Bash

php -l /var/www/mssql.danmarcrm.com/dev1/extension2v1/public/thread_view.php 2>&1 && php -l /var/www/mssql.danmarcrm.com/dev1/extension2v1/public/api.php 2>&1

Final PHP syntax check


No syntax errors detected in /var/www/mssql.danmarcrm.com/dev1/extension2v1/public/thread_view.php
No syntax errors detected in /var/www/mssql.danmarcrm.com/dev1/extension2v1/public/api.php


Everything is wired up. Here's what's been added:

## Load Deep Analysis — Persistence

**When you return to the thread page:**
1. A **"Load Deep Analysis"** button appears in the "Previous AI Analyses" card header (gradient purple/blue)
2. Click it — it fetches all 5 passes from MongoDB and re-renders the same Deep Analysis panel with all provider responses organized by pass

**How it works:**
- New API: `load_deep_analysis` — fetches responses tagged with `deep_analysis_pass` from MongoDB
- **Fallback detection** — for your existing run (which was saved before the tagging code), it matches question text patterns to identify which pass each response belongs to. Verified: all 5 passes with 2 providers each were correctly detected.
- Future runs will save `deep_analysis_pass`, `deep_analysis_name`, and `agent` fields directly to MongoDB

**Also added:**
- Deep analysis pass **badges** on saved responses in the "Previous AI Analyses" list (e.g. "Pass 3: Psychological Profiles")
- Works in both the PHP-rendered initial list and the JS "Load More" dynamically-loaded responses

Try refreshing thread `18668e45237c2001` — you should see the "Load Deep Analysis" button and pass badges on the saved responses.


I found it thank you, a new tab with all these results would be great pls create it.
Additionaly, how do we obtain these 5 steps? I guess we have some hardcoded instructions to be send to to AI models?
If so, we need a config section to customize them and add new requests.
In user interfaces I think the easisest way would be a dropdown to select what to be extracted/generated and hit the Deep Analyze button

Pls add a sections as:
How to regain client.
Formulate an email to gain his interest back
Formulate an email to gain his interest back but must have AI content for his company taking into account his company and how his company can benefit out of ai
Consider a 2-3 steps indirect interactions not necesarely direct contact that can be done to bring the client back
Use any persuasive thecniques as possible.

For exising clients pls create a Quote Analysis value so we can use it to get the most amoi


[Request interrupted by user]


I found it thank you, a new tab with all these results would be great pls create it.
Additionaly, how do we obtain these 5 steps? I guess we have some hardcoded instructions to be send to to AI models?
If so, we need a config section to customize them and add new requests.
In user interfaces I think the easisest way would be a dropdown to select what to be extracted/generated and hit the Deep Analyze button

Pls add a sections as:
How to regain client.
Formulate an email to gain his interest back
Formulate an email to gain his interest back but must have AI content for his company taking into account his company and how his company can benefit out of ai
Consider a 2-3 steps indirect interactions not necesarely direct contact that can be done to bring the client back
Use any persuasive thecniques as possible.

For exising clients pls create a Quote Analysis value so we can use it to get the most amount of money for the task/projekt being discussed. Again pls add a few methods including the deep persuasion ones, and if needed we can ask or induce different ideas to clients and have them reply .. have a few rounds and in the end decide the amount to quote.


Task

Explore deep analysis UI/config

Prompt:
I need to understand the current Deep Analysis implementation in thread_view.php to plan a major enhancement. Find:

1. The full `DEEP_ANALYSIS_PASSES` constant — all 5 passes with their IDs, names, agents, and prompts
2. The `runDeepAnalysis()` function — how it iterates passes and builds content
3. The Deep Analysis button HTML and results container
4. The `loadDeepAnalysis()` function that loads saved results
5. The AI_AGENTS constant — all agent definitions
6. How the "Send to AI" button area is structured (where Deep Analysis button sits)
7. Any existing dropdown or selection UI patterns in the page

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

Return exact line numbers for all key sections so I can plan precise edits.


Explore deep analysis UI/config


Perfect! Now I have all the information. Let me compile the comprehensive report for the user.

## Deep Analysis Implementation in thread_view.php - Complete Reference Guide

I've located all the key components for your Deep Analysis feature. Here's the complete breakdown with exact line numbers:

---

### 1. **DEEP_ANALYSIS_PASSES Constant** (Lines 1567-1598)

The 5 passes are:

| Pass # | ID | Name | Agent | Function |
|--------|----|----|-------|----------|
| 1 | `timeline` | Business Relationship Timeline | `general` | Build chronological relationship history, identify turning points, rate satisfaction curve |
| 2 | `pressure` | Pressure & Manipulation Detection | `deception_detector` | Analyze pressure tactics, broken promises, double standards, escalation patterns |
| 3 | `profiles` | Psychological Profiles | `contact_profiler` | Build detailed profiles for each contact (communication style, motivations, conflict handling) |
| 4 | `financial` | Financial & Business Patterns | `quote_analyst` | Track all prices, scope creep, payment behavior, competitor mentions, profitability |
| 5 | `winback` | Win-Back Strategy | `general` | Create concrete re-engagement strategy using insights from previous 4 passes |

**Key snippet (lines 1567-1598):**
```javascript
const DEEP_ANALYSIS_PASSES = [
    {
        id: 'timeline',
        name: 'Business Relationship Timeline',
        agent: 'general',
        prompt: '...'
    },
    // ... 4 more passes
];
```

Each pass has `id`, `name`, `agent`, and `prompt` properties. The prompts are detailed instructions (200-400 chars each) that the AI follows.

---

### 2. **AI_AGENTS Constant** (Lines 1504-1560)

5 agents defined:

| Agent ID | Name | Icon | Purpose |
|----------|------|------|---------|
| `general` | General | 💬 | Business intelligence, summaries, analysis |
| `quote_analyst` | Quotes | 💰 | Pricing analysis, negotiation patterns |
| `deception_detector` | Deception | 🔍 | Detect contradictions, pressure tactics, deception risk |
| `contact_profiler` | Profiler | 👤 | Build psychological/business profiles |
| `email_composer` | Composer | ✍ | Draft professional replies |

Each agent has:
- `name` - display name
- `icon` - emoji
- `systemPrompt` - detailed instructions (200+ chars)
- `quickPrompts` - array of 4 pre-built prompts for the agent

**Example (general agent, lines 1505-1514):**
```javascript
general: {
    name: 'General',
    icon: '💬',
    systemPrompt: 'You are a highly capable business intelligence AI assistant...',
    quickPrompts: [
        'Summarize the key points',
        'What are the action items?',
        'What is the overall sentiment?',
        'What should I reply?'
    ]
}
```

---

### 3. **runDeepAnalysis() Function** (Lines 3079-3350)

**Main flow:**
1. **Validation** (lines 3080-3098): Check providers selected, confirm with user showing all 5 pass names
2. **Content Setup** (lines 3100-3156): Build base email content + additional threads + contact memory block
3. **Sequential Pass Loop** (lines 3160-3329): For each pass:
   - Create progress placeholder div (id: `deep-pass-{passId}`)
   - Build full content = agent systemPrompt + base emails + conversation history + pass prompt + user context
   - Truncate to model limit (85% of max tokens)
   - Send to all selected providers via `api_opus/api/query.php`
   - Display results in real-time as they arrive
   - Save each provider response to MongoDB with `saveAIResponse()`
   - Add to conversation history (builds up across passes)
   - 2-second pause between passes (rate limiting)
4. **Completion** (lines 3331-3340): Show summary message

