contextdocs

Maintain lean AI IDE context files with an AGENTS-first workflow.

5|1|Updated Mar 10, 2026

Search Tags:#context management #context verification #signal gate #ai ide integration #context guard #bridge files #stale context remediation

Ready to run

npx skills add https://github.com/littlebearapps/contextdocs --skill contextdocs

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: contextdocs
Source: https://github.com/littlebearapps/contextdocs/tree/main
Command: npx skills add https://github.com/littlebearapps/contextdocs --skill contextdocs

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

ContextDocs prevents AI coding agents from wasting tokens or producing incorrect behavior due to bloated or stale project context files by generating and maintaining an AGENTS-first context system across tools.

Core Features & Use Cases

Lean AGENTS-first context with Signal Gate: generates canonical AGENTS.md plus thin tool bridges while excluding discoverable file trees and architecture overviews.
Full lifecycle maintenance: supports init, update (incremental drift patching), promote (MEMORY.md → CLAUDE.md), and audit for staleness and quality issues.
Freshness enforcement via Context Guard: installs optional Claude Code hooks that perform health checks at session start and can enforce freshness at commit time.
0–100 context health scoring: validates line budgets, path accuracy, bridge consistency, discoverable content, and aggregate context load for CI/CD gating.

Quick Start

Ask your AI to run /contextdocs:ai-context init to bootstrap canonical AGENTS.md and the required bridge files for your project.

Frequently Asked Questions about contextdocs

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I prevent stale context files from degrading AI coding agent performance?▼

To prevent stale context files from degrading AI coding agent performance, use an AGENTS-first workflow that applies Signal Gate filtering and health scoring to maintain lean, tool-compatible context. This enforces freshness via optional Context Guard hooks at session start and commit time.

What is the best way to manage AGENTS.md context across multiple coding agents?▼

The best way to manage AGENTS.md context across multiple coding agents is generating a canonical AGENTS.md file with thin tool bridges. This approach applies bridge consistency rules and excludes discoverable file trees to maintain lean context across tools like Claude Code.

How do I bootstrap an AGENTS-first context system for software projects?▼

To bootstrap an AGENTS-first context system for software projects, run the /contextdocs:ai-context init command. This generates the canonical AGENTS.md and the required bridge files, applying Signal Gate filtering to exclude discoverable architecture overviews.

Can I use Context Guard hooks to enforce context freshness at commit time?▼

Yes, you can use Context Guard hooks to enforce context freshness at commit time. These optional Claude Code hooks perform health checks at session start and validate line budgets, path accuracy, and aggregate context load for CI/CD gating.

How does MEMORY.md promotion work when updating AI IDE context?▼

MEMORY.md promotion works by upgrading verified memory entries into CLAUDE.md during incremental drift patching. This full lifecycle maintenance process patches stale context and ensures bridge consistency across your AI IDE integration.

Why does my AI IDE waste tokens on bloated project context files?▼

Your AI IDE wastes tokens on bloated project context files when they contain discoverable file trees and architecture overviews. Applying Signal Gate filtering generates lean AGENTS.md files while producing a 0–100 health score to verify aggregate context load.