Gtm Pipeline:pipeline

Pipeline

Plan and orchestrate a full GTM pipeline — from discovery to enriched contact list. Determines which skills to run, in what order, based on client requirements and starting data.

Read ~/.claude/skills/gtm-pipeline/_shared/conventions.md before executing.

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When to Use

• Client needs a complete lead generation pipeline (not just one skill)
• You need to determine the right workflow based on what data exists
• Stripe payment trigger: execute full pipeline
• Any request that spans multiple GTM skills

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Step 1 — Assess Starting Point

Ask: What data does the user have?

| Starting Point | Available Data | Next Action | |----------------|---------------|-------------| | Nothing (just an ICP description) | ICP prompt only | Determine workflow → full pipeline | | Company list | CSV with company names/domains | Skip company-search → company-enrichment | | Contacts CSV | Names + companies, missing contact info | Skip to people-enrichment | | Signal data | Companies found via signals | Skip to company-enrichment/scoring | | Demo webhook | ICP prompt, ~10 contacts scope | Run demo skill | | SN search URL | Sales Navigator saved search | Run company-search (SN+PB) → continue |

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Step 2 — Determine Workflow

Company-First Workflow (Finite Markets)

When: Client provides a company list, known industry, or uses Sales Navigator. The market is bounded — you know (or can enumerate) the target companies.

Full sequence:

1. Company Search      → csv/input/companies_raw.csv
2. Company Enrichment  → csv/intermediate/companies_enriched.csv
3. ICP Scoring         → csv/intermediate/companies_scored.csv  (credit-saving gate: icp_score >= 70)
4. Signal Search       → csv/intermediate/signals.csv  (runs on gated set; doesn't read icp_score)
5. People Search       → csv/intermediate/contacts_found.csv
6. Contact Filter      → csv/intermediate/contacts_filtered.csv
7. People Enrichment   → csv/output/contacts_enriched.csv

The icp_score >= 70 gate at step 3 is for credit savings only — signal-search itself doesn't depend on the ICP score. Skip the gate if you want to score signals on the full enriched set.

Signal-First Workflow (Infinite Markets)

When: No company list — discover companies via buying intent signals. The market is unbounded — you find companies by their behavior.

Full sequence:

1. Signal-based Discovery  → companies found via signals (FindAll, Firecrawl Agent, LinkedIn Jobs)
                              writes csv/input/companies_raw.csv
2. Company Enrichment      → csv/intermediate/companies_enriched.csv
3. ICP Scoring             → csv/intermediate/companies_scored.csv  (optional gate)
4. People Search           → csv/intermediate/contacts_found.csv
5. Contact Filter          → csv/intermediate/contacts_filtered.csv
6. People Enrichment       → csv/output/contacts_enriched.csv

In Signal-First, signal-search runs on raw company names from the discovery channels (no enrichment needed yet). Company enrichment and ICP scoring happen after to filter the discovered set.

Shortcut Flows

| Starting Point | Skills Invoked | |----------------|---------------| | Demo webhook (ICP prompt only) | demo skill (people-search → contact-filter → people-enrichment → messages, 10 contacts, no phone) | | Company list provided | company-enrichment → (optional: signal-search) → people-search → contact-filter → people-enrichment | | Contacts CSV provided | people-enrichment only | | Persona prospecting (no companies) | people-search (persona mode) → contact-filter → people-enrichment | | Full pipeline (Stripe payment) | Full Workflow 1 or 2 based on requirements |

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Step 3 — Clarify Requirements

Before executing, confirm with the user:

ICP Definition

• Industry / vertical
• Target roles / job titles
• Company size (headcount range)
• Location / geography
• Revenue range (if relevant)
• Exclusions (e.g. no software companies, no enterprise)

Scope & Budget

• How many contacts needed? (10 for demo, 100+, 500+?)
• Credit budget constraints?
• Enrichment scope: email only? email + phone?
• Message generation needed?

Trigger Context

• Demo mode? (no phone, ~10 contacts)
• Stripe payment? (full pipeline, budget confirmed)
• Manual request? (flexible scope)

Save ICP to {client-slug}-gtm/context/icp.md.

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Step 4 — Plan the Sequence

Based on workflow + starting point, build the execution plan:

1. List which skills run, in what order
2. Recommend providers at each step (with costs from skill docs)
3. Estimate total credits and cost across all steps
4. Identify n8n flow customizations needed:

• ICP Google Doc (for ICP scoring in company-enrichment)
• Offering section (for signal scoring in signal-search)

1. Present plan for user approval before executing anything

Cost Estimation Template

## Pipeline Cost Estimate

### Step 1: Company Search
- Provider: [recommended]
- Records: ~{n}
- Est. cost: {credits} credits (~${cost})

### Step 2: Company Enrichment
- Provider: [recommended]
- Records: ~{n}
- Est. cost: {credits} credits (~${cost})

### Step 3: ICP Scoring
- LLM: [ask user which model]
- Records: ~{n}
- Est. cost: ~${cost} (LLM token pricing)

### Step 4: Signal Search (companies with icp_score >= 70 if gating)
- Sources: [which of the 4 sources]
- LLM for scoring: [ask user]
- Records: ~{n} (est. {pct}% pass ICP gate, or full set if no gate)
- Est. cost: {credits} credits + ~${llm_cost}

