Agent Instructions Doctor

    1

    Audit and repair decaying agent instruction files to fix contradictions, stale rules, and token bloat.

    Free

    0 installsSecurity scanned

    Works with the AI tools you already use

    CClaude CodeCCursorCCodex CLIGGitHub CopilotGGemini CLI+17 more

    See it in action

    You say

    Audit my .cursorrules and CLAUDE.md files. The agent keeps using Yarn even though we switched to pnpm, and the context feels bloated.

    Your agent does

    Found 3 contradictions. 🔴 Actively harmful: .cursorrules line 12 references 'yarn install' while CLAUDE.md line 4 says 'use pnpm'. 🟡 Expensive: 400 tokens used for 'be thoughtful' prose. Proposed diff: update all commands to pnpm and convert prose to actionable decision-point lists.

    About this skill

    The problem

    Instruction files like CLAUDE.md or .cursorrules decay into "token scar tissue" over time. Agents start ignoring rules, behaving inconsistently, or hallucinating based on stale conventions for retired frameworks.

    What it does

    • Identifies direct and partial contradictions across multiple instruction files like AGENTS.md and .cursorrules.
    • Cross-checks rules against the actual repository to find dead paths, missing scripts, and retired framework references.
    • Flags "token bloat" and unenforceable rules that consume context without changing agent behavior.
    • Audits instruction placement to ensure high-priority rules are positioned where the LLM is most likely to attend to them.
    • Generates a prioritized report of harmful, inert, and missing instructions with ready-to-paste diffs.

    Frameworks & tools

    Works with Cursor (.cursorrules), Claude Dev/Roo Code (CLAUDE.md), GitHub Copilot (.github/copilot-instructions.md), and generic project AGENTS.md files.

    Why this beats prompting it yourself

    Manually auditing 2,000 tokens of rules for logical consistency is error-prone. This skill performs a multi-pass analysis that specifically checks for "hidden overrides" in nested directories that a standard prompt would likely miss.

    Use cases

    • Fixing an agent that keeps using the wrong library version despite instructions.
    • Reducing token costs in large monorepos with bloated context files.
    • Onboarding a new agent to a legacy project with outdated documentation.
    • Consolidating fragmented rules from three different editor-specific config files.

    Known limitations

    Does not access external networks. It analyzes and edits instruction files only, not the underlying application logic.

    How to install

    Drop the file into your AI Agent. Works with Claude, Cursor, ChatGPT, and 20+ more.

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