Signal-to-Trust GTM orchestrator for complete outbound campaign management. Use whenever the user wants to create signal-based outreach campaigns, generate multi-channel assets (email/LinkedIn/WhatsApp/VSL), analyze campaign performance, calculate Digital Silence Index, build lead magnets, adapt messaging for geographic markets (MENA/US/EU), or deploy to GHL/Instantly/HeyReach. Triggers include "launch campaign", "generate weekly assets", "analyze performance", "signal-based outreach", "wedge generation", "silence type", "DSI calculator", or any mention of the Signal-to-Trust framework. This skill orchestrates 10 specialized sub-skills to handle the complete Q→M→W→D campaign hierarchy.
This is the master orchestrator for the complete Signal-to-Trust GTM framework. It manages the hierarchical campaign flow from Quarterly → Monthly → Weekly → Daily and coordinates 10 specialized sub-skills to produce complete multi-channel outbound campaigns.
Core Philosophy: "Your funnel is not broken. It is silent."
Every campaign is built on observed signals (not assumptions), wedges derived from those signals (not generic value props), and assets optimized for specific silence types.
This skill orchestrates EVERYTHING in the Signal-to-Trust framework:
Before executing ANY campaign work, run this questionnaire to determine execution path. Present as multiple-choice with context-aware defaults.
What are you trying to do?
Execution Logic:
Which ICP are we targeting?
This determines:
What quarterly outcome/feature are we hammering?
Examples:
This becomes the North Star for all downstream (M→W→D) derivation.
How should we narrow from Quarterly → Monthly?
Examples:
Do you have signals collected, or should we gather them?
If B selected:
How should we derive 3 weekly wedges from monthly theme?
Critical Rule: Weekly wedges are DIFFERENT (not A/B test variants), but derivative from monthly theme.
Example (Monthly: "Instagram Follower Leakage"):
What lead magnet should we create for this monthly campaign?
Tied to silence types:
What VSL(s) should we create?
Default recommendation: C (both) for first month, B (weekly only) for subsequent months.
Which channels should we use?
Weekly output volumes:
Which geographic market(s)?
This triggers culture-adapter sub-skill using Erin Meyer's Culture Map framework.
How should we deploy assets?
If B or C selected:
Should we set up self-improvement loop?
If A selected:
Based on questionnaire answers, call sub-skills in this sequence:
1. campaign-strategist
Input: Q2-Q4 answers (ICP, Quarterly feature, Monthly narrowing, Weekly strategy)
Output: Campaign brief with Q→M→W→D alignment
2. signal-detector
Input: Q5 answer (signal collection), ICP Fit criteria
Output: Validated prospect list (Fit PASS only), signal taxonomy
3. wedge-generator
Input: Q6 answer (weekly strategy), signal data, monthly theme
Output: 3 weekly wedges (one-sentence each)
4. asset-factory
Input: 3 weekly wedges, Q9 (channel mix), campaign brief
Output: 6 emails, 4 LinkedIn, 3 WhatsApp, 3 social posts
5. lead-magnet-builder (if Q7 ≠ None)
Input: Q7 (lead magnet type), monthly wedge, silence type
Output: Interactive lead magnet (HTML/React artifact)
6. dsi-calculator
Input: Landing page content, objection patterns
Output: DSI score (0-100), Q&A section for objection silence
7. landing-page-architect
Input: Q8 (VSL strategy), monthly wedge, 3 weekly wedges, lead magnet
Output: Master VSL landing page with UTM variants
8. culture-adapter (if Q10 = multi-market)
Input: All assets, geographic markets
Output: Culturally adapted message variants
9. integration-orchestrator (if Q11 = B or C)
Input: All assets, tech stack selection
Output: GHL workflows, Instantly campaigns, HeyReach sequences
10. performance-analyzer (if Q12 = A)
Input: Campaign setup
Output: Tracking dashboard setup, baseline metrics
1. performance-analyzer
Input: Last week's metrics
Output: Winning wedge, performance insights
2. wedge-generator
Input: Winning wedge, A/B variant request
Output: Refined wedge + variants
3. asset-factory
Input: This week's wedge, channel mix
Output: Weekly asset bundle
4. culture-adapter
Input: Assets, geographic market
Output: Adapted variants
5. integration-orchestrator (if Q11 = B or C)
Input: Weekly assets
Output: Updated campaign deployments
1. performance-analyzer
Input: Campaign metrics (reply rate, meeting rate, objections)
Output: Performance report with insights
2. campaign-strategist
Input: Performance insights
Output: Pivot recommendation (double down, shift wedge, new signal)
3. wedge-generator (if pivot = new wedge)
Input: New signal direction
Output: Alternative wedge options
Route directly to specific sub-skill:
CRITICAL: Before ANY execution, validate these non-negotiable rules:
If prospect fails ICP Fit criteria, STOP immediately. Do not generate assets.