**Critical points:**
- Line 3165: Progress display `'Pass ' + passNum + '/' + DEEP_ANALYSIS_PASSES.length`
- Line 3180: Agent system prompt injected: `'=== AI AGENT INSTRUCTIONS ===\n' + passAgent.systemPrompt + '\n=== END AGENT INSTRUCTIONS ===\n\n'`
- Line 3190: Pass question added: `fullContent += '\n\nCURRENT USER QUESTION:\n' + pass.prompt;`
- Lines 3288-3289: Saved responses tagged with `deep_analysis_pass` and `deep_analysis_name`

---

### 4. **loadDeepAnalysis() Function** (Lines 3353-3413)

**Main flow:**
1. Show deep-analysis-container
2. Fetch saved results via `api.php?action=load_deep_analysis&thread_id=...`
3. Loop through `data.passes` array (each pass has responses from multiple providers)
4. Build UI matching `runDeepAnalysis()` layout (pass header + provider responses)
5. Display pass number, agent icon/name, provider responses with timing/cost

**Key structure:**
- Line 3377: Safely get agent: `var passAgent = AI_AGENTS[pass.agent] || { name: pass.agent, icon: '' };`
- Lines 3387-3395: Render each provider's response from the pass
- Line 3406: Show footer with total responses count

---

### 5. **Deep Analysis Button & Results Container HTML**

**Button location (lines 1442-1445):**
```html
<button class="btn" id="deep-analysis-btn" onclick="runDeepAnalysis()" style="flex:1;background:linear-gradient(135deg,#8b5cf6,#3b82f6);color:white;font-weight:600;">
    Deep Analysis (5-Pass)
</button>
```
Sits NEXT TO "Send to AI" button in same flex container.

**Results container (lines 1221-1233):**
```html
<div id="deep-analysis-container" style="display:none; margin-bottom:20px;">
    <div class="card">
        <div class="card-header" style="background:linear-gradient(135deg,#8b5cf6,#3b82f6);color:white;">
            <h2>Deep Analysis</h2>
            <div style="display:flex;gap:8px;align-items:center;">
                <span id="deep-analysis-progress"><!-- Progress text --></span>
                <button class="btn" onclick="document.getElementById('deep-analysis-container').style.display='none'">Close</button>
            </div>
        </div>
        <div id="deep-analysis-results"><!-- Results rendered here --></div>
    </div>
</div>
```

**Load button location (line 1241):**
```html
<button class="btn" onclick="loadDeepAnalysis()" style="background:linear-gradient(135deg,#8b5cf6,#3b82f6);color:white;font-size:0.85em;">Load Deep Analysis</button>
```
Only shown if `$hasDeepAnalysis` is true (PHP-side check).

---

### 6. **"Send to AI" Button Area Structure** (Lines 1437-1445)

```html
<div style="display:flex;gap:8px;">
    <button class="btn btn-primary" id="submit-ai-btn" onclick="submitToAI()" style="flex:1;">
        Send to AI
    </button>
    <button class="btn" id="deep-analysis-btn" onclick="runDeepAnalysis()" style="flex:1;background:linear-gradient(135deg,#8b5cf6,#3b82f6);color:white;font-weight:600;">
        Deep Analysis (5-Pass)
    </button>
</div>
```

- **Parent:** `<div style="display:flex;gap:8px;">` (flex container, 8px gap)
- **Left button:** "Send to AI" (`#submit-ai-btn`) - primary style, flex:1
- **Right button:** "Deep Analysis" (`#deep-analysis-btn`) - purple gradient, flex:1
- Both buttons disabled during operation via `.disabled` flag

---

### 7. **Existing Dropdown/Selection UI Patterns**

**Agent Selection (Lines 1369-1374, HTML):**
```html
<div class="agent-buttons" id="agent-buttons">
    <!-- Populated by JS on load -->
</div>
<div class="agent-quick-prompts" id="agent-quick-prompts" style="display:none;">
    <!-- Quick prompt buttons per agent -->
</div>
```

**Agent Buttons CSS (Lines 817-848):**
- `.agent-buttons`: flex wrap with 6px gap
- `.agent-btn`: pill-shaped buttons, 2px solid border, 6px top/bottom padding
- `.agent-btn.active`: purple accent background + color

**Agent Selection JS (Lines 1991-2013):**
```javascript
function initAgentButtons() {
    var container = document.getElementById('agent-buttons');
    for (var id in AI_AGENTS) {
        var agent = AI_AGENTS[id];
        html += '<button class="agent-btn' + (id === selectedAgent ? ' active' : '') + '" data-agent="' + id + '" onclick="selectAgent(\'' + id + '\')">';
        html += agent.icon + ' ' + agent.name;
        html += '</button>';
    }
}

function selectAgent(agentId) {
    selectedAgent = agentId;
    document.querySelectorAll('.agent-btn').forEach(btn => {
        btn.classList.toggle('active', btn.dataset.agent === agentId);
    });
    updateAgentQuickPrompts();
    updateTokenEstimate();
}
```

**Provider Selection Pattern (Lines 1378-1383):**
```html
<div class="ai-form-section">
    <label>Select AI Provider</label>
    <div class="provider-grid" id="provider-grid">
        <!-- Loaded via JavaScript -->
    </div>
</div>
```

---

### 8. **Critical Integration Points for Enhancement**

1. **Add new Deep Analysis pass:** Edit `DEEP_ANALYSIS_PASSES` constant (lines 1567-1598)
2. **Modify agent behavior:** Edit `AI_AGENTS` constant (lines 1504-1560) - systemPrompt or add new agent
3. **Change pass iteration logic:** Modify loop at lines 3160-3329 in `runDeepAnalysis()`
4. **Adjust results display:** Modify pass section HTML at lines 3169-3177
5. **Button positioning:** Edit flex container at lines 1437-1445
6. **Progress reporting:** Check `daProgress` element updates at lines 3095, 3165, 3332

---

This should give you everything you need to plan your enhancement. All line numbers are exact and verified from the current codebase.

agentId: ab68616 (for resuming to continue this agent's work if needed)
<usage>total_tokens: 41539
tool_uses: 23
duration_ms: 47916</usage>

Prompt:
I need to understand the current Deep Analysis implementation in thread_view.php to plan a major enhancement. Find:

1. The full `DEEP_ANALYSIS_PASSES` constant — all 5 passes with their IDs, names, agents, and prompts
2. The `runDeepAnalysis()` function — how it iterates passes and builds content
3. The Deep Analysis button HTML and results container
4. The `loadDeepAnalysis()` function that loads saved results
5. The AI_AGENTS constant — all agent definitions
6. How the "Send to AI" button area is structured (where Deep Analysis button sits)
7. Any existing dropdown or selection UI patterns in the page

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

Return exact line numbers for all key sections so I can plan precise edits.


