/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.
$ npx -y skills add llama-farm/llamafarm --skill generate-subsystem-skills --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
- Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
- You can call itInvoke it directly when you want it.
- Slash command
/generate-subsystem-skills
Context preview
The summary Claude sees to decide when to auto-load this skill.
Generate specialized skills for each subsystem in the monorepo. Creates shared language skills and subsystem-specific checklists for high-quality AI code generation.
SKILL.md
generate-subsystem-skills.SKILL.mdname: generate-subsystem-skills
description: Generate specialized skills for each subsystem in the monorepo. Creates shared language skills and subsystem-specific checklists for high-quality AI code generation.
allowed-tools: Read, Grep, Glob, Write, Edit, Task, Bash
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 levelFor 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
Read more
name: generate-subsystem-skills description: Generate specialized skills for each subsystem in the monorepo. Creates shared language skills and subsystem-specific checklists for high-quality AI code generation. allowed-tools: Read, Grep, Glob, Write, Edit, Task, Bash
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 levelFor 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
Enterprise AI capabilities on your own hardware. No cloud required. LlamaFarm is an open-source AI platform that runs entirely on your hardware.
Repo: llama-farm/llamafarm
Other skills on llamafarm.
- /cli-skills
CLI best practices for LlamaFarm. Covers Cobra, Bubbletea, Lipgloss patterns for Go CLI development.
Open skill - /code-review
Comprehensive code review for diffs. Analyzes changed code for security vulnerabilities, anti-patterns, and quality issues. Auto-detects domain (frontend/backend) from file paths.
Open skill - /commit-push-pr
Commit changes, push to GitHub, and open a PR. Includes quality checks (security, patterns, simplification). Use --quick to skip checks.
Open skill - /common-skills
Best practices for the Common utilities package in LlamaFarm. Covers HuggingFace Hub integration, GGUF model management, and shared utilities.
Open skill - /config-skills
Configuration module patterns for LlamaFarm. Covers Pydantic v2 models, JSONSchema generation, YAML processing, and validation.
Open skill - /designer-skills
Designer subsystem patterns for LlamaFarm. Covers React 18, TanStack Query, TailwindCSS, and Radix UI.
Open skill

