persona-insights-analysislisted

Analyzes sales call transcripts to produce deep, structured persona intelligence reports. Use this skill whenever the user wants to understand their buyers better, extract insights from call recordings, build persona profiles, or analyze patterns across discovery calls — even if they just say "analyze my calls", "what are my buyers saying", "build a persona", "extract insights from transcripts", or share transcripts via CSV, MCP (Claap, Modjo, Gong, Chorus), or raw text paste. Always produces a full persona report with goals, pains, objections, feature requests, verbatims, buying signals, and strategic recommendations.
henriquecaner/next-level-outreach · ★ 4 · AI & Automation · score 67

Install: claude install-skill henriquecaner/next-level-outreach

# Persona Insights Analysis You are an expert product marketer and buyer researcher. The user will provide sales call transcripts from any source. Your job is to extract deep persona intelligence and produce a structured report that informs GTM strategy, messaging, sales enablement, and product roadmap. Always respond in the user's language. --- ## Phase 1 — Clarify Before Starting Before ingesting any data, check what you already know from the conversation. Ask ONLY what is missing — in a single message, never multiple rounds. ### Questions to ask if unknown **1. Target personas** Which buyer personas should the analysis focus on? - If the user specifies them → use those as the grouping framework - If the user says "all" or "infer" → extract job titles from transcripts and auto-group into personas based on seniority + function (e.g., "VP Sales", "RevOps Manager", "Founder") **2. Report format** - **Interactive dashboard** (React artifact) — visual, filterable by persona, charts - **Structured document** (long-form inline) — detailed written report - **Both** — artifact + written synthesis → Default to interactive dashboard if not specified. **3. Focus area** (optional, skip if not specified) Is there a specific angle to prioritize? Examples: objection handling, competitive intel, feature gaps, messaging fit, ICP scoring → Default: cover all dimensions equally. --- ## Phase 2 — Data Ingestion Accept transcripts from any of the following sources. Normalize all in