestBody)
--
2128-            for (var key in data.responses) {
2129-                var response = data.responses[key];
2130-                var providerName = key.replace('_response', '');
2131:                var providerConfig = providers[providerName] || { name: providerName };
2132-
2133-                var tab = document.createElement('button');
2134-                tab.className = 'response-tab' + (tabIndex === 0 ? ' active' : '');
2135:                tab.textContent = providerConfig.name;
2136-                tab.dataset.index = tabIndex;
2137-                (function(idx) {
2138-                    tab.onclick = function() { switchRound2Tab(idx); };
--
2146-                if (response.success) {
2147-                    content.innerHTML = '
' +
2148-                        '
' +
2149:                        '' + escapeHtml(providerConfig.name) + ' Round 2' +
2150-                        'Model: ' + escapeHtml(response.model || '?') + ' | Time: ' + (response.request_time ? response.request_time.toFixed(2) : '?') + 's | Cost: $' + (response.cost ? response.cost.toFixed(6) : '0') + '' +
2151-                        '
' +
2152-                        '
' + escapeHtml(response.response) + '
' +
--
2248-                question: question.trim()
2249-            });
2250-
2251:            fetchWithTimeout('../aiapi/api_opus/api/query.php', {
2252-                method: 'POST',
2253-                headers: { 'Content-Type': 'application/json' },
2254-                body: JSON.stringify(requestBody)
--
2385-                };
2386-                requestBody['model_' + provider] = model;
2387-
2388:                var response = await fetch('../aiapi/api_opus/api/query.php', {
2389-                    method: 'POST',
2390-                    headers: {'Content-Type': 'application/json'},
2391-                    body: JSON.stringify(requestBody)
--
2729-        // Load AI providers from API
2730-        async function loadProviders() {
2731-            try {
2732:                const response = await fetch('../aiapi/api_opus/api/query.php?action=providers');
2733-                const data = await response.json();
2734-
2735-                if (data.success) {
--
2780-                div.innerHTML = `
2781-                    <input type="checkbox" name="providers[]" value="${key}" id="provider_${key}" onclick="event.stopPropagation(); toggleProvider('${key}', this.checked)">
2782-

2783:
${provider.name}

2784-                        ${modelsHtml}
2785-

2786-                `;
--
3165-
3166-                // Send to AI API with timeout
3167-                var timeoutSec = parseInt(document.getElementById('provider-timeout').value) || 120;
3168:                var response = await fetchWithTimeout('../aiapi/api_opus/api/query.php', {
3169-                    method: 'POST',
3170-                    headers: { 'Content-Type': 'application/json' },
3171-                    body: JSON.stringify(requestBody)
--
3342-            for (var key in data.responses) {
3343-                var response = data.responses[key];
3344-                var providerName = key.replace('_response', '');
3345:                var providerConfig = providers[providerName] || { name: providerName };
3346-
3347-                // Store for Round 2 / follow-up
3348-                lastRound1Responses[providerName] = response;
--
3350-                // Create tab
3351-                var tab = document.createElement('button');
3352-                tab.className = 'response-tab' + (tabIndex === 0 ? ' active' : '');
3353:                tab.textContent = providerConfig.name + (response.success ? '' : ' (fail)');
3354-                tab.dataset.index = tabIndex;
3355-                (function(idx) {
3356-                    tab.onclick = function() { switchResponseTab(idx); };
--
3374-                        '
' +
3375-                        '
' +
3376-                        '
' +
3377:                        '' + escapeHtml(providerConfig.name) + '' +
3378-                        '' +
3379-                        'Model: ' + escapeHtml(response.model || 'Unknown') + ' | ' +
3380-                        'Time: ' + (response.request_time ? response.request_time.toFixed(2) : '?') + 's | ' +
--

[Showing results with pagination = limit: 100, offset: 0]

Prompt:
I need to understand the AI agent system in thread_view.php to help the user craft the best analysis prompts. Find:

1. All AI_AGENTS defined — their names, system prompts, and quick prompts
2. The content modes available (stripped/smart/full)
3. How the two-round AI feature works — what happens in round 1 vs round 2
4. Any existing quick prompt buttons and what they contain
5. The available AI providers and models (MODEL_LIMITS)
6. How context builder threads get included in the AI request
7. Any token limits or truncation logic

Focus on /var/www/mssql.danmarcrm.com/dev1/extension2v1/public/thread_view.php

Return the full text of each agent's systemPrompt and quickPrompts so I can see exactly what analysis capabilities exist.


