AgentSkillOS/SkillAnything — Gitpedia

<img src="https://img.shields.io/badge/SkillAnything-v1.0.0-00d4aa?style=for-the-badge&logo=data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIyNCIgaGVpZ2h0PSIyNCIgdmlld0JveD0iMCAwIDI0IDI0IiBmaWxsPSJub25lIiBzdHJva2U9IndoaXRlIiBzdHJva2Utd2lkdGg9IjIiPjxwYXRoIGQ9Ik0xMiAyTDIgN2wxMCA1IDEwLTV6Ii8+PHBhdGggZD0iTTIgMTdsMTAgNSAxMC01Ii8+PHBhdGggZD0iTTIgMTJsMTAgNSAxMC01Ii8+PC9zdmc+" alt="SkillAnything"/> <img src="https://img.shields.io/badge/Python-3.9+-3776AB?style=flat-square&logo=python&logoColor=white" alt="Python"/> <img src="https://img.shields.io/badge/License-MIT-green?style=flat-square" alt="License"/> <img src="https://img.shields.io/badge/Claude_Code-Compatible-7C3AED?style=flat-square" alt="Claude Code"/> <img src="https://img.shields.io/badge/OpenClaw-Compatible-FF6B35?style=flat-square" alt="OpenClaw"/> <img src="https://img.shields.io/badge/Codex-Compatible-10A37F?style=flat-square" alt="Codex"/> <h1 align="center">SkillAnything</h1> Making ANY Software Skill-Native The meta-skill that generates production-ready Skills for AI agent platforms. <a href="#quick-start">Quick Start</a> • <a href="#the-7-phase-pipeline">Pipeline</a> • <a href="#supported-platforms">Platforms</a> • <a href="#architecture">Architecture</a> • <a href="#examples">Examples</a> • <a href="#attribution">Attribution</a>

What is SkillAnything?

One target in, production-ready Skills out.

SkillAnything is a Skill that generates Skills. Give it any target -- a CLI tool, REST API, Python library, workflow, or web service -- and it runs a fully automated 7-phase pipeline:

Target: "jq"
  |
  v
[Analyze] -> [Design] -> [Implement] -> [Test] -> [Benchmark] -> [Optimize] -> [Package]
  |                                                                                  |
  v                                                                                  v
analysis.json                                                          dist/
                                                                        ├── claude-code/
                                                                        ├── openclaw/
                                                                        ├── codex/
                                                                        └── generic/

No manual prompt engineering. No copy-paste between platforms. Just tell it what you want a skill for.

Quick Start

Install

bash
# Claude Code
git clone https://github.com/AgentSkillOS/SkillAnything.git ~/.claude/skills/skill-anything

# OpenClaw
git clone https://github.com/AgentSkillOS/SkillAnything.git ~/.openclaw/skills/skill-anything

# Codex
git clone https://github.com/AgentSkillOS/SkillAnything.git ~/.codex/skills/skill-anything

Use

In Claude Code, just say:

> Create a skill for the httpie CLI tool
> Generate a multi-platform skill for the Stripe API
> Turn this data pipeline workflow into a skill

SkillAnything handles the rest.

Run Individual Phases

bash
# Phase 1: Analyze a target
python -m scripts.analyze_target --target "jq" --output analysis.json

# Phase 2: Design architecture
python -m scripts.design_skill --analysis analysis.json --output architecture.json

# Phase 3: Scaffold skill
python -m scripts.init_skill my-skill --template cli --output ./out

# Phase 4: Generate test cases
python -m scripts.generate_tests --analysis analysis.json --skill-path ./out/my-skill

# Phase 5: Run evaluation
python -m scripts.run_eval --eval-set evals.json --skill-path ./out/my-skill

# Phase 6: Optimize description
python -m scripts.run_loop --eval-set trigger-evals.json --skill-path ./out/my-skill --model claude-sonnet-4-20250514

# Phase 7: Package for all platforms
python -m scripts.package_multiplatform ./out/my-skill --platforms claude-code,openclaw,codex

The 7-Phase Pipeline

Inspired by CLI-Anything's methodology, adapted for Skill generation:

Phase	Name	What It Does	Output
1	Analyze	Auto-detect target type, extract capabilities	`analysis.json`
2	Design	Map capabilities to skill architecture	`architecture.json`
3	Implement	Generate SKILL.md + scripts + references	Complete skill directory
4	Test Plan	Auto-generate eval cases + trigger queries	`evals.json`
5	Evaluate	Benchmark with/without skill, grade results	`benchmark.json`
6	Optimize	Improve description via train/test loop	Optimized SKILL.md
7	Package	Multi-platform distribution packages	`dist/`

Target Auto-Detection

Target Type	Detection Method	Example
CLI Tool	`which <name>` + `--help` parsing	`jq`, `httpie`, `ffmpeg`
REST API	URL with OpenAPI/Swagger spec	Stripe API, GitHub API
Library	Package name via pip/npm	`pandas`, `lodash`
Workflow	Step-by-step description	ETL pipeline, CI/CD flow
Service	URL with web docs	Slack, Notion

