ICP Prompt Builder GTM Skill

Before you pay to pull 5,000 companies, tune a qualification prompt on 10-50 of them. This skill walks you through the iterative loop.

Why this exists

List-builder skills (DiscoLike, Blitz, Prospeo, Google Maps) return COMPANIES, but they don't know whether those companies match your ICP. If your list-builder returns 5,000 companies and 80% are wrong fits, you'll waste money enriching them for emails that go nowhere.

The fix: build an AI qualification prompt BEFORE scaling. Pull 10 companies, have the prompt score them, compare to your judgment, refine, repeat. Once the prompt agrees with you 2 rounds in a row with zero corrections, lock it in and apply it at scale.

Always uses Task sub-agents (no API key)

This skill runs entirely inside Claude Code via the Task tool. No Anthropic SDK calls, no OpenAI calls — Claude Code does the scoring itself. This is intentional:

No extra API spend. Uses your Claude Code plan.
No key management. Works out of the box.
Scaleable within reason. For 20-100 evaluations, parallel Task sub-agents batch 10-20 companies per agent.

At very large scale (5,000+ companies per batch), you may want to export the tuned prompt and run it through the OpenAI / Anthropic API with parallelism for speed. But TUNING happens inside Claude Code.

The loop (8 steps)

Step 1 — Gather ICP context

Claude asks the user (or reads client-profile.yaml from /icp-onboarding):

Website of the client selling (to scrape for context)
Who IS a good customer? What makes them a good fit?
Who is NOT a good customer? What disqualifies them?
Any specific signals? (B2B only, revenue range, tech stack, hiring status, recent fundraise, etc.)
Any HARD disqualifiers? (competitor domains, existing customer domains, certain industries/geographies)

Step 2 — Select 10 test companies

Pull 10 companies from the list-builder output:

Mix likely-good and likely-bad fits
Variety in industry, size, location
Each company needs at minimum: domain, company_name, industry, headcount, description
Richer fields (Clay-derived: Business Type, Scale Scope, Revenue) make scoring better

Step 3 — Build the initial qualification prompt

Template:

You are an ICP evaluator for {CLIENT_NAME}.

## Target ICP
{ICP description from user or client-profile.yaml}

## Qualification criteria (MUST be true)
- {criterion 1}
- {criterion 2}
- ...

## Disqualification criteria (ANY match = disqualify)
- {disqualifier 1}
- {disqualifier 2}
- ...

## Input
You will receive a company with these fields:
- domain, name, industry, headcount, description
- (optional) Business Type, Revenue, Scale Scope

## Output
For each company, return JSON:
{
  "qualified": true | false,
  "confidence": 0.0-1.0,
  "reason": "one-sentence explanation"
}

Step 4 — Run the prompt on the 10 companies

Via the Task tool. Launch one Task sub-agent that reads the prompt + 10 companies, returns 10 JSON scores.

Step 5 — Present results to the user

Format as a table:

Company                   | Qualified | Conf | Reason
--------------------------+-----------+------+----------------------------------------
acme-corp.com             | YES       | 0.92 | B2B SaaS, 200 employees, target industry
random-nonprofit.org      | NO        | 0.95 | Nonprofit, not a business customer
edge-case-company.com     | YES       | 0.55 | Could fit but revenue model unclear

Step 6 — Collect user feedback

Ask specifically:

Which evaluations are wrong? (e.g., "acme-corp should be NO because they're a competitor")
Which are right but for the wrong reason?
Any patterns the prompt missed?
Any new disqualifiers to add?

If the user has zero corrections, log this round as "approved."

Step 7 — Refine the prompt (or move on)

If the user gave corrections:

Add/remove qualification criteria
Tighten/loosen disqualifiers
Add specific examples of edge cases ("companies like X are NOT a fit because Y")
Adjust confidence thresholds if everything is coming back 0.5

Then go back to Step 4 with a NEW batch of 10 companies.