Grep

const providers|let providers|var providers

Prompt:
I need to understand the AI agent system in thread_view.php to help the user craft the best analysis prompts. Find:

1. All AI_AGENTS defined — their names, system prompts, and quick prompts
2. The content modes available (stripped/smart/full)
3. How the two-round AI feature works — what happens in round 1 vs round 2
4. Any existing quick prompt buttons and what they contain
5. The available AI providers and models (MODEL_LIMITS)
6. How context builder threads get included in the AI request
7. Any token limits or truncation logic

Focus on /var/www/mssql.danmarcrm.com/dev1/extension2v1/public/thread_view.php

Return the full text of each agent's systemPrompt and quickPrompts so I can see exactly what analysis capabilities exist.


1450-                'body' => $email['body_text'] ?? $email['body_html'] ?? ''
1451-            ];
1452-        }, $emails)) ?>;
1453-
1454-        // Provider data
1455:        let providers = {};
1456-        let selectedProvider = null;
1457-        let selectedModel = null;
1458-
1459-        // Primary contact info from PHP
1460-        const primaryContactName = '<?= addslashes($primaryContactName ?: '') ?>';
--
2202-
2203-            followUpContent += '=== END PREVIOUS ANALYSES ===\n\n';
2204-            followUpContent += 'FOLLOW-UP QUESTION: ' + question.trim();
2205-
2206-            // Use currently selected providers
2207:            var providersList = getSelectedProviders();
2208-            if (providersList.length === 0) {
2209-                alert('No AI providers selected.');
2210-                return;
2211-            }
2212-
--
2372-            document.getElementById('d3-chart-container').style.display = 'block';
2373-            document.getElementById('d3-chart').innerHTML = '
Generating chart data with AI...
';
2374-
2375-            try {
2376-                // Send to AI proxy to generate structured JSON
2377:                var providersList = getSelectedProviders();
2378-                var provider = providersList.length > 0 ? providersList[0] : 'claude';
2379-                var modelSel = document.getElementById('model_' + provider);
2380-                var model = modelSel ? modelSel.value : 'claude-sonnet-4-6';
2381-
2382-                var requestBody = {

Prompt:
I need to understand the AI agent system in thread_view.php to help the user craft the best analysis prompts. Find:

1. All AI_AGENTS defined — their names, system prompts, and quick prompts
2. The content modes available (stripped/smart/full)
3. How the two-round AI feature works — what happens in round 1 vs round 2
4. Any existing quick prompt buttons and what they contain
5. The available AI providers and models (MODEL_LIMITS)
6. How context builder threads get included in the AI request
7. Any token limits or truncation logic

Focus on /var/www/mssql.danmarcrm.com/dev1/extension2v1/public/thread_view.php

Return the full text of each agent's systemPrompt and quickPrompts so I can see exactly what analysis capabilities exist.


Prompt:
I need to understand the AI agent system in thread_view.php to help the user craft the best analysis prompts. Find:

1. All AI_AGENTS defined — their names, system prompts, and quick prompts
2. The content modes available (stripped/smart/full)
3. How the two-round AI feature works — what happens in round 1 vs round 2
4. Any existing quick prompt buttons and what they contain
5. The available AI providers and models (MODEL_LIMITS)
6. How context builder threads get included in the AI request
7. Any token limits or truncation logic

Focus on /var/www/mssql.danmarcrm.com/dev1/extension2v1/public/thread_view.php

Return the full text of each agent's systemPrompt and quickPrompts so I can see exactly what analysis capabilities exist.