Edit

# 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

# Deep Analysis Enhancement — Configurable Playbooks with Dropdown Selection

## Context

The current Deep Analysis feature has 5 hardcoded passes focused on one use case (lost client win-back). The user wants:
1. **Configurable analysis playbooks** — not hardcoded, ability to add/edit/manage passes
2. **Dropdown selection** — pick which playbook to run, then hit Deep Analysis
3. **New playbooks** for different business scenarios (win-back, quote optimization, persuasion)
4. **Results in a dedicated tab** — not just inline, a proper tabbed view for deep analysis results

## Architecture

### Replace hardcoded `DEEP_ANALYSIS_PASSES` with `DEEP_ANALYSIS_PLAYBOOKS`

A **playbook** is a named collection of passes. The dropdown selects a playbook, and the Deep Analysis button runs all passes in that playbook sequentially.

### File: `public/thread_view.php` (all changes in one file)

---

## Step 1: Define Playbooks Constant

**Replace** the current `DEEP_ANALYSIS_PASSES` array (lines 1567-1598) with `DEEP_ANALYSIS_PLAYBOOKS` object:

```javascript
const DEEP_ANALYSIS_PLAYBOOKS = {
    // ---- LOST CLIENT WIN-BACK ----
    'win_back': {
        name: 'Win Back Lost Client',
        description: 'Full relationship analysis + win-back strategy',
        passes: [
            { id: 'timeline', name: 'Business Relationship Timeline', agent: 'general', prompt: '...' },
            { id: 'pressure', name: 'Pressure & Manipulation Detection', agent: 'deception_detector', prompt: '...' },
            { id: 'profiles', name: 'Psychological Profiles', agent: 'contact_profiler', prompt: '...' },
            { id: 'financial', name: 'Financial & Business Patterns', agent: 'quote_analyst', prompt: '...' },
            { id: 'winback', name: 'Win-Back Strategy', agent: 'general', prompt: '...' }
        ]
    },

    // ---- RE-ENGAGEMENT EMAIL DRAFTS ----
    'regain_email': {
        name: 'Re-Engagement Emails',
        description: 'Draft re-engagement emails with persuasion techniques',
        passes: [
            { id: 'regain_analysis', name: 'How to Regain Client', agent: 'general', prompt: '...' },
            { id: 'regain_email_direct', name: 'Direct Re-engagement Email', agent: 'email_composer', prompt: '...' },
            { id: 'regain_email_ai', name: 'AI Value Proposition Email', agent: 'email_composer', prompt: '...' },
            { id: 'indirect_steps', name: 'Indirect Re-engagement Plan', agent: 'general', prompt: '...' },
            { id: 'persuasion_toolkit', name: 'Persuasion Techniques', agent: 'general', prompt: '...' }
        ]
    },

    // ---- QUOTE OPTIMIZATION ----
    'quote_optimize': {
        name: 'Quote Optimization',
        description: 'Maximize quote value for current client discussions',
        passes: [
            { id: 'quote_history', name: 'Quote & Pricing History', agent: 'quote_analyst', prompt: '...' },
            { id: 'client_budget', name: 'Client Budget & Willingness', agent: 'contact_profiler', prompt: '...' },
            { id: 'value_framing', name: 'Value Framing Strategy', agent: 'general', prompt: '...' },
            { id: 'anchoring', name: 'Anchoring & Negotiation Tactics', agent: 'general', prompt: '...' },
            { id: 'quote_draft', name: 'Optimal Quote Recommendation', agent: 'quote_analyst', prompt: '...' }
        ]
    },

    // ---- FULL RELATIONSHIP ANALYSIS ----
    'relationship': {
        name: 'Full Relationship Analysis',
        description: 'Comprehensive relationship health check',
        passes: [
            { id: 'timeline', name: 'Business Relationship Timeline', agent: 'general', prompt: '...' },
            { id: 'profiles', name: 'Psychological Profiles', agent: 'contact_profiler', prompt: '...' },
            { id: 'financial', name: 'Financial & Business Patterns', agent: 'quote_analyst', prompt: '...' },
            { id: 'pressure', name: 'Pressure & Manipulation Detection', agent: 'deception_detector', prompt: '...' }
        ]
    }
};
```

---

## Step 2: Playbook Dropdown + Deep Analysis Button UI

**Replace** the current button area (lines 1437-1445) with dropdown + button:

```html
<div style="display:flex;gap:8px;">
    <button class="btn btn-primary" id="submit-ai-btn" onclick="submitToAI()" style="flex:1;">
        Send to AI
    </button>
</div>
<div style="display:flex;gap:8px;margin-top:8px;">
    <select id="deep-analysis-playbook" style="flex:2;padding:8px;border-radius:6px;border:1px solid #ccc;font-size:0.9em;">
        <!-- Populated from DEEP_ANALYSIS_PLAYBOOKS by JS -->
    </select>
    <button class="btn" id="deep-analysis-btn" onclick="runDeepAnalysis()" style="flex:1;background:linear-gradient(135deg,#8b5cf6,#3b82f6);color:white;font-weight:600;">
        Deep Analysis
    </button>
</div>
```

Add JS to populate dropdown on page load:
```javascript
function initPlaybookDropdown() {
    var sel = document.getElementById('deep-analysis-playbook');
    for (var key in DEEP_ANALYSIS_PLAYBOOKS) {
        var pb = DEEP_ANALYSIS_PLAYBOOKS[key];
        var opt = document.createElement('option');
        opt.value = key;
        opt.textContent = pb.name + ' (' + pb.passes.length + ' passes)';
        opt.title = pb.description;
        sel.appendChild(opt);
    }
}
```

---

## Step 3: Update `runDeepAnalysis()` to Use Selected Playbook

Change the function to read the selected playbook from the dropdown instead of using the hardcoded `DEEP_ANALYSIS_PASSES`:

```javascript
async function runDeepAnalysis() {
    var playbookKey = document.getElementById('deep-analysis-playbook').value;
    var playbook = DEEP_ANALYSIS_PLAYBOOKS[playbookKey];
    if (!playbook) { alert('Select a playbook'); return; }
    var passes = playbook.passes;
    // ... rest of function uses `passes` instead of `DEEP_ANALYSIS_PASSES`
    // Also save `playbookKey` and `playbook.name` to MongoDB
}
```

In the `saveAIResponse` call inside the loop, add `deep_analysis_playbook: playbookKey`.

---

## Step 4: Deep Analysis Results as Dedicated Tab

Add a new tab to the thread view for deep analysis results, sitting alongside the existing email timeline and AI query panel.

**Add tab button** in the thread view navigation area (where the main thread layout is):

After the existing `ai-response-container`, add a container for the tabbed deep analysis results view. This uses the same `deep-analysis-container` div but enhances it with tabs for each playbook run.

The `loadDeepAnalysis()` function already handles this — just make the container more prominent and keep it visible as a section rather than a popup.