Validation:
signal-detector checks:
- Company size in range?
- Geography match?
- Industry/vertical match?
- Revenue band match?
If ANY = NO → Exclude from campaign
Signals older than 90 days are stale. Skip them.
Validation:
signal-detector checks signal timestamp
If (today - signal_date) > 90 days → Flag as STALE
If you can't articulate the signal clearly in one sentence, it's not actionable.
Validation:
wedge-generator attempts one-sentence wedge
If wedge requires 2+ sentences or hedging → REJECT signal
When both Trust AND Intent signals present, Intent takes priority.
Logic:
if has_intent_signal AND has_trust_signal:
use_intent_signal()
Example:
1. Campaign Brief (Markdown)
# [ICP] - [Monthly Theme] Campaign
## Quarterly Alignment
**Feature**: [Quarterly hammering outcome]
**Monthly Focus**: [How we narrowed it]
## Signal Intelligence
- Total prospects: [N]
- Fit PASS: [N]
- Primary signal type: [Trust/Intent]
- Signal subtype: [specific subtype]
## Weekly Wedge Strategy
- **Week 1**: [Wedge sentence]
- **Week 2**: [Wedge sentence]
- **Week 3**: [Wedge sentence]
## Lead Magnet
**Type**: [DSI Scorecard / Signal Library / etc.]
**Silence addressed**: [Positioning / Proof / Objection / etc.]
## Channel Mix
- Email: [Y/N]
- LinkedIn: [Y/N]
- WhatsApp: [Y/N]
## Geographic Markets
- Primary: [MENA/US/EU]
- Adaptation: [Culture Map dimensions]
## Integration Status
- GHL: [Deployed/Pending/Manual]
- Instantly: [Deployed/Pending/Manual]
- HeyReach: [Deployed/Pending/Manual]
2. Asset Folder Structure
campaign-assets/
├── week-1/
│ ├── email-sequence-A.md (3 emails)
│ ├── email-sequence-B.md (3 emails)
│ ├── linkedin-sequence-A.md (2 messages)
│ ├── linkedin-sequence-B.md (2 messages)
│ ├── whatsapp-messages-ABC.md (3 variants)
│ ├── vsl-script-week1.md
│ └── social-posts.md (3 posts)
├── week-2/
│ └── [same structure]
├── week-3/
│ └── [same structure]
├── lead-magnet/
│ └── [interactive artifact or guide]
├── landing-page/
│ └── master-vsl-page.html (with UTM variants)
└── integration/
├── ghl-workflows.json
├── instantly-campaign-config.json
└── heyreach-sequences.csv
3. GHL Deployment Configs (JSON)
{
"campaign_name": "[ICP] - [Monthly Theme]",
"workflows": [
{
"name": "Week 1 Email Sequence A",
"trigger": "Tag added: week1_sequence_a",
"steps": [
{
"type": "email",
"delay_days": 0,
"subject": "{{custom_field.wedge_subject}}",
"body": "...",
"merge_fields": ["contact.first_name", "custom_field.signal_data"]
}
]
}
],
"custom_fields": [
{"name": "signal_type", "type": "text"},