1582-            // Add context threads + checkbox threads estimate
1583-            // Use emailCount * 500 chars per stripped email (realistic average)
1584-            var checkboxThreads = getSelectedThreads();
1585:            contextThreads.forEach(function(ct) { total += ct.emailCount * 500; });
1586-            total += checkboxThreads.length * 5000; // rough estimate for checkbox threads
1587-            return Math.ceil(total / 3.5);
1588-        }
--
1610-        // ============================================
1611-        // CONTEXT BUILDER
1612-        // ============================================
1613:        var contextThreads = []; // {threadId, subject, emailCount}
1614-        var contextSearchTimeout = null;
1615-        var contactMemories = [];
1616-
--
1673-
1674-                    // "Add All" buttons
1675-                    var notAddedCount = threads.filter(function(t) {
1676:                        return t.thread_id !== currentThreadId && !contextThreads.some(function(ct) { return ct.threadId === t.thread_id; });
1677-                    }).length;
1678-
1679-                    html += '
';
--
1687-                    html += '
';
1688-
1689-                    threads.forEach(function(t) {
1690:                        var already = contextThreads.some(function(ct) { return ct.threadId === t.thread_id; });
1691-                        var isCurrent = t.thread_id === currentThreadId;
1692-                        html += '
';
1693-                        html += '
';
--
1719-            var threads = window._lastSearchThreads || [];
1720-            var added = 0;
1721-            threads.forEach(function(t) {
1722:                if (t.thread_id !== currentThreadId && !contextThreads.some(function(ct) { return ct.threadId === t.thread_id; })) {
1723:                    contextThreads.push({
1724-                        threadId: t.thread_id,
1725-                        subject: t.subject || '(No Subject)',
1726-                        emailCount: t.email_count || 0
--
1762-                // Add all to context
1763-                var added = 0;
1764-                allThreads.forEach(function(t) {
1765:                    if (t.thread_id !== currentThreadId && !contextThreads.some(function(ct) { return ct.threadId === t.thread_id; })) {
1766:                        contextThreads.push({
1767-                            threadId: t.thread_id,
1768-                            subject: t.subject || '(No Subject)',
1769-                            emailCount: t.email_count || 0
--
1801-                    window._lastContactThreads = threads;
1802-
1803-                    threads.forEach(function(t) {
1804:                        var already = contextThreads.some(function(ct) { return ct.threadId === t.thread_id; });
1805-                        var isCurrent = t.thread_id === currentThreadId;
1806-                        html += '
';
1807-                        html += '
';
--
1828-        function addAllContactThreads() {
1829-            var threads = window._lastContactThreads || [];
1830-            threads.forEach(function(t) {
1831:                if (t.thread_id !== currentThreadId && !contextThreads.some(function(ct) { return ct.threadId === t.thread_id; })) {
1832:                    contextThreads.push({
1833-                        threadId: t.thread_id,
1834-                        subject: t.subject || '(No Subject)',
1835-                        emailCount: t.email_count || 0
--
1845-
1846-        function addThreadToContext(threadId, subject, emailCount) {
1847-            if (threadId === currentThreadId) return;
1848:            if (contextThreads.some(function(ct) { return ct.threadId === threadId; })) return;
1849:            contextThreads.push({
1850-                threadId: threadId,
1851-                subject: subject,
1852-                emailCount: emailCount
--
1859-        }
1860-
1861-        function removeThreadFromContext(threadId) {
1862:            contextThreads = contextThreads.filter(function(ct) { return ct.threadId !== threadId; });

[Showing results with pagination = limit: 80, offset: 0]

Prompt:
I need to understand the AI agent system in thread_view.php to help the user craft the best analysis prompts. Find:

1. All AI_AGENTS defined — their names, system prompts, and quick prompts
2. The content modes available (stripped/smart/full)
3. How the two-round AI feature works — what happens in round 1 vs round 2
4. Any existing quick prompt buttons and what they contain
5. The available AI providers and models (MODEL_LIMITS)
6. How context builder threads get included in the AI request
7. Any token limits or truncation logic

Focus on /var/www/mssql.danmarcrm.com/dev1/extension2v1/public/thread_view.php

Return the full text of each agent's systemPrompt and quickPrompts so I can see exactly what analysis capabilities exist.