---

## Step 5: Save Playbook to MongoDB

Update `save_ai_response` API (api.php line ~4802) to persist new field:
- `deep_analysis_playbook` — the playbook key (e.g., 'win_back', 'quote_optimize')

Update `load_deep_analysis` API to also return the playbook key.

---

## Full Playbook Prompts

### Playbook 1: Win Back Lost Client (existing 5 passes — keep as-is)

### Playbook 2: Re-Engagement Emails

**Pass 1 — How to Regain Client:**
```
Based on the complete email history, analyze WHY we lost this client and create a detailed re-engagement strategy:
1. What exactly caused them to leave? (be specific, quote emails)
2. What do they value most in a business partner?
3. What unmet needs might their current supplier have?
4. What timing signals suggest they might be open to reconnecting?
5. What would make them give us a second chance?
6. What should we absolutely avoid mentioning or doing?
```

**Pass 2 — Direct Re-engagement Email:**
```
Draft a professional re-engagement email to win back this client. Use these persuasion techniques:
- RECIPROCITY: Offer something valuable upfront (insight, free consultation, industry report)
- SOCIAL PROOF: Reference similar clients we've helped recently
- SCARCITY: Mention limited availability or exclusive offer
- AUTHORITY: Subtly demonstrate our expertise growth since we last worked together
- LIKING: Reference specific positive memories from our past collaboration
- COMMITMENT: Start with a small ask (meeting, not a contract)

The email must feel natural, not salesy. Reference specific details from our history. Keep it under 200 words.
```

**Pass 3 — AI Value Proposition Email:**
```
Draft a re-engagement email specifically focused on how AI can benefit THIS client's company. Based on their industry and business:
1. Research what their company does (from email context) and identify 3 specific AI use cases
2. Explain how AI could solve problems they mentioned in emails or improve their operations
3. Position us as the bridge between their business and AI transformation
4. Include a concrete example or mini case study relevant to their industry
5. Offer a free AI readiness assessment or demo

Make it compelling, specific to THEIR company, not generic. Use the Cialdini persuasion principles subtly.
```

**Pass 4 — Indirect Re-engagement Plan:**
```
Create a 2-3 step INDIRECT re-engagement plan. Instead of contacting them directly, propose indirect touchpoints:
1. STEP 1: Social/professional interaction (LinkedIn engagement, industry event, mutual connection intro, comment on their company news)
2. STEP 2: Value-first contact (share a relevant article, invite to a webinar, send an industry report that benefits them)
3. STEP 3: Warm re-engagement (after steps 1-2 create familiarity, make the direct ask)

For each step: specify WHAT to do, WHEN, through which CHANNEL, and WHAT to say. Use persuasion psychology: mere exposure effect, Ben Franklin effect, door-in-the-face, foot-in-the-door techniques.
```

**Pass 5 — Persuasion Techniques Toolkit:**
```
Based on all email history and the client's psychological profile, create a persuasion toolkit:
1. LOSS AVERSION: What are they losing by NOT working with us? Frame it.
2. ANCHORING: What high-value anchor should we set before making our actual offer?
3. CONTRAST PRINCIPLE: How to position our offer vs their current situation
4. STORYTELLING: Craft a compelling narrative about a similar client's transformation
5. OBJECTION HANDLING: List their likely objections and pre-written responses
6. URGENCY TRIGGERS: What legitimate urgency factors can we introduce?
7. NEGOTIATION PLAYBOOK: If they engage, what's our opening position, walkaway point, and BATNA?
```

### Playbook 3: Quote Optimization

**Pass 1 — Quote & Pricing History:**
```
Analyze ALL pricing/quoting history in these emails:
1. Create a TABLE of every quote, price, amount, date, and what it was for
2. Track how prices changed over time — did we increase, decrease, or stay flat?
3. What discounts did we give and why? Were they justified?
4. How did the client react to each price point? (accepted quickly = too low, pushed back hard = near their limit)
5. What's the client's apparent budget range based on their reactions?
6. Are there patterns in WHEN they accept quotes (end of quarter, start of year, etc.)?
```

**Pass 2 — Client Budget & Willingness:**
```
Build a psychological pricing profile for this client:
1. What's their budget authority? Who approves what amounts?
2. Are they price-sensitive or value-sensitive? Evidence from emails.
3. Do they comparison shop? Have they mentioned competitors or alternative quotes?
4. What non-price factors influence their decision (speed, quality, relationship, support)?
5. What's their pain threshold — at what price point do they push back vs walk away?
6. Do they negotiate every time or accept fair prices? What's their negotiation style?
7. WILLINGNESS TO PAY: Based on all evidence, what's the maximum they would pay for our typical service?
```

**Pass 3 — Value Framing Strategy:**
```
Create a value framing strategy to maximize what we can charge:
1. REFRAME FROM COST TO INVESTMENT: How to position our price as ROI, not expense
2. UNBUNDLING: Can we break our offering into components to increase perceived value?
3. TIERED OPTIONS: Design 3 pricing tiers (Good/Better/Best) with the middle one being our target
4. COMPARISON ANCHORING: What expensive alternatives exist that make our price seem reasonable?
5. VALUE QUANTIFICATION: Calculate the actual dollar value we deliver (time saved, revenue generated, problems avoided)
6. RISK REVERSAL: What guarantees or pilot offers reduce their perceived risk?
```

**Pass 4 — Anchoring & Negotiation Tactics:**
```
Design a negotiation playbook for the current discussion:
1. OPENING ANCHOR: What price should we start with? (typically 15-30% above target)
2. JUSTIFICATION: How to justify the anchor with value-based reasoning
3. CONCESSION STRATEGY: Plan 3 concessions we can make (each smaller than the last) — what to give and what to get in return
4. NIBBLING: Small add-ons we can include that cost us little but add perceived value
5. DEADLINE STRATEGY: How to create legitimate urgency without being pushy
6. WALK-AWAY POINT: Below what price is this project not worth doing?
7. BATNA: What's our best alternative if this deal doesn't happen?
8. IF THEY WANT TO NEGOTIATE: Script for handling "can you do better on price?" with reciprocity
```

**Pass 5 — Optimal Quote Recommendation:**
```
Based on all previous analysis, recommend the optimal quote:
1. RECOMMENDED PRICE: Exact amount with justification
2. FLOOR PRICE: Minimum acceptable amount
3. CEILING PRICE: Maximum we could reasonably charge
4. PRESENTATION FORMAT: How to structure the quote document for maximum impact
5. WHAT TO INCLUDE: Line items, value descriptions, terms that favor us
6. WHAT TO EXCLUDE: Items to hold back for negotiation leverage
7. TIMING: When to send the quote for best acceptance probability
8. FOLLOW-UP SCRIPT: What to say 3 days after sending if no response
9. MULTI-ROUND PLAN: If they push back, what's the next version of the quote?
```

### Playbook 4: Full Relationship Analysis (reuses passes from win_back: timeline, profiles, financial, pressure — no win-back pass)

---

## Verification

1. Page loads: dropdown populated with 4 playbooks
2. Select "Win Back Lost Client" → button shows "Deep Analysis" → runs 5 passes sequentially
3. Select "Quote Optimization" → runs 5 quote-focused passes
4. Results display in the deep analysis container with per-pass sections
5. "Load Deep Analysis" button on page reload restores all results
6. Deep analysis pass badges shown on saved responses
7. `php -l` passes on all modified PHP files


