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.
Best use case
generate-subsystem-skills is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Generate specialized skills for each subsystem in the monorepo. Creates shared language skills and subsystem-specific checklists for high-quality AI code generation.
Teams using generate-subsystem-skills should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/generate-subsystem-skills/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How generate-subsystem-skills Compares
| Feature / Agent | generate-subsystem-skills | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this 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.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
Related Guides
SKILL.md Source
# 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](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
```Related Skills
typescript-skills
Shared TypeScript best practices for Designer and Electron subsystems.
server-skills
Server-specific best practices for FastAPI, Celery, and Pydantic. Extends python-skills with framework-specific patterns.
runtime-skills
Universal Runtime best practices for PyTorch inference, Transformers models, and FastAPI serving. Covers device management, model loading, memory optimization, and performance tuning.
react-skills
React 18 patterns for LlamaFarm Designer. Covers components, hooks, TanStack Query, and testing.
rag-skills
RAG-specific best practices for LlamaIndex, ChromaDB, and Celery workers. Covers ingestion, retrieval, embeddings, and performance.
python-skills
Shared Python best practices for LlamaFarm. Covers patterns, async, typing, testing, error handling, and security.
go-skills
Shared Go best practices for LlamaFarm CLI. Covers idiomatic patterns, error handling, and testing.
config-skills
Configuration module patterns for LlamaFarm. Covers Pydantic v2 models, JSONSchema generation, YAML processing, and validation.
common-skills
Best practices for the Common utilities package in LlamaFarm. Covers HuggingFace Hub integration, GGUF model management, and shared utilities.
cli-skills
CLI best practices for LlamaFarm. Covers Cobra, Bubbletea, Lipgloss patterns for Go CLI development.
electron-skills
Electron patterns for LlamaFarm Desktop. Covers main/renderer processes, IPC, security, and packaging.
designer-skills
Designer subsystem patterns for LlamaFarm. Covers React 18, TanStack Query, TailwindCSS, and Radix UI.