Prompt:
I need to understand the AI agent system in thread_view.php to help the user craft the best analysis prompts. Find:

1. All AI_AGENTS defined — their names, system prompts, and quick prompts
2. The content modes available (stripped/smart/full)
3. How the two-round AI feature works — what happens in round 1 vs round 2
4. Any existing quick prompt buttons and what they contain
5. The available AI providers and models (MODEL_LIMITS)
6. How context builder threads get included in the AI request
7. Any token limits or truncation logic

Focus on /var/www/mssql.danmarcrm.com/dev1/extension2v1/public/thread_view.php

Return the full text of each agent's systemPrompt and quickPrompts so I can see exactly what analysis capabilities exist.


2574-
2575-        // ============================================
2576-        // CONVERSATION MEMORY - ChatGPT-like history
2577-        // ============================================
2578-        // Conversation history - stores Q&A pairs for context
2579:        let conversationHistory = [];
2580-        const MAX_CONVERSATION_HISTORY = 20; // Maximum Q&A pairs to keep
2581-
2582-        // Load conversation history from sessionStorage (persists during page session)
2583-        function loadConversationHistory() {
2584-            const stored = sessionStorage.getItem(`conversation_${currentThreadId}`);
2585-            if (stored) {
2586-                try {
2587:                    conversationHistory = JSON.parse(stored);
2588-                    updateConversationDisplay();
2589-                } catch (e) {
2590:                    conversationHistory = [];
2591-                }
2592-            }
2593-        }
2594-
2595-        // Save conversation history to sessionStorage
--
2610-
2611-            conversationHistory.push(entry);
2612-
2613-            // Keep only the last MAX_CONVERSATION_HISTORY entries
2614-            if (conversationHistory.length > MAX_CONVERSATION_HISTORY) {
2615:                conversationHistory = conversationHistory.slice(-MAX_CONVERSATION_HISTORY);
2616-            }
2617-
2618-            saveConversationHistory();
2619-            updateConversationDisplay();
2620-        }
2621-
2622-        // Clear conversation history
2623-        function clearConversationHistory() {
2624-            if (confirm('Clear all conversation history for this thread?')) {
2625:                conversationHistory = [];
2626-                saveConversationHistory();
2627-                updateConversationDisplay();
2628-            }
2629-        }
2630-

Prompt:
I need to understand the AI agent system in thread_view.php to help the user craft the best analysis prompts. Find:

1. All AI_AGENTS defined — their names, system prompts, and quick prompts
2. The content modes available (stripped/smart/full)
3. How the two-round AI feature works — what happens in round 1 vs round 2
4. Any existing quick prompt buttons and what they contain
5. The available AI providers and models (MODEL_LIMITS)
6. How context builder threads get included in the AI request
7. Any token limits or truncation logic

Focus on /var/www/mssql.danmarcrm.com/dev1/extension2v1/public/thread_view.php

Return the full text of each agent's systemPrompt and quickPrompts so I can see exactly what analysis capabilities exist.


Prompt:
I need to understand the AI agent system in thread_view.php to help the user craft the best analysis prompts. Find:

1. All AI_AGENTS defined — their names, system prompts, and quick prompts
2. The content modes available (stripped/smart/full)
3. How the two-round AI feature works — what happens in round 1 vs round 2
4. Any existing quick prompt buttons and what they contain
5. The available AI providers and models (MODEL_LIMITS)
6. How context builder threads get included in the AI request
7. Any token limits or truncation logic

Focus on /var/www/mssql.danmarcrm.com/dev1/extension2v1/public/thread_view.php

Return the full text of each agent's systemPrompt and quickPrompts so I can see exactly what analysis capabilities exist.