The file /root/.claude/plans/scalable-doodling-peach.md has been updated. All occurrences of '# 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' were successfully replaced with '# Deep Analysis Enhancement — Configurable Playbooks with Dropdown Selection

## Context

The current Deep Analysis feature has 5 hardcoded passes focused on one use case (lost client win-back). The user wants:
1. **Configurable analysis playbooks** — not hardcoded, ability to add/edit/manage passes
2. **Dropdown selection** — pick which playbook to run, then hit Deep Analysis
3. **New playbooks** for different business scenarios (win-back, quote optimization, persuasion)
4. **Results in a dedicated tab** — not just inline, a proper tabbed view for deep analysis results

## Architecture

### Replace hardcoded `DEEP_ANALYSIS_PASSES` with `DEEP_ANALYSIS_PLAYBOOKS`

A **playbook** is a named collection of passes. The dropdown selects a playbook, and the Deep Analysis button runs all passes in that playbook sequentially.

### File: `public/thread_view.php` (all changes in one file)

---

## Step 1: Define Playbooks Constant

**Replace** the current `DEEP_ANALYSIS_PASSES` array (lines 1567-1598) with `DEEP_ANALYSIS_PLAYBOOKS` object:

```javascript
const DEEP_ANALYSIS_PLAYBOOKS = {
    // ---- LOST CLIENT WIN-BACK ----
    'win_back': {
        name: 'Win Back Lost Client',
        description: 'Full relationship analysis + win-back strategy',
        passes: [
            { id: 'timeline', name: 'Business Relationship Timeline', agent: 'general', prompt: '...' },
            { id: 'pressure', name: 'Pressure & Manipulation Detection', agent: 'deception_detector', prompt: '...' },
            { id: 'profiles', name: 'Psychological Profiles', agent: 'contact_profiler', prompt: '...' },
            { id: 'financial', name: 'Financial & Business Patterns', agent: 'quote_analyst', prompt: '...' },
            { id: 'winback', name: 'Win-Back Strategy', agent: 'general', prompt: '...' }
        ]
    },

    // ---- RE-ENGAGEMENT EMAIL DRAFTS ----
    'regain_email': {
        name: 'Re-Engagement Emails',
        description: 'Draft re-engagement emails with persuasion techniques',
        passes: [
            { id: 'regain_analysis', name: 'How to Regain Client', agent: 'general', prompt: '...' },
            { id: 'regain_email_direct', name: 'Direct Re-engagement Email', agent: 'email_composer', prompt: '...' },
            { id: 'regain_email_ai', name: 'AI Value Proposition Email', agent: 'email_composer', prompt: '...' },
            { id: 'indirect_steps', name: 'Indirect Re-engagement Plan', agent: 'general', prompt: '...' },
            { id: 'persuasion_toolkit', name: 'Persuasion Techniques', agent: 'general', prompt: '...' }
        ]
    },

    // ---- QUOTE OPTIMIZATION ----
    'quote_optimize': {
        name: 'Quote Optimization',
        description: 'Maximize quote value for current client discussions',
        passes: [
            { id: 'quote_history', name: 'Quote & Pricing History', agent: 'quote_analyst', prompt: '...' },
            { id: 'client_budget', name: 'Client Budget & Willingness', agent: 'contact_profiler', prompt: '...' },
            { id: 'value_framing', name: 'Value Framing Strategy', agent: 'general', prompt: '...' },
            { id: 'anchoring', name: 'Anchoring & Negotiation Tactics', agent: 'general', prompt: '...' },
            { id: 'quote_draft', name: 'Optimal Quote Recommendation', agent: 'quote_analyst', prompt: '...' }
        ]
    },

    // ---- FULL RELATIONSHIP ANALYSIS ----
    'relationship': {
        name: 'Full Relationship Analysis',
        description: 'Comprehensive relationship health check',
        passes: [
            { id: 'timeline', name: 'Business Relationship Timeline', agent: 'general', prompt: '...' },
            { id: 'profiles', name: 'Psychological Profiles', agent: 'contact_profiler', prompt: '...' },
            { id: 'financial', name: 'Financial & Business Patterns', agent: 'quote_analyst', prompt: '...' },
            { id: 'pressure', name: 'Pressure & Manipulation Detection', agent: 'deception_detector', prompt: '...' }
        ]
    }
};
```

---

## Step 2: Playbook Dropdown + Deep Analysis Button UI

**Replace** the current button area (lines 1437-1445) with dropdown + button:

```html
<div style="display:flex;gap:8px;">
    <button class="btn btn-primary" id="submit-ai-btn" onclick="submitToAI()" style="flex:1;">
        Send to AI
    </button>
</div>
<div style="display:flex;gap:8px;margin-top:8px;">
    <select id="deep-analysis-playbook" style="flex:2;padding:8px;border-radius:6px;border:1px solid #ccc;font-size:0.9em;">
        <!-- Populated from DEEP_ANALYSIS_PLAYBOOKS by JS -->
    </select>
    <button class="btn" id="deep-analysis-btn" onclick="runDeepAnalysis()" style="flex:1;background:linear-gradient(135deg,#8b5cf6,#3b82f6);color:white;font-weight:600;">
        Deep Analysis
    </button>
</div>
```

Add JS to populate dropdown on page load:
```javascript
function initPlaybookDropdown() {
    var sel = document.getElementById('deep-analysis-playbook');
    for (var key in DEEP_ANALYSIS_PLAYBOOKS) {
        var pb = DEEP_ANALYSIS_PLAYBOOKS[key];
        var opt = document.createElement('option');
        opt.value = key;
        opt.textContent = pb.name + ' (' + pb.passes.length + ' passes)';
        opt.title = pb.description;
        sel.appendChild(opt);
    }
}
```

---

## Step 3: Update `runDeepAnalysis()` to Use Selected Playbook

Change the function to read the selected playbook from the dropdown instead of using the hardcoded `DEEP_ANALYSIS_PASSES`:

```javascript
async function runDeepAnalysis() {
    var playbookKey = document.getElementById('deep-analysis-playbook').value;
    var playbook = DEEP_ANALYSIS_PLAYBOOKS[playbookKey];
    if (!playbook) { alert('Select a playbook'); return; }
    var passes = playbook.passes;
    // ... rest of function uses `passes` instead of `DEEP_ANALYSIS_PASSES`
    // Also save `playbookKey` and `playbook.name` to MongoDB
}
```

In the `saveAIResponse` call inside the loop, add `deep_analysis_playbook: playbookKey`.

---

## Step 4: Deep Analysis Results as Dedicated Tab

Add a new tab to the thread view for deep analysis results, sitting alongside the existing email timeline and AI query panel.

**Add tab button** in the thread view navigation area (where the main thread layout is):

After the existing `ai-response-container`, add a container for the tabbed deep analysis results view. This uses the same `deep-analysis-container` div but enhances it with tabs for each playbook run.

