SkillsAdd

smithery.ai

community-insight-taxonomy

Tagging schema for classifying community signals by persona, journey, and business impact.

First seen Mar 20, 2026

Installation

$ npx skills add https://smithery.ai
View on skills.sh ↗ Website ↗

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Also in this package

Other skills from smithery.ai · top by installs.

npx skills add https://smithery.ai
smithery-ai-cli 7.8K frontend-design 6.2K skill-creator 2.5K ck:ui-ux-pro-max 1.3K pdf 1.2K xlsx 966 pptx 894 docx 884 webapp-testing 1 web-design-reviewer 657

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More details

Agent compatibility

Declared targets from SKILL.md / docs. Unmarked agents are not listed — the skill may still install via the CLI.

Claude Code Not declared
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Codex Not declared
GitHub Copilot Not declared
Windsurf Not declared
Gemini CLI Not declared
Cline Not declared
OpenCode Not declared

Package contents

Files included with this skill beyond the listing page.

  • skill md SKILL.md 1,293 B
  • docs SUMMARY.md 124 B

History

  1. First seen on skills.sh
  2. First recorded snapshot · 1 installs

SKILL.md

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.