/icp-prompt-builder

name: icp-prompt-builder
description: Interactive loop that builds and tunes an AI prompt for evaluating whether a company fits a client's ICP. Run after any list-building skill (disco-like, blitz-list-builder, google-maps-list-builder, prospeo-full-export) to qualify companies before scaling. Iterates batches of 10 companies with user feedback, stops when 2 consecutive rounds have zero corrections, saves the final prompt for reuse. Always uses Claude Code Task sub-agents — never an external API key.

ICP Prompt Builder

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:

1. Save the final tuned prompt to `~/cold-email-ai-skills/profiles/<business-slug>/icp-prompt.txt` 2. 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

3. Print a one-liner for the next skill:

Prompt locked. To score your 5000 companies:
  npx tsx ~/cold-email-ai-skills/skills/list-expander/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. N