The `loadDeepAnalysis()` function already handles this — just make the container more prominent and keep it visible as a section rather than a popup.

---

## Step 5: Save Playbook to MongoDB

Update `save_ai_response` API (api.php line ~4802) to persist new field:
- `deep_analysis_playbook` — the playbook key (e.g., 'win_back', 'quote_optimize')

Update `load_deep_analysis` API to also return the playbook key.

---

## Full Playbook Prompts

### Playbook 1: Win Back Lost Client (existing 5 passes — keep as-is)

### Playbook 2: Re-Engagement Emails

**Pass 1 — How to Regain Client:**
```
Based on the complete email history, analyze WHY we lost this client and create a detailed re-engagement strategy:
1. What exactly caused them to leave? (be specific, quote emails)
2. What do they value most in a business partner?
3. What unmet needs might their current supplier have?
4. What timing signals suggest they might be open to reconnecting?
5. What would make them give us a second chance?
6. What should we absolutely avoid mentioning or doing?
```

**Pass 2 — Direct Re-engagement Email:**
```
Draft a professional re-engagement email to win back this client. Use these persuasion techniques:
- RECIPROCITY: Offer something valuable upfront (insight, free consultation, industry report)
- SOCIAL PROOF: Reference similar clients we've helped recently
- SCARCITY: Mention limited availability or exclusive offer
- AUTHORITY: Subtly demonstrate our expertise growth since we last worked together
- LIKING: Reference specific positive memories from our past collaboration
- COMMITMENT: Start with a small ask (meeting, not a contract)

The email must feel natural, not salesy. Reference specific details from our history. Keep it under 200 words.
```

**Pass 3 — AI Value Proposition Email:**
```
Draft a re-engagement email specifically focused on how AI can benefit THIS client's company. Based on their industry and business:
1. Research what their company does (from email context) and identify 3 specific AI use cases
2. Explain how AI could solve problems they mentioned in emails or improve their operations
3. Position us as the bridge between their business and AI transformation
4. Include a concrete example or mini case study relevant to their industry
5. Offer a free AI readiness assessment or demo

Make it compelling, specific to THEIR company, not generic. Use the Cialdini persuasion principles subtly.
```

**Pass 4 — Indirect Re-engagement Plan:**
```
Create a 2-3 step INDIRECT re-engagement plan. Instead of contacting them directly, propose indirect touchpoints:
1. STEP 1: Social/professional interaction (LinkedIn engagement, industry event, mutual connection intro, comment on their company news)
2. STEP 2: Value-first contact (share a relevant article, invite to a webinar, send an industry report that benefits them)
3. STEP 3: Warm re-engagement (after steps 1-2 create familiarity, make the direct ask)

For each step: specify WHAT to do, WHEN, through which CHANNEL, and WHAT to say. Use persuasion psychology: mere exposure effect, Ben Franklin effect, door-in-the-face, foot-in-the-door techniques.
```

**Pass 5 — Persuasion Techniques Toolkit:**
```
Based on all email history and the client's psychological profile, create a persuasion toolkit:
1. LOSS AVERSION: What are they losing by NOT working with us? Frame it.
2. ANCHORING: What high-value anchor should we set before making our actual offer?
3. CONTRAST PRINCIPLE: How to position our offer vs their current situation
4. STORYTELLING: Craft a compelling narrative about a similar client's transformation
5. OBJECTION HANDLING: List their likely objections and pre-written responses
6. URGENCY TRIGGERS: What legitimate urgency factors can we introduce?
7. NEGOTIATION PLAYBOOK: If they engage, what's our opening position, walkaway point, and BATNA?
```

### Playbook 3: Quote Optimization

**Pass 1 — Quote & Pricing History:**
```
Analyze ALL pricing/quoting history in these emails:
1. Create a TABLE of every quote, price, amount, date, and what it was for
2. Track how prices changed over time — did we increase, decrease, or stay flat?
3. What discounts did we give and why? Were they justified?
4. How did the client react to each price point? (accepted quickly = too low, pushed back hard = near their limit)
5. What's the client's apparent budget range based on their reactions?
6. Are there patterns in WHEN they accept quotes (end of quarter, start of year, etc.)?
```

**Pass 2 — Client Budget & Willingness:**
```
Build a psychological pricing profile for this client:
1. What's their budget authority? Who approves what amounts?
2. Are they price-sensitive or value-sensitive? Evidence from emails.
3. Do they comparison shop? Have they mentioned competitors or alternative quotes?
4. What non-price factors influence their decision (speed, quality, relationship, support)?
5. What's their pain threshold — at what price point do they push back vs walk away?
6. Do they negotiate every time or accept fair prices? What's their negotiation style?
7. WILLINGNESS TO PAY: Based on all evidence, what's the maximum they would pay for our typical service?
```

**Pass 3 — Value Framing Strategy:**
```
Create a value framing strategy to maximize what we can charge:
1. REFRAME FROM COST TO INVESTMENT: How to position our price as ROI, not expense
2. UNBUNDLING: Can we break our offering into components to increase perceived value?
3. TIERED OPTIONS: Design 3 pricing tiers (Good/Better/Best) with the middle one being our target
4. COMPARISON ANCHORING: What expensive alternatives exist that make our price seem reasonable?
5. VALUE QUANTIFICATION: Calculate the actual dollar value we deliver (time saved, revenue generated, problems avoided)
6. RISK REVERSAL: What guarantees or pilot offers reduce their perceived risk?
```

**Pass 4 — Anchoring & Negotiation Tactics:**
```
Design a negotiation playbook for the current discussion:
1. OPENING ANCHOR: What price should we start with? (typically 15-30% above target)
2. JUSTIFICATION: How to justify the anchor with value-based reasoning
3. CONCESSION STRATEGY: Plan 3 concessions we can make (each smaller than the last) — what to give and what to get in return
4. NIBBLING: Small add-ons we can include that cost us little but add perceived value
5. DEADLINE STRATEGY: How to create legitimate urgency without being pushy
6. WALK-AWAY POINT: Below what price is this project not worth doing?
7. BATNA: What's our best alternative if this deal doesn't happen?
8. IF THEY WANT TO NEGOTIATE: Script for handling "can you do better on price?" with reciprocity
```

**Pass 5 — Optimal Quote Recommendation:**
```
Based on all previous analysis, recommend the optimal quote:
1. RECOMMENDED PRICE: Exact amount with justification
2. FLOOR PRICE: Minimum acceptable amount
3. CEILING PRICE: Maximum we could reasonably charge
4. PRESENTATION FORMAT: How to structure the quote document for maximum impact
5. WHAT TO INCLUDE: Line items, value descriptions, terms that favor us
6. WHAT TO EXCLUDE: Items to hold back for negotiation leverage
7. TIMING: When to send the quote for best acceptance probability
8. FOLLOW-UP SCRIPT: What to say 3 days after sending if no response
9. MULTI-ROUND PLAN: If they push back, what's the next version of the quote?
```

### Playbook 4: Full Relationship Analysis (reuses passes from win_back: timeline, profiles, financial, pressure — no win-back pass)

---

## Verification

1. Page loads: dropdown populated with 4 playbooks
2. Select "Win Back Lost Client" → button shows "Deep Analysis" → runs 5 passes sequentially
3. Select "Quote Optimization" → runs 5 quote-focused passes
4. Results display in the deep analysis container with per-pass sections
5. "Load Deep Analysis" button on page reload restores all results
6. Deep analysis pass badges shown on saved responses
7. `php -l` passes on all modified PHP files'.

