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

This repository transforms any general-purpose LLM (Claude, Gemini, GPT-4) 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.

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.

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.

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").

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

cargo run --release --bin rust-agentic-skills

(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).

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.

• 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.

MIT