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insights-repository-kit

31 stars 7 forks
28
F

Governance + tooling pattern for storing research artifacts, tagging, and discovery.

Marketplace
Also in: github

Third-Party Agent Skill: Review the code before installing. Agent skills execute in your AI assistant's environment and can access your files. Learn more about security

Installation for Agentic Skill

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skilz install gtmagents/gtm-agents/insights-repository-kit
skilz install gtmagents/gtm-agents/insights-repository-kit --agent opencode
skilz install gtmagents/gtm-agents/insights-repository-kit --agent codex
skilz install gtmagents/gtm-agents/insights-repository-kit --agent gemini

First time? Install Skilz: pip install skilz

Works with 22+ AI coding assistants

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Extract and copy to ~/.claude/skills/ then restart Claude Desktop

1. Clone the repository:
git clone https://github.com/gtmagents/gtm-agents
2. Copy the agent skill directory:
cp -r gtm-agents/plugins/market-research/skills/insights-repository-kit ~/.claude/skills/
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Need detailed installation help? Check our platform-specific guides:

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Agentic Skill Details

Owner
gtmagents (GitHub)
Repository
gtm-agents
Stars
31
Forks
7
Type
Non-Technical
Meta-Domain
development
Primary Domain
javascript
Market Score
28
Agentic Skill Grades →

Agent Skill Grade

F
Score: 58/100 Click to see breakdown

Score Breakdown

Spec Compliance
11/15
PDA Architecture
18/30
Ease of Use
13/25
Writing Style
7/10
Utility
8/20
Modifiers: +1

Areas to Improve

  • Description needs trigger phrases
  • Missing Reference Files for Templates
  • No Step-by-Step Workflow

Recommendations

  • Focus on improving Ease Of Use (currently 13/25)
  • Focus on improving Utility (currently 8/20)
  • Address 2 high-severity issues first

Graded: 2026-01-24

Developer Feedback

I've been digging through repository analysis tools, and the insights-repository-kit's approach to metadata extraction caught my eye—though I'm curious why the Progressive Disclosure architecture feels a bit top-heavy for what it's trying to accomplish (58/100 suggests there might be some structural trade-offs worth examining).

Links:

The TL;DR

You're at 58/100, which is F territory. This is based on Anthropic's Progressive Disclosure Architecture best practices for agentic skills. Your strongest area is Spec Compliance (11/15)—the YAML frontmatter is clean and properly formatted. The weakest link is Utility (8/20)—the framework outlines concepts without giving developers actual implementation guidance or examples they can use.

What's Working Well

  • Clean frontmatter structure – Your YAML is valid with required fields properly configured
  • Consistent terminology – "repository," "metadata," "research artifacts" are used consistently throughout
  • Real problem identification – You're addressing an actual pain point (organizing research without getting lost)
  • grep-friendly structure – Simple, scannable file format that's easy to parse

The Big One: Missing Reference Files & Templates

Here's what's holding you back: you mention templates (repository IA diagram, metadata dictionary, intake forms) but don't actually provide them. This breaks the Progressive Disclosure principle—instead of layering from simple to complex, you're leaving readers hanging.

The fix: Create a references/ directory with actual implementation files:

  • references/templates/repository-ia-template.md – Actual folder structure diagram
  • references/templates/metadata-schema.json –...

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