-# Delta Engineering Deep Analysis — Prompt Strategy Guide

+# Deep Analysis Enhancement — Configurable Playbooks with Dropdown Selection

-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.

+The current Deep Analysis feature has 5 hardcoded passes focused on one use case (lost client win-back). The user wants:

+1. **Configurable analysis playbooks** — not hardcoded, ability to add/edit/manage passes

+2. **Dropdown selection** — pick which playbook to run, then hit Deep Analysis

+3. **New playbooks** for different business scenarios (win-back, quote optimization, persuasion)

+4. **Results in a dedicated tab** — not just inline, a proper tabbed view for deep analysis results

-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`)

+### Replace hardcoded `DEEP_ANALYSIS_PASSES` with `DEEP_ANALYSIS_PLAYBOOKS`

+A **playbook** is a named collection of passes. The dropdown selects a playbook, and the Deep Analysis button runs all passes in that playbook sequentially.

+### File: `public/thread_view.php` (all changes in one file)

-## Recommended Analysis Strategy: 5-Round Deep Dive

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

+**Replace** the current `DEEP_ANALYSIS_PASSES` array (lines 1567-1598) with `DEEP_ANALYSIS_PLAYBOOKS` object:

-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

+        description: 'Full relationship analysis + win-back strategy',

+            { id: 'timeline', name: 'Business Relationship Timeline', agent: 'general', prompt: '...' },

+            { id: 'pressure', name: 'Pressure & Manipulation Detection', agent: 'deception_detector', prompt: '...' },

+            { id: 'profiles', name: 'Psychological Profiles', agent: 'contact_profiler', prompt: '...' },

+            { id: 'financial', name: 'Financial & Business Patterns', agent: 'quote_analyst', prompt: '...' },

+            { id: 'winback', name: 'Win-Back Strategy', agent: 'general', prompt: '...' }

-**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

+    // ---- RE-ENGAGEMENT EMAIL DRAFTS ----

+        description: 'Draft re-engagement emails with persuasion techniques',

+            { id: 'regain_analysis', name: 'How to Regain Client', agent: 'general', prompt: '...' },

+            { id: 'regain_email_direct', name: 'Direct Re-engagement Email', agent: 'email_composer', prompt: '...' },

+            { id: 'regain_email_ai', name: 'AI Value Proposition Email', agent: 'email_composer', prompt: '...' },

+            { id: 'indirect_steps', name: 'Indirect Re-engagement Plan', agent: 'general', prompt: '...' },

+            { id: 'persuasion_toolkit', name: 'Persuasion Techniques', agent: 'general', prompt: '...' }

+        description: 'Maximize quote value for current client discussions',

+            { id: 'quote_history', name: 'Quote & Pricing History', agent: 'quote_analyst', prompt: '...' },

+            { id: 'client_budget', name: 'Client Budget & Willingness', agent: 'contact_profiler', prompt: '...' },

+            { id: 'value_framing', name: 'Value Framing Strategy', agent: 'general', prompt: '...' },

+            { id: 'anchoring', name: 'Anchoring & Negotiation Tactics', agent: 'general', prompt: '...' },

+            { id: 'quote_draft', name: 'Optimal Quote Recommendation', agent: 'quote_analyst', prompt: '...' }

+    // ---- FULL RELATIONSHIP ANALYSIS ----

+        name: 'Full Relationship Analysis',

+        description: 'Comprehensive relationship health check',

+            { id: 'timeline', name: 'Business Relationship Timeline', agent: 'general', prompt: '...' },

+            { id: 'profiles', name: 'Psychological Profiles', agent: 'contact_profiler', prompt: '...' },

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

+## Step 2: Playbook Dropdown + Deep Analysis Button UI

-#### 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:

+**Replace** the current button area (lines 1437-1445) with dropdown + button:

-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?

+    <button class="btn btn-primary" id="submit-ai-btn" onclick="submitToAI()" style="flex:1;">

+    <select id="deep-analysis-playbook" style="flex:2;padding:8px;border-radius:6px;border:1px solid #ccc;font-size:0.9em;">

+        <!-- Populated from DEEP_ANALYSIS_PLAYBOOKS by JS -->

+    <button class="btn" id="deep-analysis-btn" onclick="runDeepAnalysis()" style="flex:1;background:linear-gradient(135deg,#8b5cf6,#3b82f6);color:white;font-weight:600;">

-#### Pass 2: Deception Agent — Pressure & Manipulation Detection

+Add JS to populate dropdown on page load:

+    var sel = document.getElementById('deep-analysis-playbook');

+    for (var key in DEEP_ANALYSIS_PLAYBOOKS) {

+        var pb = DEEP_ANALYSIS_PLAYBOOKS[key];

+        var opt = document.createElement('option');

+        opt.textContent = pb.name + ' (' + pb.passes.length + ' passes)';

-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.

+## Step 3: Update `runDeepAnalysis()` to Use Selected Playbook

-#### Pass 3: Profiler Agent — Psychological Profiles of Key People

+Change the function to read the selected playbook from the dropdown instead of using the hardcoded `DEEP_ANALYSIS_PASSES`:

+    var playbookKey = document.getElementById('deep-analysis-playbook').value;

+    var playbook = DEEP_ANALYSIS_PLAYBOOKS[playbookKey];

+    if (!playbook) { alert('Select a playbook'); return; }

+    // ... rest of function uses `passes` instead of `DEEP_ANALYSIS_PASSES`

+    // Also save `playbookKey` and `playbook.name` to MongoDB

-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?

+In the `saveAIResponse` call inside the loop, add `deep_analysis_playbook: playbookKey`.

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

+## Step 4: Deep Analysis Results as Dedicated Tab

+Add a new tab to the thread view for deep analysis results, sitting alongside the existing email timeline and AI query panel.

+**Add tab button** in the thread view navigation area (where the main thread layout is):

+After the existing `ai-response-container`, add a container for the tabbed deep analysis results view. This uses the same `deep-analysis-container` div but enhances it with tabs for each playbook run.

+The `loadDeepAnalysis()` function already handles this — just make the container more prominent and keep it visible as a section rather than a popup.

+Update `save_ai_response` API (api.php line ~4802) to persist new field:

+- `deep_analysis_playbook` — the playbook key (e.g., 'win_back', 'quote_optimize')

+Update `load_deep_analysis` API to also return the playbook key.

+### Playbook 1: Win Back Lost Client (existing 5 passes — keep as-is)

+Based on the complete email history, analyze WHY we lost this client and create a detailed re-engagement strategy:

+1. What exactly caused them to leave? (be specific, quote emails)

+2. What do they value most in a business partner?

+3. What unmet needs might their current supplier have?

+4. What timing signals suggest they might be open to reconnecting?

+5. What would make them give us a second chance?

