SkillAnything

agent
Guvenlik Denetimi
Gecti
Health Gecti
  • License — License: MIT
  • Description — Repository has a description
  • Active repo — Last push 9 days ago
  • Community trust — 149 GitHub stars
Code Gecti
  • Code scan — Scanned 12 files during light audit, no dangerous patterns found
Permissions Gecti
  • Permissions — No dangerous permissions requested
Purpose
SkillAnything is a meta-skill designed to generate production-ready AI agent skills for various platforms. You provide it with a target (like a CLI tool, API, or library), and it runs a 7-phase pipeline to analyze, design, implement, and package compatible skills automatically.

Security Assessment
The automated code scan reviewed 12 files and found no dangerous patterns, hardcoded secrets, or requests for dangerous permissions. The tool requires standard git cloning to install and executes a multi-phase pipeline, which inherently involves processing data and potentially running local commands or scripts depending on the target you specify. However, based on the light audit, the overall security risk is rated as Low.

Quality Assessment
The project demonstrates solid health and active maintenance. It is protected by a standard, permissive MIT license. The repository is actively maintained, with its most recent push occurring just 9 days ago. It also possesses a respectable level of community trust, currently backed by 149 GitHub stars, indicating that multiple developers have reviewed or found the tool useful.

Verdict
Safe to use.
SUMMARY

Making ANY Software Skill-Native -- Auto-generate production-ready AI Agent Skills for Claude Code, OpenClaw, Codex, and more.

README.md

SkillAnything
Python License Claude Code OpenClaw Codex

SkillAnything

Making ANY Software Skill-Native
The meta-skill that generates production-ready Skills for AI agent platforms.

Quick StartPipelinePlatformsArchitectureExamplesAttribution


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

# 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

# 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

Claude Code
~/.claude/skills/
OpenClaw
~/.openclaw/skills/
OpenAI Codex
~/.codex/skills/
Generic
.skill zip
Full support
Hooks in frontmatter
Full support
External settings.json
Full support
openai.yaml companion
Full support
Platform-agnostic

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:

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:

# 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.

Contributing

We welcome contributions! Areas where help is needed:

  • New target type analyzers (e.g., GraphQL, gRPC)
  • Platform adapters for additional agent frameworks
  • Evaluation improvements and test case quality
  • Documentation and examples

License

MIT License -- see LICENSE for details.


SkillAnything -- Making ANY Software Skill-Native
If CLI-Anything makes CLIs for software, SkillAnything makes Skills for everything.

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