Lint Hunter

Rust Agentic Skills

A modular, constraint-based skill set for Autonomous AI coding agents.

This repository transforms any general-purpose LLM (Claude, Gemini) into a disciplined Rust engineering team. It adheres to the Agent Context Protocol (ACP) to provide self-describing skills that explicitly define their triggers, capabilities, and execution phases.

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// Architecture Overview

We do not use monolithic instruction files. Instead, every skill in skills/ follows the Brain-Tool-Context architecture to maximize token efficiency:

1. Driver (SKILL.md):
   • The Brain. Contains minimal YAML metadata (trigger, rpi_phase) and high-level role benchmarks.
   • Usage: The agent reads this first to decide if it is relevant.
2. Tools (scripts/):
   • The Hands. Executable code (Shell scripts, Rust binaries) for reliable, deterministic task execution.
   • Usage: The agent executes these to perform work (e.g., init_project.sh, explain_error.sh).
3. Context (references/):
   • The Knowledge. Deep-dive documentation and "Dictionaries of Pain".
   • Usage: "Lazy loaded" by the agent only when specifically needed to solve a complex problem.

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// Integration Guide

1. Claude Code (Plugin)

This repository is a compliant Claude Plugin.

1. Clone this repository locally.
2. Allow Claude to discover the .Claude-plugin/marketplace.json manifest.
3. Result: Claude will automatically see "Rust Kernel" and "Lint Hunter" as available tools.

2. Gemini CLI (Extension)

Compatible with the Gemini CLI tool via the Model Context Protocol (MCP). This repo provides a dynamic server that auto-discovers skills.

1. Link Extension:

   gemini extensions link .
   

2. Activate:

   gemini context set rust-agentic-skills
   

3. Result: The rust-agentic-skills server starts up, reads your skills/ directory, and dynamically routes your requests to the appropriate skill (e.g., "Lint Hunter").

3. Manual / Custom Agents

Running the MCP Server Manually: You can run the server directly to verify standard JSON-RPC communication:

./bootstrap.sh

(Or cargo run --release if you prefer manual builds)

(Expects JSON-RPC messages on stdin)

For generic agents (ChatGPT, heavily customized setups):

1. System Prompt: Load AGENTS.md as your system instruction. It defines the RPI (Research → Plan → Implement) loop.
2. Context Loading: When the agent enters a specific phase (e.g., "Verification"), manually load the relevant SKILL.md (e.g., skills/lint-hunter/SKILL.md).

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// See it in Action

Want to see what a full session looks like?

• See Example Trace: A real-world interaction log showing the agent initializing a project, running commands, and validating outputs.

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// How to Contribute 🤝

We welcome new skills! Follow the Triad Pattern:

1. Create Directory: skills/<your-skill-name>/.
2. Create Driver: Add SKILL.md with YAML frontmatter:

   ---
   name: My Skill
   description: What it does.
   rpi_phase: Implementation
   trigger:
     - "keyword1"
     - "keyword2"
   capabilities:
     - capability 1
   ---
   

3. Add Tools: Put executable scripts in skills/<your-skill-name>/scripts/.
4. Add Context: Put documentation in skills/<your-skill-name>/references/.
5. Generate Docs: Run make doc to update AGENTS.md.

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// Roadmap (In Progress / TBD)

• Multi-Agent Beast Mode: Chaining multiple skills in a single "Beast Mode" loop without human intervention.
• New Skill: Test Architect (Refactoring & property-based testing).
• New Skill: Crates.io Scout (Dependency analysis).
• Automated CI: GitHub Action to run make verify on PRs.

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📜 License

MIT