Step 8 — Stop condition + save

The loop ends when 2 consecutive rounds have zero corrections from the user. When that happens:

Save the final tuned prompt to ~/cold-email-ai-skills/profiles/<business-slug>/icp-prompt.txt
Append metadata to client-profile.yaml:

icp_qualification_prompt:
  path: profiles/<slug>/icp-prompt.txt
  tuned_at: YYYY-MM-DD
  rounds_to_convergence: 3
  final_batch_size: 10

Print a one-liner for the next skill:

Prompt locked. To score your 5000 companies:
  npx tsx ~/cold-email-ai-skills/skills/icp-prompt-builder/scripts/score-batch.ts \
    --prompt-file=profiles/<slug>/icp-prompt.txt \
    --companies=path/to/companies.csv \
    --out=scored.csv

Approval-loop rules (important)

Never auto-approve. Even if the prompt looks right, require the user to explicitly say "approved" or give zero corrections for 2 consecutive rounds.
Reset counter on any correction. One correction resets the streak to 0.
Don't skip the batches. Running 30 companies all at once feels faster but masks errors. 10 at a time is the right batch size — small enough to eyeball.
Show the prompt each round. After each refinement, display the current full prompt back to the user so they can see what changed.
Always use Task tool sub-agents for the scoring inside each round. Never call external APIs.

Using the tuned prompt at scale

Once saved, the prompt is applied to the full list via scripts/score-batch.ts. Options:

Option A (free, slow) — run through Claude Code Task sub-agents in batches of 20 companies per agent. Good for <500 total.

Option B (paid, fast) — export prompt + companies to OpenAI / Anthropic API with parallelism. Good for 500-50,000.

The script supports both. Default is Option A to keep everything inside Claude Code.

Recommended flow

/icp-onboarding → produce client-profile.yaml
/disco-like OR /blitz-list-builder OR /prospeo-full-export → pull a sample of 50-100 companies
/icp-prompt-builder → tune qualification prompt on that sample (3-5 rounds typical)
Scale the list-builder to 5,000+ companies
Apply the tuned prompt to the full list → only keep qualified: true with confidence >= 0.6
/blitz-list-builder or /email-waterfall on the qualified subset
Upload to Smartlead

Data points the prompt can use

From most list-builder outputs:

domain, company_name, industry, headcount, description, LinkedIn URL

Additional fields (if enrichment skills have been run):

Business Type (B2B / B2C / B2B2C)
Annual Revenue range
Scale Scope (Enterprise / Mid-Market / SMB)
SubIndustry (more specific than primary industry)
Tech stack (Clearbit, BuiltWith data)
Recent signals (funding, hiring, news)

Tell the AI about the fields you have access to in the prompt preamble.

Common mistakes

Building the prompt too tight on round 1. Start broad, narrow with feedback.
Not including negative examples. "Companies like Netflix are NOT a fit because they're B2C" is more powerful than generic "must be B2B".
Using only "qualified: true/false" without confidence. Always ask for confidence — 0.5-0.7 borderline cases are where you learn the most.
Scoring 50 at once "to save time." Defeats the point of the loop.
Not saving the prompt. The point of tuning is reuse. If you don't save, you'll re-tune next time.

Scripts

scripts/score-batch.ts — apply tuned prompt to a CSV of companies

What to do next

Apply the tuned prompt to your full list (the list-building skill you came from — Prospeo, Blitz, DiscoLike, Google Maps, or Competitor Engagers — will walk through this). Then /list-quality-scorecard to grade the filtered output.

Or wait: if the prompt didn't converge within 5 rounds (you kept making corrections), your source data may be too thin. Enrich with more fields (company description, headcount, tech stack) before retrying.

Related skills

/icp-onboarding — run FIRST to produce client-profile.yaml
/disco-like, /blitz-list-builder, /google-maps-list-builder, /prospeo-full-export — pull the companies this skill qualifies
/personalization-subagent-pattern — same approval-loop pattern, applied to copy personalization