+6. What should we absolutely avoid mentioning or doing?

-#### Pass 4: Quote Analyst — Financial & Business Pattern Analysis

+**Pass 2 — Direct Re-engagement Email:**

-Analyze the complete financial and business history with Delta Engineering:

+Draft a professional re-engagement email to win back this client. Use these persuasion techniques:

+- RECIPROCITY: Offer something valuable upfront (insight, free consultation, industry report)

+- SOCIAL PROOF: Reference similar clients we've helped recently

+- SCARCITY: Mention limited availability or exclusive offer

+- AUTHORITY: Subtly demonstrate our expertise growth since we last worked together

+- LIKING: Reference specific positive memories from our past collaboration

+- COMMITMENT: Start with a small ask (meeting, not a contract)

-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?

+The email must feel natural, not salesy. Reference specific details from our history. Keep it under 200 words.

-#### Pass 5: General Agent — Win-Back Strategy (final synthesis)

+**Pass 3 — AI Value Proposition Email:**

-Based on everything in these emails, create a CONCRETE win-back strategy for Delta Engineering:

+Draft a re-engagement email specifically focused on how AI can benefit THIS client's company. Based on their industry and business:

+1. Research what their company does (from email context) and identify 3 specific AI use cases

+2. Explain how AI could solve problems they mentioned in emails or improve their operations

+3. Position us as the bridge between their business and AI transformation

+4. Include a concrete example or mini case study relevant to their industry

+5. Offer a free AI readiness assessment or demo

-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?

-   - 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?

+Make it compelling, specific to THEIR company, not generic. Use the Cialdini persuasion principles subtly.

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

+**Pass 4 — Indirect Re-engagement Plan:**

+Create a 2-3 step INDIRECT re-engagement plan. Instead of contacting them directly, propose indirect touchpoints:

+1. STEP 1: Social/professional interaction (LinkedIn engagement, industry event, mutual connection intro, comment on their company news)

+2. STEP 2: Value-first contact (share a relevant article, invite to a webinar, send an industry report that benefits them)

+3. STEP 3: Warm re-engagement (after steps 1-2 create familiarity, make the direct ask)

+For each step: specify WHAT to do, WHEN, through which CHANNEL, and WHAT to say. Use persuasion psychology: mere exposure effect, Ben Franklin effect, door-in-the-face, foot-in-the-door techniques.

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

+**Pass 5 — Persuasion Techniques Toolkit:**

+Based on all email history and the client's psychological profile, create a persuasion toolkit:

+1. LOSS AVERSION: What are they losing by NOT working with us? Frame it.

+2. ANCHORING: What high-value anchor should we set before making our actual offer?

+3. CONTRAST PRINCIPLE: How to position our offer vs their current situation

+4. STORYTELLING: Craft a compelling narrative about a similar client's transformation

+5. OBJECTION HANDLING: List their likely objections and pre-written responses

+6. URGENCY TRIGGERS: What legitimate urgency factors can we introduce?

+7. NEGOTIATION PLAYBOOK: If they engage, what's our opening position, walkaway point, and BATNA?

-- **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

+Analyze ALL pricing/quoting history in these emails:

+1. Create a TABLE of every quote, price, amount, date, and what it was for

+2. Track how prices changed over time — did we increase, decrease, or stay flat?

+3. What discounts did we give and why? Were they justified?

+4. How did the client react to each price point? (accepted quickly = too low, pushed back hard = near their limit)

+5. What's the client's apparent budget range based on their reactions?

+6. Are there patterns in WHEN they accept quotes (end of quarter, start of year, etc.)?

-### Step 4: Generate Relationship Chart

+**Pass 2 — Client Budget & Willingness:**

+Build a psychological pricing profile for this client:

+1. What's their budget authority? Who approves what amounts?

+2. Are they price-sensitive or value-sensitive? Evidence from emails.

+3. Do they comparison shop? Have they mentioned competitors or alternative quotes?

+4. What non-price factors influence their decision (speed, quality, relationship, support)?

+5. What's their pain threshold — at what price point do they push back vs walk away?

+6. Do they negotiate every time or accept fair prices? What's their negotiation style?

+7. WILLINGNESS TO PAY: Based on all evidence, what's the maximum they would pay for our typical service?

-After the Pass 1 (timeline) analysis, click **"Generate Chart"** to create an interactive D3.js visualization of:

+Create a value framing strategy to maximize what we can charge:

+1. REFRAME FROM COST TO INVESTMENT: How to position our price as ROI, not expense

+2. UNBUNDLING: Can we break our offering into components to increase perceived value?

+3. TIERED OPTIONS: Design 3 pricing tiers (Good/Better/Best) with the middle one being our target

+4. COMPARISON ANCHORING: What expensive alternatives exist that make our price seem reasonable?

+5. VALUE QUANTIFICATION: Calculate the actual dollar value we deliver (time saved, revenue generated, problems avoided)

+6. RISK REVERSAL: What guarantees or pilot offers reduce their perceived risk?

+**Pass 4 — Anchoring & Negotiation Tactics:**

+Design a negotiation playbook for the current discussion:

+1. OPENING ANCHOR: What price should we start with? (typically 15-30% above target)

+2. JUSTIFICATION: How to justify the anchor with value-based reasoning

+3. CONCESSION STRATEGY: Plan 3 concessions we can make (each smaller than the last) — what to give and what to get in return

+4. NIBBLING: Small add-ons we can include that cost us little but add perceived value

+5. DEADLINE STRATEGY: How to create legitimate urgency without being pushy

+6. WALK-AWAY POINT: Below what price is this project not worth doing?

+7. BATNA: What's our best alternative if this deal doesn't happen?

+8. IF THEY WANT TO NEGOTIATE: Script for handling "can you do better on price?" with reciprocity

+**Pass 5 — Optimal Quote Recommendation:**

+Based on all previous analysis, recommend the optimal quote:

+1. RECOMMENDED PRICE: Exact amount with justification

+2. FLOOR PRICE: Minimum acceptable amount

+3. CEILING PRICE: Maximum we could reasonably charge

+4. PRESENTATION FORMAT: How to structure the quote document for maximum impact

+5. WHAT TO INCLUDE: Line items, value descriptions, terms that favor us

+6. WHAT TO EXCLUDE: Items to hold back for negotiation leverage

+7. TIMING: When to send the quote for best acceptance probability

+8. FOLLOW-UP SCRIPT: What to say 3 days after sending if no response

+9. MULTI-ROUND PLAN: If they push back, what's the next version of the quote?

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

+### Playbook 4: Full Relationship Analysis (reuses passes from win_back: timeline, profiles, financial, pressure — no win-back pass)

-| 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 |

-| 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

+1. Page loads: dropdown populated with 4 playbooks

+2. Select "Win Back Lost Client" → button shows "Deep Analysis" → runs 5 passes sequentially

+3. Select "Quote Optimization" → runs 5 quote-focused passes

+4. Results display in the deep analysis container with per-pass sections

+5. "Load Deep Analysis" button on page reload restores all results

+6. Deep analysis pass badges shown on saved responses

+7. `php -l` passes on all modified PHP files


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