1571-        function estimateCurrentTokens() {
1572-            var content = buildEmailContent();
1573-            var question = (document.getElementById('ai-question').value || '').trim();
1574-            var context = (document.getElementById('ai-context').value || '').trim();
1575-            var agent = AI_AGENTS[selectedAgent];
1576-            var total = content.length;
1577-            if (agent) total += agent.systemPrompt.length;
1578-            if (question) total += question.length;
1579-            if (context) total += context.length;
1580-            // Add conversation history estimate
1581:            total += buildConversationContext().length;
1582-            // Add context threads + checkbox threads estimate
1583-            // Use emailCount * 500 chars per stripped email (realistic average)
1584-            var checkboxThreads = getSelectedThreads();
1585-            contextThreads.forEach(function(ct) { total += ct.emailCount * 500; });
1586-            total += checkboxThreads.length * 5000; // rough estimate for checkbox threads
1587-            return Math.ceil(total / 3.5);
1588-        }
1589-
1590-        function updateTokenEstimate() {
1591-            var el = document.getElementById('token-estimate');
--
2622-        // Clear conversation history
2623-        function clearConversationHistory() {
2624-            if (confirm('Clear all conversation history for this thread?')) {
2625-                conversationHistory = [];
2626-                saveConversationHistory();
2627-                updateConversationDisplay();
2628-            }
2629-        }
2630-
2631-        // Build conversation context string for AI
2632:        function buildConversationContext() {
2633-            if (conversationHistory.length === 0) return '';
2634-
2635:            let context = '\n\n=== PREVIOUS CONVERSATION HISTORY ===\n';
2636-            context += '(The user has asked previous questions about this email thread. Use this context for continuity.)\n\n';
2637-
2638-            conversationHistory.forEach((entry, index) => {
2639-                context += `--- Exchange ${index + 1} (${new Date(entry.timestamp).toLocaleString()}) ---\n`;
2640-                context += `USER QUESTION: ${entry.question}\n`;
2641-                context += `AI RESPONSE (${entry.provider}/${entry.model}):\n${entry.answer}\n\n`;
2642-            });
2643-
2644-            context += '=== END OF PREVIOUS CONVERSATION ===\n\n';
2645-            context += 'Please consider the above conversation history when answering the new question. ';
--
3049-                    memoryBlock = '=== KNOWN FACTS ABOUT ' + primaryContactName.toUpperCase() + ' ===\n';
3050-                    contactMemories.forEach(function(m) {
3051-                        memoryBlock += '- ' + (m.fact || m) + '\n';
3052-                    });
3053-                    memoryBlock += '=== END KNOWN FACTS ===\n\n';
3054-                }
3055-
3056-                var fullContent = agentPrompt + memoryBlock + buildEmailContent();
3057-
3058-                // ADD CONVERSATION HISTORY FOR CONTEXT (ChatGPT-like memory)
3059:                var conversationContext = buildConversationContext();
3060-                if (conversationContext) {
3061-                    fullContent += conversationContext;
3062-                    console.log('Including conversation history:', conversationHistory.length, 'previous exchanges');
3063-                }
3064-
3065-                // Add question if provided
3066-                var actualQuestion = question || 'Please analyze this email conversation and provide insights on:\n1. Key points and summary\n2. Sentiment and tone analysis\n3. Any potential concerns or red flags\n4. Recommended next steps';
3067-
3068-                if (question) {
3069-                    fullContent += '\n\nCURRENT USER QUESTION:\n' + question;

Prompt:
I need to understand the AI agent system in thread_view.php to help the user craft the best analysis prompts. Find:

1. All AI_AGENTS defined — their names, system prompts, and quick prompts
2. The content modes available (stripped/smart/full)
3. How the two-round AI feature works — what happens in round 1 vs round 2
4. Any existing quick prompt buttons and what they contain
5. The available AI providers and models (MODEL_LIMITS)
6. How context builder threads get included in the AI request
7. Any token limits or truncation logic

Focus on /var/www/mssql.danmarcrm.com/dev1/extension2v1/public/thread_view.php

Return the full text of each agent's systemPrompt and quickPrompts so I can see exactly what analysis capabilities exist.


Prompt:
I need to understand the AI agent system in thread_view.php to help the user craft the best analysis prompts. Find:

1. All AI_AGENTS defined — their names, system prompts, and quick prompts
2. The content modes available (stripped/smart/full)
3. How the two-round AI feature works — what happens in round 1 vs round 2
4. Any existing quick prompt buttons and what they contain
5. The available AI providers and models (MODEL_LIMITS)
6. How context builder threads get included in the AI request
7. Any token limits or truncation logic

Focus on /var/www/mssql.danmarcrm.com/dev1/extension2v1/public/thread_view.php

Return the full text of each agent's systemPrompt and quickPrompts so I can see exactly what analysis capabilities exist.


