# Product Behavior Signals

> Convert mixed-method research and live telemetry into actionable user insight loops that guide product bets.

- Skill: `tjboudreaux/product-behavior-signals` (Agent Skill)
- Install (CLI): `npx skillmds@latest add tjboudreaux/product-behavior-signals`
- Raw SKILL.md: https://api.skillmd.com/api/skills/tjboudreaux/product-behavior-signals/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Research & Search
- Author: tjboudreaux (https://skillmd.com/u/tjboudreaux)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/tjboudreaux/product-behavior-signals

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# Behavior Signal Intelligence

## Intent
- Triangulate qualitative stories with quantitative funnels to uncover true motivation, anxiety, and habit loops.
- Maintain a living evidence stack that de-risks product, game, or web3 bets.

## Required Inputs
1. Latest analytics dashboards or raw event exports.
2. Recent interview/UGC transcripts, community threads, customer support logs.
3. Current instrumentation coverage map (events, properties, cohorts).

## Workflow
1. **Instrument gap check**
   - Audit current telemetry vs. critical behaviors; file gaps immediately.
2. **Pattern mining**
   - Cluster behaviors by intent (explorers, creators, whales, lurkers, churned) using both quant cuts and qual quotes.
   - Map anxieties vs. triggers vs. rewards.
3. **Insight synthesis**
   - For each cluster, craft a “belief statement” (user tension + evidence + implication).
   - Link to supporting artifacts (charts, clips, tickets).
4. **Action routing**
   - Translate belief statements into testable hypotheses or backlog items with owners.
   - Set review cadence (weekly insight review, monthly deep dive).

## Verification
- Every belief statement references at least one quantitative and one qualitative source.
- Instrumentation gaps tracked with owners/dates.
- Insight backlog reviewed with product/game leads and updated within the last sprint.