{"name": "wedge_variant", "type": "dropdown", "options": ["A", "B"]},
{"name": "signal_data", "type": "text"}
]
}
Weekly Asset Bundle (Markdown + Files)
# Week [N] Assets - [Campaign Name]
## Performance Context (from last week)
- Winning wedge: [Wedge 1/2/3]
- Reply rate: [X%]
- Meeting rate: [X%]
- Key insight: [pattern observed]
## This Week's Strategy
**Wedge**: [Refined wedge sentence]
**Variant strategy**: [A/B psychology difference]
## Assets
- [Links to email/LinkedIn/WhatsApp sequences]
- [Link to updated VSL script if applicable]
- [Link to social posts]
## Deployment Status
- GHL: [Updated/Pending]
- Instantly: [Updated/Pending]
- HeyReach: [Updated/Pending]
Performance Report (Markdown)
# Campaign Performance Analysis
## Campaign: [Name]
## Period: [Date range]
## Metrics Summary
| Metric | Week 1 | Week 2 | Week 3 | Trend |
|--------|--------|--------|--------|-------|
| Reply Rate | X% | X% | X% | ↑/↓/→ |
| Meeting Rate | X% | X% | X% | ↑/↓/→ |
| Objection Rate | X% | X% | X% | ↑/↓/→ |
## Wedge Performance
- **Wedge 1**: [Reply X%, Meeting X%]
- **Wedge 2**: [Reply X%, Meeting X%] ← **WINNER**
- **Wedge 3**: [Reply X%, Meeting X%]
## Pattern Discovery
**New signals observed**:
1. "[8 responders mentioned 'sales team scattered']" → Potential new Intent signal
2. "[5 prospects asked about geographic coverage]" → Potential Friction silence
## Recommendations
### Option A: Double Down (Recommended)
- **Action**: Continue Wedge 2, create A/B variants
- **Rationale**: 13.7% reply rate is 2.1x benchmark
- **Assets needed**: 2 new email variants
### Option B: Pivot to New Signal
- **Action**: Test "geographic distribution" signal
- **New wedge**: "[One-sentence wedge for new signal]"
- **Risk**: Restart momentum
### Option C: Hybrid
- **Action**: 70% Wedge 2, 30% new signal test
- **Rationale**: Maintain performance while exploring
## Template Evolution
**Winning patterns**:
- Subject lines with "[specific data point]" → 1.8x open rate
- P.S. with "[micro-commitment]" → 2.3x reply rate
**Updated battle cards**:
- [Link to updated templates in battle-cards/ folder]
The meta skill references these knowledge files (located in references/):
Complete taxonomy: Fit → Trigger → Signal Type (Trust/Intent) → Signal Subtype → Wedge
Critical distinctions:
From your PDF, the complete taxonomy:
Each silence type maps to specific DSI scoring dimensions and lead magnet types.
ICP-specific wedge templates with examples:
Template: "[Observed Signal] is costing you [Specific Outcome]. Here's the [One-Line Solution]."
