MCPInstall

generate-subsystem-skills

Generate specialized skills for each subsystem in the monorepo. Creates shared language skills and subsystem-specific checklists for high-quality AI code generation.

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Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/llama-farm/llamafarm --skill generate-subsystem-skills
  1. 01

    Install

    Run the command in your project. The CLI asks which agents to install for.

  2. 02

    Describe the task

    Ask your agent for something this skill covers. It loads generate-subsystem-skills automatically when relevant.

  3. 03

    Review the result

    The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

Generate Subsystem Skills

This skill analyzes each subsystem in the LlamaFarm monorepo and generates specialized Claude Code skills for security, performance, and language-specific best practices.

Usage

/generate-subsystem-skills

What Gets Generated

Shared Language Skills (4)

  • python-skills/ - Used by: server, rag, runtime, config, common
  • go-skills/ - Used by: cli
  • typescript-skills/ - Used by: designer, electron
  • react-skills/ - Used by: designer

Subsystem-Specific Skills (8)

  • cli-skills/ - Cobra, Bubbletea patterns
  • server-skills/ - FastAPI, Celery, Pydantic patterns
  • rag-skills/ - LlamaIndex, ChromaDB patterns
  • runtime-skills/ - PyTorch, Transformers patterns
  • designer-skills/ - TanStack Query, Tailwind, Radix patterns
  • electron-skills/ - Electron IPC, security patterns
  • config-skills/ - Pydantic, JSONSchema patterns
  • common-skills/ - HuggingFace Hub patterns

Generation Process

Step 1: Read Registry

Load subsystem definitions from subsystem-registry.md.

Step 2: Generate Shared Language Skills

Launch sub-agents IN PARALLEL to generate:

  1. Python Skills Agent - Analyze Python subsystems (server, rag, runtime, config, common), identify ideal patterns, generate python-skills/

  2. Go Skills Agent - Analyze CLI subsystem, identify ideal Go patterns, generate go-skills/

  3. TypeScript Skills Agent - Analyze designer and electron, identify ideal TS patterns, generate typescript-skills/

  4. React Skills Agent - Analyze designer, identify ideal React 18 patterns, generate react-skills/

Step 3: Generate Subsystem Skills

Launch sub-agents IN PARALLEL for each subsystem:

For each subsystem, the agent should:

  1. Read the subsystem's dependency files (package.json, pyproject.toml, go.mod)
  2. Analyze code patterns using Grep and Read
  3. Generate SKILL.md that links to shared language skills
  4. Generate framework-specific checklist files
  5. Write all files to .claude/skills/{subsystem}-skills/

Step 4: Report Summary

After all agents complete, report:

  • Number of skills generated
  • Total files created
  • Any errors encountered

Sub-Agent Prompt Templates

For Shared Language Skills

You are generating a shared {LANGUAGE} skills directory for Claude Code.

Analyze these subsystems that use {LANGUAGE}:
{SUBSYSTEM_PATHS}

Your task:
1. Read key files to understand patterns used
2. When patterns vary, document the IDEAL approach (not inconsistencies)
3. Reference industry best practices
4. Generate files in .claude/skills/{LANGUAGE}-skills/

Files to generate:
- SKILL.md (overview, ~100 lines)
- patterns.md (idiomatic patterns)
- error-handling.md
- testing.md
- security.md
- {additional language-specific files}

Each checklist item should have:
- Description of what to check
- Search pattern (grep command)
- Pass/fail criteria
- Severity level

For Subsystem Skills

You are generating subsystem-specific skills for {SUBSYSTEM} in Claude Code.

Directory: {PATH}
Tech Stack: {TECH_STACK}
Links to: {SHARED_SKILLS}

Your task:
1. Read dependency files and key source files
2. Identify framework-specific patterns
3. Generate SKILL.md that links to shared language skills
4. Generate framework-specific checklists

Files to generate:
- SKILL.md (overview with links to shared skills)
- {framework}.md for each framework used
- performance.md (subsystem-specific optimizations)

Remember: Document IDEAL patterns, not existing inconsistencies.

Key Principle

Prescribe ideal patterns - When the codebase has inconsistent patterns, the generated skills should document the BEST practice according to industry standards, not codify existing inconsistencies.


Output Location

All skills are written to .claude/skills/ with this structure:

.claude/skills/
├── python-skills/      # Shared
├── go-skills/          # Shared
├── typescript-skills/  # Shared
├── react-skills/       # Shared
├── cli-skills/         # Subsystem
├── server-skills/      # Subsystem
├── rag-skills/         # Subsystem
├── runtime-skills/     # Subsystem
├── designer-skills/    # Subsystem
├── electron-skills/    # Subsystem
├── config-skills/      # Subsystem
└── common-skills/      # Subsystem

FAQ

What does the generate-subsystem-skills skill do?

Generate specialized skills for each subsystem in the monorepo. Creates shared language skills and subsystem-specific checklists for high-quality AI code generation.

How do I install generate-subsystem-skills?

Run npx skills add https://github.com/llama-farm/llamafarm --skill generate-subsystem-skills in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/generate-subsystem-skills/ (personal) or .claude/skills/generate-subsystem-skills/ (project).

Which AI agents can use generate-subsystem-skills?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is generate-subsystem-skills safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains generate-subsystem-skills?

It is published by llama-farm in llama-farm/llamafarm. It has 39 recorded installs.

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