cli-skills
CLI best practices for LlamaFarm. Covers Cobra, Bubbletea, Lipgloss patterns for Go CLI development.
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.
/generate-subsystem-skillsContext 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.
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
This skill analyzes each subsystem in the LlamaFarm monorepo and generates specialized Claude Code skills for security, performance, and language-specific best practices.
/generate-subsystem-skills
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Load subsystem definitions from [subsystem-registry.md](subsystem-registry.md).
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/`
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/`
After all agents complete, report:
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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 levelYou 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.---
**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.
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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
CLI best practices for LlamaFarm. Covers Cobra, Bubbletea, Lipgloss patterns for Go CLI development.
Comprehensive code review for diffs. Analyzes changed code for security vulnerabilities, anti-patterns, and quality issues. Auto-detects domain…
Commit changes, push to GitHub, and open a PR. Includes quality checks (security, patterns, simplification). Use --quick to skip checks.
Best practices for the Common utilities package in LlamaFarm. Covers HuggingFace Hub integration, GGUF model management, and shared utilities.
Configuration module patterns for LlamaFarm. Covers Pydantic v2 models, JSONSchema generation, YAML processing, and validation.
Designer subsystem patterns for LlamaFarm. Covers React 18, TanStack Query, TailwindCSS, and Radix UI.