### Step 5: People Search
- Provider: [recommended]
- Records: ~{n} contacts across ~{m} companies
- Est. cost: {credits} credits (~${cost})

### Step 6: Contact Filter
- No API cost — local classification only
- Records: ~{n} contacts in → ~{n_filtered} contacts out (est. {pct}% pass ICP filter)
- ICP dimensions: job tier, industry tier, location tier, company size

### Step 7: People Enrichment
- Email provider: [recommended]
- Phone provider: [recommended or "skip — demo mode"]
- Records: ~{n}
- Est. cost: {credits} credits (~${cost})

### Total
- Credits: ~{total_credits}
- Cost: ~${total_cost}

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Step 5 — Execute and Chain

Invoke skills in order. After each step:

1. Review output quality — hit rates, data completeness, issues
2. Log to run_log.md — timestamp, provider, records, credits, duration
3. Log provider performance — to context/provider_performance.md
4. Chain output → input — each skill's output CSV becomes the next skill's input
5. Decide whether to proceed — if quality is poor, flag and discuss before continuing

Chaining Rules

| Skill Output | Next Skill Input | |-------------|-----------------| | csv/input/companies_raw.csv | company-enrichment reads this | | csv/intermediate/companies_enriched.csv | ICP scoring reads this | | csv/intermediate/companies_scored.csv | signal-search reads this (optional credit-saving gate: icp_score >= 70) | | csv/intermediate/signals.csv | Used for outreach context, not a direct input | | csv/intermediate/contacts_found.csv | contact-filter reads this | | csv/intermediate/contacts_filtered.csv | people-enrichment reads this | | csv/output/contacts_enriched.csv | Final output — ready for outreach or message generation |

After Each Step

Print the standardized Run Summary (from conventions.md) and ask:

• Results look good? Proceed to next step?
• Any issues to address first?
• Want to adjust providers or parameters?

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Step 6 — Customize Per Client

ICP Scoring (company-enrichment Phase 2 — n8n-backed)

• Create/connect a Google Doc with the client's ICP definition
• Or write to context/icp.md and use that as the reference
• Adjust score threshold (default: >= 70) based on selectivity

Signal Assessment (signal-search — script-based)

Signal-search runs ~/.claude/skills/gtm-signal-search/signal_search.py. Universal templates (extraction prompts, scoring rubric, request shapes) are baked into the script. Client-specific prompts come from three context files:

• context/icp.md — ICP definition (typically written by gtm-setup)
• context/offering.md — what we sell, value props (used in the scoring prompt). If absent, the script falls back to context/profile.md.
• context/signal_criteria.md — what counts as a signal for this offering: an include list + a short "not a signal" exclude list (used in the web-search objective + both extraction prompts)

Before invoking signal-search:

1. Confirm all three files exist; collect any missing pieces from the user. If signal_criteria.md is thin, help the user construct it (include + exclude + tuning params like max-age and result count) — see the signal-search skill's Step 2 for the interview.
2. Pick which optional sources to enable (--firecrawl or --firecrawl-pages-dir, --parallel-enrichment) based on whether on-site content / structured fields matter for this client. Use --firecrawl-pages-dir (Firecrawl-via-MCP) when there is no FIRECRAWL_API_KEY in the env.

No n8n workflow edits are required for signal-search anymore — the script reads everything from context/ at runtime. Keys are injected via _shared/resolve_env.sh (resolves $GTM_ENV_PATH on any machine).

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Working Directory

The pipeline creates and manages the full directory structure:

{client-slug}-gtm/
├── context/
│   ├── icp.md
│   └── provider_performance.md
├── prompts/
│   └── message_prompt.md          (demo only)
├── csv/
│   ├── input/
│   │   ├── companies_raw.csv
│   │   └── contacts_raw.csv
│   ├── intermediate/
│   │   ├── companies_enriched.csv
│   │   ├── companies_scored.csv
│   │   ├── signals.csv
│   │   ├── contacts_found.csv
│   │   ├── contacts_filtered.csv
│   │   └── request_ids.json
│   └── output/
│       ├── contacts_enriched.csv
│       └── messages.csv           (demo only)
└── run_log.md

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Decision Logic Summary

IF demo mode:
  → Run demo skill directly

IF user has contacts CSV:
  → people-enrichment only

IF user has company list:
  → company-enrichment → (optional: signal-search) → people-search → people-enrichment

IF user has nothing (just ICP):
  → Ask: Company-First or Signal-First?
  → Company-First: company-search → company-enrichment → signal-search → people-search → contact-filter → people-enrichment
  → Signal-First: signal-search (discovery) → company-enrichment → people-search → contact-filter → people-enrichment

IF paid trigger (e.g. Stripe webhook):
  → Full pipeline based on workflow determination above

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What's Missing (To Document)

• Payment trigger integration (e.g. Stripe webhook → pipeline invocation)
• Automated webhook integration for demo triggers
• Orchestration platform (e.g. n8n) integration for creating client-specific flow copies
• Pipeline recovery: resuming a partially completed pipeline from the last successful step
• Cost tracking dashboard / running totals across multiple pipeline runs

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: keinsaasforever
• Source: keinsaasforever/gtm-pipeline-skills
• License: MIT
• Homepage: https://www.keinsaas.com/de/research-agent

Install and usage instructions live in the source repository linked above.