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    🤖 AGENTS.md Linter

    1

    Optimized Agent Instruction Linting Maintaining clear and consistent instructions for AI agents is critical for reliable development workflows.

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    🤖 AGENTS.md Linter

    🤖 AGENTS.md Linter

    Example session with this skill installed

    Lint my .cursorrules and AGENTS.md files for any contradictions or stale command references.

    • Read your context and instructions
    • Compiled the agents md linter

    Linting Results

    • [AML003] Stale Command: .cursorrules line 12 references npm run dev:legacy, but only dev:next exists in package.json.
    • [AML001] Contradiction: AGENTS.md mandates tabs for indents, while .cursorrules line 45 enforces 2-space indentation.
    • [AML007] Redundant: Rule 'No comments' found in both files.
    line 12 references , but only exists in package.json

    Connects securely to your tools. The creator never sees your data.

    About this skill

    Optimized Agent Instruction Linting

    Maintaining clear and consistent instructions for AI agents is critical for reliable development workflows. The AGENTS.md Linter provides a rigorous analysis of your configuration files (AGENTS.md, CLAUDE.md, .cursorrules) to ensure your AI isn't being slowed down by conflicting commands or stale context.

    What it does

    This skill scans your repository's instruction files against your actual codebase to identify breaking issues. It goes beyond simple text checking by cross-referencing your project's package.json, Makefile, and source files to verify that the commands your agents are told to run actually exist.

    • Detects conflicting rules that cause agent "hallucination" or refusal.
    • Flags stale file paths and obsolete terminal commands.
    • Identifies overly broad or unsafe instructions that could compromise code quality.
    • Suggests consolidation strategies when multiple competing rule files are found.

    Why use this skill

    Unlike basic prompting, this tool uses specific rule IDs (AML001-AML007) and manifest scanning to provide high-precision feedback. It prevents the common "instruction bloat" that leads to context window waste and ensures your Cursor, Claude, or Copilot agents remain surgical and efficient.

    How to use

    1. 1

      Connect your agent to the Agensi MCP

      Copy the prompt and paste it into Claude, ChatGPT, Cursor or any agent that supports MCP. It sets up the connection for you. Manual setup

    2. 2

      Sign in to Agensi

      Your agent shows a sign-in link. Sign in and click Allow.

    3. 3

      Ask for what you need

      Your agent loads this skill and uses it on your task. It always asks before any payment.

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    Security scan passed with 1 warning 4 months ago

    Listed4 months ago

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