1579-            if (context) total += context.length;
1580-            // Add conversation history estimate
1581-            total += buildConversationContext().length;
1582-            // Add context threads + checkbox threads estimate
1583-            // Use emailCount * 500 chars per stripped email (realistic average)
1584:            var checkboxThreads = getSelectedThreads();
1585-            contextThreads.forEach(function(ct) { total += ct.emailCount * 500; });
1586-            total += checkboxThreads.length * 5000; // rough estimate for checkbox threads
1587-            return Math.ceil(total / 3.5);
1588-        }
1589-
--
2846-            const checkboxes = document.querySelectorAll('input[name="providers[]"]:checked');
2847-            return Array.from(checkboxes).map(cb => cb.value);
2848-        }
2849-
2850-        // Get selected additional threads
2851:        function getSelectedThreads() {
2852-            const checkboxes = document.querySelectorAll('input[name="additional_threads[]"]:checked');
2853-            return Array.from(checkboxes).map(cb => cb.value);
2854-        }
2855-
2856-        /**
--
3009-            }
3010-
3011-            var question = document.getElementById('ai-question').value.trim();
3012-            var context = document.getElementById('ai-context').value.trim();
3013-            var contentMode = document.getElementById('content-mode').value;
3014:            var additionalThreads = getSelectedThreads();
3015-            // Also include context builder threads
3016-            var contextThreadIds = contextThreads.map(function(ct) { return ct.threadId; });
3017:            var allAdditionalThreads = additionalThreads.concat(contextThreadIds);
3018-
3019-            // Build models map for each selected provider
3020-            var modelsMap = {};
3021-            selectedProvidersList.forEach(function(provider) {
3022-                var modelSelect = document.getElementById('model_' + provider);
--
3076-                    fullContent += '\n\nADDITIONAL CONTEXT:\n' + context;
3077-                }
3078-
3079-                // Fetch additional thread content (checkboxes + context builder threads)
3080-                // Uses thread_for_ai endpoint (lightweight, stripped bodies, no analysis)
3081:                if (allAdditionalThreads.length > 0) {
3082:                    document.getElementById('loading-message').textContent = 'Loading additional threads (0/' + allAdditionalThreads.length + ')...';
3083-
3084:                    for (var i = 0; i < allAdditionalThreads.length; i++) {
3085:                        var tid = allAdditionalThreads[i];
3086:                        document.getElementById('loading-message').textContent = 'Loading thread ' + (i + 1) + '/' + allAdditionalThreads.length + '...';
3087-                        try {
3088-                            var threadResp = await fetch('api.php?action=thread_for_ai&thread_id=' + tid);
3089-                            if (!threadResp.ok) {
3090-                                console.warn('Failed to fetch thread ' + tid + ': HTTP ' + threadResp.status);
3091-                                fullContent += '\n\n--- THREAD ' + (i + 1) + ': FAILED TO LOAD (HTTP ' + threadResp.status + ') ---\n';
3092-                                continue;
3093-                            }
3094-                            var thread = await threadResp.json();
3095-
3096-                            if (thread.emails && thread.emails.length > 0) {
3097:                                fullContent += '\n\n--- ADDITIONAL THREAD ' + (i + 1) + ' OF ' + allAdditionalThreads.length + ' ---\n';
3098-                                fullContent += 'Subject: ' + (thread.emails[0].subject || '(No Subject)') + '\n\n';
3099-                                // Bodies already stripped by thread_for_ai endpoint
3100-                                fullContent += processAdditionalThreadContent(thread.emails, 'full');
3101-                            }
3102-                        } catch (fetchErr) {
--
3124-                    providers: selectedProvidersList,
3125-                    models: modelsMap,
3126-                    content_length: fullContent.length,
3127-                    estimated_tokens: finalTokens,
3128-                    question: actualQuestion,
3129:                    context_threads: allAdditionalThreads,
3130-                    truncated: wasTruncated,
3131-                    full_content: fullContent.substring(0, 10000) + (fullContent.length > 10000 ? '\n...(truncated in log, full was ' + fullContent.length + ' chars)' : '')
3132-                });
3133-
3134-                // Console log
3135-                console.log('=== AI REQUEST LOG ===');
3136-                console.log('Agent:', selectedAgent);
3137-                console.log('Providers:', selectedProvidersList);
3138-                console.log('Models:', modelsMap);
3139-                console.log('Content Mode:', contentMode);
3140:                console.log('Context Threads:', allAdditionalThreads.length);
3141-                console.log('Conversation History:', conversationHistory.length);
3142-                console.log('Content Length:', fullContent.length, 'chars');
3143-                console.log('Estimated Tokens:', finalTokens);
3144-                console.log('======================');
3145-
--
3226-                                firstSuccessfulResponse = providerResponse;
3227-                            }
3228-
3229-                            // Save to MongoDB via our API
3230-                            await saveAIResponse({
3231:                                thread_ids: [currentThreadId].concat(allAdditionalThreads),
3232-                                contact_id: primaryContactId,
3233-                                question: actualQuestion,
3234-                                context: context,
3235-                                agent: selectedAgent,
3236-                                content_mode: contentMode,

