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community-insight-taxonomy

community-insight-taxonomy

github.com/gtmagentsMarketing & growth42865 installsUpdated 30w ago
Repository

About community-insight-taxonomy

community-insight-taxonomy is a skill for standardizing how community feedback is categorized and shared. It organizes signals by persona, journey, driver, sentiment, urgency, and business context.

Key features

  1. 01Persona and journey-stage dimensions
  2. 02Product and experience drivers
  3. 03Sentiment and urgency labels
  4. 04Business linkage categories
  5. 05Tag dictionary and schema templates

Use cases

  1. 01Standardize tags across feedback channels
  2. 02Share structured insights with teams
  3. 03Prepare community-signal dashboards and routing

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/gtmagents/gtm-agents --skill community-insight-taxonomy

After installing, type/community-insight-taxonomyin Claude Code to confirm it loaded.

Details

From the project README

Community Insight Taxonomy Skill

When to Use

  • Standardizing feedback tagging across forums, events, surveys, and interviews.
  • Sharing structured insights with product, marketing, or CS teams.
  • Powering dashboards and AI routing for community signals.

Framework

  1. Persona Dimension – builders, admins, execs, partners, champions, newcomers.
  2. Journey Stage – onboarding, activation, adoption, expansion, advocacy, lapse.
  3. Driver Dimension – product, support, education, pricing, community experience, roadmap.
  4. Sentiment & Urgency – positive/neutral/negative + severity/impact.
  5. Business Linkage – map to ARR tier, account health, opportunity pipeline, or churn risk.

Templates

  • Tag dictionary with definitions, examples, and confidence guidelines.
  • Airtable/Sheet schema with validation + automation hooks.
  • Quick-reference cheatsheet for moderators/interviewers.

Tips

  • Run monthly taxonomy reviews to merge duplicates and add emerging themes.
  • Capture "what good looks like" examples to maintain consistency.
  • Pair with run-member-insight-sprint for auto-tagged datasets.

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