ryannadel

ryannadel / cognitive-coverage

Public

A coding agent skill that helps measure and close the understanding gap in any codebase, research corpus, or documentation set.

16
2
100% credibility
Found May 10, 2026 at 16 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

Cognitive Coverage provides a skill for AI coding agents to generate interactive teaching guides, dashboards, and progress trackers that measure and improve human understanding of codebases, research, documentation, or knowledge domains.

How It Works

1
🕵️‍♀️ Discover the tool

You find Cognitive Coverage, a helpful skill that makes AI assistants create learning guides for projects, papers, or docs.

2
🔧 Add it to your AI helper

You run a quick setup script to install the skill for your AI buddy like Copilot or Claude, so it's ready in any project.

3
📂 Open your project

You pick a codebase, set of research papers, documentation wiki, or knowledge area you want to truly understand.

4
🤖 Ask AI to create coverage

You simply tell your AI 'Generate cognitive coverage for this project,' and it scans everything to build your personalized understanding tools.

5
🚀 Launch the results

A friendly landing page pops open automatically, linking to your new dashboard, guide, and progress tracker.

6
📊 View your dashboard

Check the visual board to see what's covered, what's not, and priority spots to learn next.

7
📖 Learn with the guide

Dive into the interactive teaching pages with explanations, examples, mental models, and fun quizzes to test yourself.

🎉 Master your project

As you learn and quiz right, your coverage updates, filling gaps until you have a complete mental map of the whole thing.

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AI-Generated Review

What is cognitive-coverage?

Cognitive Coverage is a free Python skill for coding agents on GitHub that quantifies "cognitive debt"—the understanding gap in codebases, research papers, or docs—much like test coverage tracks code execution. Feed it to GitHub Copilot, Claude Code, or any local LLM-powered agent via custom instructions, and it generates interactive HTML learning guides with quizzes, JSON manifests, coverage dashboards, and launchers to track progress across files, concepts, and flows. It adapts to domains like codebases or wikis, prioritizing critical paths in large projects.

Why is it gaining traction?

This coding agent benchmark stands out by turning vague AI code gen into measurable comprehension, with auto-clustering for big repos and quiz-synced status updates via localStorage. As a zero-cost GitHub coding agent skill, it integrates seamlessly with Copilot, Cursor, or OpenAI Codex without pricing tiers, plus an optional MCP server for real-time queries like listing uncovered concepts during sessions. Devs grab it for coding agents comparison and ranking in agent harness setups.

Who should use this?

Teams onboarding devs to sprawling monorepos, AI-reliant squads using GitHub coding agents in IntelliJ or VS Code to combat blind code acceptance, or researchers/docs teams mapping knowledge gaps. Ideal for GitHub coding project ideas where local LLM coding agent skills boost cognitive coverage without external dependencies.

Verdict

At 16 stars and 1.0% credibility, it's immature but shines with thorough docs, examples, and a JSON schema for CI validation—try the quick Copilot install script if you're benchmarking coding agents. Promising for early adopters, but watch for broader agent support.

(198 words)

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