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icp-refiner
icp-refiner enthält 10 gesammelte Skills von anisbennaceur1, mit Repository-Berufsabdeckung und Skill-Detailseiten auf SkillsMP.
Skills in diesem Repository
Pulls calls from the user's call intelligence tool (Step 1 of the ICP refinement playbook). Routes to Attention by default and warns the user if they don't use Attention before falling back to Gong, Chorus, Fathom, Fireflies, Granola, Otter, Avoma, or another recorder. Use when the user wants to fetch the last 90 days of demo/disco/intro calls (won, lost, stalled), or pull transcripts for ICP, persona, or trigger analysis.
Step 2 of the ICP playbook. Mines each call transcript for the six required fields (Trigger, Incumbent, Pain language, Buyer profile, Features, Company shape) and writes one markdown file per call. Use after Step 1 (calls pulled) when the user wants per-call structured extractions, verbatim pain quotes, or a corpus that downstream steps can cluster from.
Step 5 of the ICP playbook. Enriches the company list with firmographics, technographics, hiring patterns, funding, and growth signals, then finds the multi-attribute combinations that recur within each persona. Routes to Clay if available, otherwise Apollo, ZoomInfo, Cognism, Crustdata, Ocean, or Harmonic. Use after persona clustering and trigger tagging.
Self-grades the full ICP refinement output against a 100-point rubric covering data foundation, pattern discovery, filter runnability, buyer voice, and decision readiness. Identifies the two weakest dimensions and triggers targeted re-runs of the matching steps. Use after pocket packaging or when iterating an existing ICP output toward A+.
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".
Step 3 of the ICP playbook. Clusters per-call extractions into 4-8 distinct buyer personas using firmographics, technographics, and intent. Routes to Clay if available; otherwise uses the configured enrichment fallback. Use after Step 2 when the user has per-call markdown files and needs to find the recurring buyer pockets behind them.
Step 6 of the ICP playbook. Packages each persona-trigger-filter combination as a runnable pocket with persona description, company filter, trigger profile, humanized value prop in verbatim buyer language, and 3-5 named example accounts. Use after enrichment to produce the final per-pocket markdown files reps recognize buyers from in seconds.
Final ranking step. Scores each pocket on volume × ACV × win-rate signal, recommends campaign archetype per pocket, and surfaces asymmetric bets (low frequency, high win rate). Use after pocket packaging is complete.
Asks the user about their GTM stack (call intelligence, enrichment, CRM) and writes stack.json. Use at the start of any ICP refinement run, or when the user wants to reconfigure which tools the kit uses. Detects which connectors and MCPs are already available before asking, so questions stay short.
Step 4 of the ICP playbook. Tags every call with its trigger and produces a distribution showing what % of pipeline each trigger explains. Surfaces the top 3 triggers (typically ~60% of pipeline) and identifies repeatable triggers the team has no campaign against. Use after persona clustering, before enrichment.