Examples by ICP:
Non-negotiable enforcement:
Erin Meyer's 8 dimensions applied to GTM messaging:
MENA Adaptation (Trust-first):
US Adaptation (Intent-first):
Germany Adaptation (Data-first):
Proven templates from previous campaigns:
email-subject-lines-winners.md (top-performing subject lines by ICP)linkedin-opener-variants.md (connection request + first message combos)whatsapp-psychology-abc.md (Pattern Interrupt / Problem Amplification / Peer Reference)vsl-hooks-library.md (tested opening hooks by silence type)objection-handling-scripts.md (responses to common objections by ICP)If Question 12 = "Yes, track performance", the skill evolves over time:
performance-analyzer tracks:
Outputs:
performance-analyzer identifies:
Outputs:
campaign-strategist analyzes:
Outputs:
The skill integrates with your full tech stack:
What we export:
How to deploy:
What we export:
How to deploy:
What we export:
How to deploy:
What we use:
Integration mode:
What we can generate:
User: "Start new monthly campaign for MENA SaaS Founders targeting Proof Silence"
Meta Skill Execution:
Runs questionnaire:
Calls campaign-strategist:
Calls signal-detector:
Calls wedge-generator:
Calls asset-factory:
Calls lead-magnet-builder:
Calls dsi-calculator:
Calls landing-page-architect:
Calls culture-adapter:
Calls integration-orchestrator:
Calls performance-analyzer:
Output to User:
✅ Campaign created: "MENA SaaS Founders - Proof Silence (Data Fragmentation)"
📁 Assets folder: /campaign-assets/
- 18 emails (3 weeks × 2 variants × 3 emails each)
- 12 LinkedIn messages (3 weeks × 2 variants × 2 messages each)
- 9 WhatsApp messages (3 variants × 3 psychology types)
- 3 VSL scripts
- 3 social posts
- 1 Case Study Library (interactive artifact)
- 1 Master VSL landing page
🚀 Integration status:
- GHL: Deployed (3 workflows active)
- Instantly: Deployed (147 prospects loaded)
- HeyReach: Ready (CSV exported, manual import needed)
📊 Performance tracking: ENABLED
- Week 1 check-in scheduled
- Baseline: 6.2% reply, 0.8% meeting (MENA SaaS benchmark)
- Tracking: Reply rate, meeting rate, objection keywords
🎯 Next steps:
1. Review assets in /campaign-assets/
2. Import HeyReach CSV (147 prospects)
3. Activate GHL workflows (currently paused)
4. Launch Instantly campaign (scheduled for Monday 9am UAE time)
Want to review assets before launch? Or shall I activate everything?
User: "Generate Week 2 assets for MENA SaaS campaign"
Meta Skill Execution:
Loads campaign context:
Calls performance-analyzer:
Calls wedge-generator:
Calls asset-factory:
Calls culture-adapter:
Calls integration-orchestrator:
Output to User:
✅ Week 2 assets ready
📊 Week 1 Performance:
- Winner: Wedge 2 (13.7% reply rate - 2.2x benchmark)
- Key insight: "Unified dashboard" messaging resonated
- New objection: "Implementation time" (3 mentions)
📁 Week 2 Assets:
- 6 emails (A/B variants, now includes "implementation time" handling)
- 4 LinkedIn messages
- 3 WhatsApp messages
- 2 social posts
🚀 Deployment:
- GHL: Updated (new sequences uploaded)
- Instantly: Variant B deployed to top 50 engaged prospects
🎯 Recommendation: Double down on Wedge 2 for Week 3 as well.
Reply rate is 2.2x benchmark—this is working.
User: "Analyze last week's performance and recommend pivot"
Meta Skill Execution:
Calls performance-analyzer:
Calls campaign-strategist:
Calls wedge-generator (for Option B):
Output to User:
📊 Performance Analysis: MENA SaaS Campaign (Weeks 1-3)
✅ Strong Performance:
- Reply rate: 9.1% (1.5x benchmark)
- Meeting rate: 1.2% (1.5x benchmark)
- Wedge 2 remains winner (14.3% reply)
🔍 New Signal Discovered:
"8 responders mentioned 'sales team scattered across regions'"
→ Potential Intent signal: Geographic distribution pain
🎯 Recommendations:
**Option A: Double Down (Recommended)**
- Continue Wedge 2, create 2 new A/B variants
- Rationale: 14.3% reply is exceptional, don't break what's working
- Risk: LOW
**Option B: Pivot to New Signal**
- New wedge: "Regional sales teams need regional dashboards"
- Rationale: Strong signal (8 mentions), specific pain
- Risk: MEDIUM (restart momentum)
**Option C: Hybrid (70/30 Split)**
- 70% continue Wedge 2
- 30% test new "geographic distribution" wedge
- Rationale: Maintain performance while exploring
- Risk: LOW-MEDIUM
Which option should I execute?
This meta skill is the orchestrator for your entire Signal-to-Trust GTM framework. It coordinates 10 specialized sub-skills to transform observed signals into complete multi-channel campaigns with performance tracking and continuous improvement.
Key principles:
The framework is designed to be self-improving: every campaign generates data, every data point refines templates, every pattern discovered expands the signal library.
Let's build signal-based campaigns that actually convert.