| name | gtm-pipeline:demo |
| description | Generate a demo lead list of ~10 enriched contacts with personalized message examples. Use when a demo is requested, a webhook prompt describes a target audience, or someone asks to "create a demo for [client]". Enforces demo mode restrictions (email only, no phone, ~10 contacts). Chains people-search → people-enrichment → message generation. |
Demo
Generate a demo lead list of ~10 enriched contacts with personalized message examples, triggered by a webhook prompt.
Read .agents/skills/_shared/conventions.md before executing.
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
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
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.
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.
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.
Step 5 — People Enrichment (Email Only)
Run people-enrichment on the filtered contacts. Demo mode: email only, no phone.
Recommended flow:
- FullEnrich v2 (email) — all contacts
- 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)
Step 6 — Generate Message Examples
Generate 2–4 sample messages before committing to the full batch.
Message Structure
Every message must follow: Hook → Bridge → Offer → Soft CTA
| Part | Purpose | Length |
|---|
| Hook | Reference something specific to this person (post, career move, company signal) | 1 sentence |
| Bridge | Connect their situation to your offer | 1 sentence |
| Offer | What you provide, clearly stated | 1 sentence |
| CTA | Soft ask — not "let's schedule a call" | 1 sentence |
Total: 320–450 characters. No blank line after greeting. Paragraphs separated by single line break.
Quality Rules
Must have:
- Specific hook (post reference OR career insight — not generic)
- Clear value proposition
- Natural, conversational tone
- Soft CTA
Must avoid:
- Repeating profile info they already know ("You work as X at Y")
- Generic observations ("impressive background", "I noticed you're in [industry]")
- Corporate jargon or buzzwords
- Pushy CTAs ("Let's schedule a call this week")
Generation Process
- Write a client-specific system prompt (save to
prompts/message_prompt.md)
- Generate 2–4 samples — include contacts with and without LinkedIn posts
- Review against quality checklist above
- If issues found, refine the system prompt and regenerate
- 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
Step 7 — Output
Deliver:
- CSV at
csv/output/contacts_enriched.csv: lead data + generated messages
- 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.
Quality Checklist (Before Delivering)
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.
What's Missing (To Document)
- LinkedIn post scraping via PhantomBuster API (launch, poll, download)
- Automated webhook integration (currently manual trigger)
- Stripe payment trigger integration
