Gtm Pipeline:demo

Demo

Generate a demo lead list of ~10 enriched contacts with personalized message examples, triggered by a webhook prompt.

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

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When to Use

• Webhook trigger: user submits a free demo form describing their target audience
• Goal: prove the AI agent writes authentic, non-generic outreach using real leads
• Scope: ~10 contacts, enriched with LinkedIn + email, 2–4 message examples

Demo Restrictions

• No phone enrichment — email only
• ~10 contacts (request 10–15, expect enrichment drop-off)
• Message generation is optional but recommended
• Signal search is opt-in — off by default. Enable via --with-signals flag or explicit user request. See Step 5.5.

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Step 1 — Parse the Prompt & Ask Discovery Questions

The webhook prompt describes the user's target audience. Before running anything, extract or ask for:

Must have:

• What do you sell / offer?
• Who is your ideal customer? (industry, role, company size, location)
• What's your value proposition?
• What tone? (formal vs. casual, examples if possible)
• Is this for recruiting OR selling to customers?

If not in the prompt, infer or ask:

• Target job titles
• Target location
• Target company size or type

Do NOT proceed to search until ICP is clear enough to build a meaningful filter.

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

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Step 2 — Create Working Directory

Create the {client-slug}-gtm/ directory structure as defined in conventions.md. Write the ICP definition to context/icp.md.

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Step 3 — People Search (10 contacts)

Use the people-search skill to find ~10–15 contacts.

Provider selection for demo:

• Prefer BetterContact Lead Finder or FullEnrich Finder — both return LinkedIn URLs directly, needed for email enrichment
• If no company list (persona-based prompt), use Parallel FindAll or BC Search

Key fields to collect:

full_name, first_name, last_name,
job_title, company_name, company_domain,
linkedin_profile_url, location

Follow the people-search execution protocol: sandbox → test → review → run.

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Step 4 — Contact Filter (ICP Ranking)

Run contact-filter on the 10–15 contacts found. Even small batches benefit from ICP ranking — it ensures the enrichment step focuses on the best-fit contacts.

• Applies job tier, industry tier, location tier, and company size classification
• Rejects hard non-ICP contacts
• Ranks passed contacts by priority
• Output: csv/intermediate/contacts_filtered.csv

For demos: use a relaxed hard-reject threshold (allow tiers 1–5 to pass), prioritize ranking over filtering.

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Step 5 — People Enrichment (Email Only)

Run people-enrichment on the filtered contacts. Demo mode: email only, no phone.

Recommended flow:

1. FullEnrich v2 (email) — all contacts
2. Pipe0 waterfall — for FE misses only

Additional enrichment for message personalization (if available):

• LinkedIn headline and summary (from LinkedIn scrape via PhantomBuster)
• Recent LinkedIn posts (2–3 per contact) — significantly improves message quality

Minimum viable fields for message generation:

name, job_title, company_name, linkedin_profile_url,
headline (optional), summary (optional), recent_posts (optional)

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Step 5.5 — Signal Search (Optional)

Default: OFF. Enable when:

• The user explicitly asks for signal-anchored messages
• The webhook prompt mentions buying triggers (funding, hiring, transformation, recent news)
• The client's offering depends on timing signals to make sense (e.g. "we help post-Series-A companies scale ops")
• A --with-signals flag is passed to the demo invocation

Cost note: adds ~$0.01–0.05 per unique company (web search + scoring). On a 10-contact demo, that's typically 5–10 unique companies, so = 70 signal anchored in real recent news is the strongest hook the demo can produce.

Generation Process

1. Write a client-specific system prompt (save to prompts/message_prompt.md)
2. Generate 2–4 samples — include contacts with and without LinkedIn posts
3. Review against quality checklist above
4. If issues found, refine the system prompt and regenerate
5. Only batch generate once quality is approved

System Prompt Template (key sections)

- Client context: what they sell, who they target, their value prop, tone
- Forbidden rules: no profile repetition, no generic flattery
- Message structure: hook → bridge → offer → CTA
- Hook examples: with posts / without posts
- Character limit: 320–450

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Step 7 — Output

Deliver:

1. CSV at csv/output/contacts_enriched.csv: lead data + generated messages
2. Google Sheet (optional): formatted for easy review

Output CSV Columns

name, first_name, last_name, location, headline, summary,
linkedin_url, email, email_status,
company_name, job_title,
post_1_content, post_1_date,
post_2_content, post_2_date,
generated_message, char_count, has_posts

Messages saved separately to csv/output/messages.csv.

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Quality Checklist (Before Delivering)

• [ ] Messages feel personal, not templated
• [ ] No profile info repetition
• [ ] Clear value proposition in every message
• [ ] Proper formatting (line breaks, character count 320–450)
• [ ] Hook differs between contacts (no copy-paste structure)
• [ ] All data fields populated correctly
• [ ] Client-specific context incorporated

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Trigger Context

Webhook (demo form): Free demo trigger — user describes their ICP in a text prompt. Run this skill with ~10 contacts and 2–4 message samples.

Stripe payment (full list): After successful payment, run the full pipeline via the pipeline skill. See pipeline skill for orchestration.

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What's Missing (To Document)

• LinkedIn post scraping via PhantomBuster API (launch, poll, download)
• Automated webhook integration (currently manual trigger)
• Stripe payment trigger integration

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.