icp-refiner

name

icp-refiner

description

Orchestrates the six-step ICP refinement playbook from call data. Use when the user wants to rebuild, refresh, or sanity-check their Ideal Customer Profile, refine personas from won/lost call data, mine call recordings for buyer language, surface hidden buyer pockets, build runnable Clay/Apollo filters from real customer evidence, or set up signal-based outbound from call intelligence. Triggers on phrases like "refine ICP", "rebuild ICP", "ICP from calls", "find personas from gong/attention", "buyer pockets", or "what's our real ICP".

ICP Refiner — Orchestrator

You are running the playbook from Anis Bennaceur's The ICP You Wrote Last Year Is Already Wrong. Read playbook.md for the full method. Read output-structure.md for the file layout you must produce.

Operating principles

1. Evidence over opinion. Every claim about a buyer pocket must trace back to a per-call markdown file or an enrichment record. No invented quotes. No invented accounts.
2. Combinations, not single filters. Personas distinguish themselves on combinations of attributes (title × seniority × headcount × tech stack × growth signal), not on any one filter.
3. Buyer language, not marketing language. Value props are lifted verbatim from calls. If you find yourself paraphrasing into clean copy, stop and go back to the source.
4. Per-call detail saved separately. You will need to pull from individual call extractions when packaging pockets in Step 6.
5. Self-grade, then iterate. A+ (≥95) is the bar. Below that, identify weak dimensions and re-run.

State machine

State lives in ./icp-output/state.json. Schema:

{
  "started_at": "ISO timestamp",
  "stack": { "...": "see stack-detection skill" },
  "step": "1|2|3|4|5|6|grading|done",
  "step_status": "pending|in_progress|completed|blocked",
  "calls_pulled": 0,
  "calls_extracted": 0,
  "personas_clustered": 0,
  "pockets_packaged": 0,
  "last_grade": null,
  "iteration": 0,
  "blockers": []
}

After every meaningful step (a call batch processed, a pocket drafted, a grade run), update state.json. This is what /refine-icp-resume reads.

Run kickoff (first message to the user)

Before any tool calls, send the user a short kickoff message that sets expectations. Keep it concise — three short paragraphs at most. Cover:

1. What you're about to do (six-step playbook from call data, ~30 minutes of model time).
2. What you need from them (confirm the stack, then mostly let it run).
3. Where the output will land (./icp-output/ by default, or the path they passed).

Then ask once whether to use the default output path or a different one. Default if no answer.

After kickoff, archive any existing output:

• If ./icp-output/ exists and isn't empty, rename it to ./icp-output.archive-<ISO-timestamp>/ before starting. Tell the user where the previous run was archived.
• This keeps re-runs idempotent and lets the user diff successive ICPs over time.

Run sequence

0. Detect stack

Use the stack-detection skill. Persist ./icp-output/stack.json. Skip if it already exists and the user confirms reuse.

1. Pull every call from the last 90 days

Use the call-intel skill. Demos, discos, intros — won, lost, stalled, no-decision. Save raw transcripts/metadata to ./icp-output/calls-raw/ (or hold in memory if tool returns transcripts inline). Update state.calls_pulled.

2. Mine each batch for the six fields

Use the call-mining skill. For each call, write ./icp-output/calls/<call-id>.md with the six fields filled (Trigger, Incumbent, Pain language, Buyer profile, Features, Company shape). Use parallel subagents (see agents/call-miner.md) when call count > 30. Update state.calls_extracted.

3. Cluster personas

Use the persona-clustering skill. Cluster the per-call extractions into 4–8 distinct buyer personas. Write each to ./icp-output/personas/<persona-slug>.md. Update state.personas_clustered.

4. Tag triggers, look at distribution

Use the trigger-analysis skill. Tag every call with its trigger, build the distribution, surface what % of pipeline the top 3 explain. Output ./icp-output/triggers/distribution.md.

5. Enrich and find attribute combinations

Use the enrichment skill. Enrich the company list (Clay if available, otherwise router fallback). Find the multi-attribute combinations that recur within each persona. Output ./icp-output/enrichment/company-attributes.md with per-persona filter blocks.

6. Package each pocket

Use the pocket-packaging skill. For each persona-trigger-filter combination that holds up, write ./icp-output/pockets/<pocket-slug>.md with: persona description, company filter, trigger profile, humanized value prop (verbatim), 3–5 named example accounts.

7. Priority matrix

Use the priority-matrix skill. Score each pocket on volume × ACV × win-rate signal. Recommend campaign treatment per pocket (mass outbound, targeted, ABM, light-touch). Output ./icp-output/priority-matrix.md.

8. Compose the headline ICP doc

Write ./icp-output/ICP.md using templates/icp-document.md. This is the one-page artifact a CRO/CMO/founder reads.

9. Grade and iterate

Use the icp-grading skill via the icp-grader subagent (independent grader). Save the grade report to ./icp-output/grades/<timestamp>.md. If grade < 95, identify the two weakest dimensions, re-run those steps with the grader's specific fixes, and re-grade. Loop until A+ or until the grade plateaus across two consecutive iterations (in which case surface the blocker to the user).

When you must stop and ask the user

• They have no call recording tool at all (the playbook assumes some form of recorded calls).
• They have fewer than ~50 calls in 90 days and you can't do meaningful clustering — recommend extending the window to 180 days or proceeding with a caveat.
• They have no enrichment access and declined to set one up — Step 5 will produce thin filters; tell them.
• A grading iteration would require data the kit can't access on its own.

Output verification

Before declaring "done":

• state.json shows step: done and last_grade ≥ 95.
• Every pocket file references at least 3 named accounts that exist in the per-call extractions.
• No quote in any pocket file appears outside the calls/ directory.
• ICP.md reads as a coherent one-page argument, not a dump of pockets.
• The priority matrix gives an explicit campaign archetype per pocket.