Prompt:
I need to understand the AI agent system in thread_view.php to help the user craft the best analysis prompts. Find:

1. All AI_AGENTS defined — their names, system prompts, and quick prompts
2. The content modes available (stripped/smart/full)
3. How the two-round AI feature works — what happens in round 1 vs round 2
4. Any existing quick prompt buttons and what they contain
5. The available AI providers and models (MODEL_LIMITS)
6. How context builder threads get included in the AI request
7. Any token limits or truncation logic

Focus on /var/www/mssql.danmarcrm.com/dev1/extension2v1/public/thread_view.php

Return the full text of each agent's systemPrompt and quickPrompts so I can see exactly what analysis capabilities exist.


pls create the flow to be automates


The user wants me to automate the 5-pass analysis flow so they don't have to manually run each pass one by one. They want to click a button and have the system automatically run all 5 analysis passes sequentially.

Let me think about how to implement this:

1. Add a new "Deep Analysis" button/mode in thread_view.php
2. When clicked, it runs all 5 passes automatically:
   - Pass 1: General agent with the timeline prompt
   - Pass 2: Deception agent with the pressure/manipulation prompt
   - Pass 3: Profiler agent with the psychological profiles prompt
   - Pass 4: Quote analyst with the financial analysis prompt
   - Pass 5: General agent with the win-back strategy prompt
3. Each pass uses the same providers/models selected
4. Results are displayed sequentially, each in its own section
5. Conversation history builds up between passes

I'll store the 5 preset prompts as a JavaScript constant, add a prominent "Deep Analysis" button, and create a function that loops through each pass while displaying progress indicators and waiting for each one to complete before moving to the next.

Looking at how `submitToAI` works, I realize it's handling a lot of UI complexity, so instead of calling it directly, I should build a simpler flow that sets the agent and question, calls the API, stores the response, updates the conversation history, and moves to the next pass. I'm thinking the cleanest approach is a modal with checkboxes for each of the 5 passes (all selected by default) and a "Start Analysis" button that kicks off the automated sequence.


TodoWrite

Add Deep Analysis preset prompts as JS constant

Add Deep Analysis button and results panel to thread_view.php UI

Implement automated multi-pass analysis function

Implementing automated multi-pass analysis

Add progress tracking and sequential pass execution

Test the automated flow


Todos have been modified successfully. Ensure that you continue to use the todo list to track your progress. Please proceed with the current tasks if applicable

Add Deep Analysis preset prompts as JS constant

Add Deep Analysis button and results panel to thread_view.php UI

Implement automated multi-pass analysis function

Implementing automated multi-pass analysis

Add progress tracking and sequential pass execution

Test the automated flow


Let me first understand how the AI API calls work so I can replicate them in the automated flow.