Supported Platforms

<table> <tr> <td align="center">Claude Code <code>~/.claude/skills/</code></td> <td align="center">OpenClaw <code>~/.openclaw/skills/</code></td> <td align="center">OpenAI Codex <code>~/.codex/skills/</code></td> <td align="center">Generic <code>.skill</code> zip</td> </tr> <tr> <td align="center">Full support Hooks in frontmatter</td> <td align="center">Full support External settings.json</td> <td align="center">Full support openai.yaml companion</td> <td align="center">Full support Platform-agnostic</td> </tr> </table>

Architecture

SkillAnything/
├── SKILL.md                    # Main entry point (< 500 lines)
├── METHODOLOGY.md              # Full 7-phase pipeline spec
├── config.yaml                 # Pipeline configuration
│
├── agents/                     # Subagent instructions
│   ├── analyzer.md             # Phase 1: Target analysis
│   ├── designer.md             # Phase 2: Skill design
│   ├── implementer.md          # Phase 3: Content writing
│   ├── grader.md               # Phase 5: Eval grading
│   ├── comparator.md           # Blind A/B comparison
│   ├── optimizer.md            # Phase 6: Description optimization
│   └── packager.md             # Phase 7: Multi-platform packaging
│
├── scripts/                    # Python automation core
│   ├── analyze_target.py       # [NEW] Target auto-detection
│   ├── design_skill.py         # [NEW] Architecture generation
│   ├── init_skill.py           # [NEW] Skill scaffolding
│   ├── generate_tests.py       # [NEW] Auto test generation
│   ├── package_multiplatform.py # [NEW] Multi-platform packaging
│   ├── obfuscate.py            # [NEW] PyArmor wrapper
│   ├── run_eval.py             # Trigger evaluation
│   ├── improve_description.py  # AI-powered optimization
│   ├── run_loop.py             # Eval + improve loop
│   ├── aggregate_benchmark.py  # Benchmark statistics
│   └── ...                     # + validators, reporters
│
├── references/                 # Documentation
│   ├── platform-formats.md     # Platform-specific specs
│   ├── schemas.md              # JSON schemas
│   └── pipeline-phases.md      # Phase details
│
├── templates/                  # Generation templates
│   ├── skill-scaffold/         # Skill directory template
│   └── platform-adapters/      # Platform-specific adapters
│
└── eval-viewer/                # Interactive eval review UI
    └── generate_review.py

Examples

Example 1: CLI Tool Skill

> Create a skill for the jq CLI tool

Phase 1: Analyzing jq... detected as CLI tool (confidence: 0.95)
Phase 2: Designing skill architecture... tool-augmentation pattern
Phase 3: Generating SKILL.md + 2 scripts + 1 reference
Phase 4: Created 5 test cases + 20 trigger queries
Phase 5: Benchmark: 87% pass rate (vs 42% baseline)
Phase 6: Description optimized: 18/20 trigger accuracy
Phase 7: Packaged for claude-code, openclaw, codex, generic

Done! Skill at: sa-workspace/dist/

Example 2: API Skill

> Generate a skill for the Stripe API, focus on payments

Phase 1: Fetching Stripe OpenAPI spec... 247 endpoints found
Phase 2: Focusing on payment_intents, customers, charges
Phase 3: Generated SKILL.md with auth setup + endpoint references
...

Example 3: Workflow Skill

> Turn this into a skill: fetch from Postgres, clean with pandas, upload to S3

Phase 1: Detected workflow with 3 steps
Phase 2: workflow-orchestrator pattern, 3 dependencies
Phase 3: Step-by-step SKILL.md with error handling guidance
...

Configuration

Edit config.yaml:

yaml
pipeline:
  auto_mode: true              # Full automation or interactive
  skip_eval: false             # Skip phases 5-6 for rapid prototyping

platforms:
  enabled: [claude-code, openclaw, codex, generic]
  primary: claude-code

eval:
  max_optimization_iterations: 5
  runs_per_query: 3

obfuscation:
  enabled: false               # PyArmor protection for core scripts

Code Protection

SkillAnything supports code obfuscation for commercial distribution:

bash
# Obfuscate original scripts (Apache 2.0 derived files are excluded)
python -m scripts.obfuscate --config config.yaml

# Output: dist-protected/ with PyArmor-protected core + readable adapted scripts

Category	Files	Protection
SkillAnything Original	6 scripts	PyArmor obfuscated
Anthropic Adapted	9 scripts	Source (Apache 2.0 requires it)
Agent Instructions	7 .md files	Readable (required by agents)

Attribution

Built on the shoulders of giants:

Project	License	What We Used
CLI-Anything	MIT	7-phase pipeline methodology
Dazhuang Skill Creator	Apache 2.0	Project structure pattern
Anthropic Skill Creator	Apache 2.0	Eval/benchmark system, agent instructions

See NOTICE for complete attribution details.