name: skill-anything description: > Automatically generate production-ready Skills for any software, API, CLI tool, library, workflow, or service. Use this skill whenever the user wants to create a skill from scratch for a target application, convert an existing tool into an agent-native skill, generate skills for multiple platforms (Claude Code, OpenClaw, Codex), or automate the full skill creation pipeline including analysis, design, implementation, testing, optimization, and multi-platform packaging. Also use when the user mentions "skill-anything", "generate a skill for", "make a skill from", "skillify", or wants to turn any software into an agent-ready skill. Even if they just say "create a skill for X" where X is any tool or API, this skill should trigger.

SkillAnything

Automatically generate production-ready Skills for any target — software, API, CLI tool, library, workflow, or web service. SkillAnything runs a 7-phase pipeline that analyzes your target, designs the skill architecture, implements it, generates test cases, benchmarks performance, optimizes the description, and packages for multiple agent platforms.

Quick Start

Fully automated (one command):

Give SkillAnything a target and it handles everything:
- "Create a skill for the jq CLI tool"
- "Generate a skill for the Stripe API"
- "Turn this workflow into a multi-platform skill"

The pipeline runs all 7 phases automatically. Results land in sa-workspace/.

The 7-Phase Pipeline

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

See METHODOLOGY.md for the full pipeline specification.

Usage Modes

Auto Mode (default)

Runs all 7 phases end-to-end. Provide the target and SkillAnything does the rest:

Target: "the httpie CLI tool"
→ Analyzes httpie --help output, designs command structure, generates skill,
  creates tests, benchmarks, optimizes, packages for 4 platforms

Interactive Mode

Set auto_mode: false in config.yaml. SkillAnything pauses after each phase for review:

• Phase 1 → "Here's what I found about the target. Look right?"
• Phase 2 → "Here's the proposed skill architecture. Any changes?"
• Phase 3 → "Draft skill ready for review."
• ...continues with user feedback at each step

Single Phase Mode

Run any phase independently:

python -m scripts.analyze_target --target "jq" --output analysis.json
python -m scripts.design_skill --analysis analysis.json --output architecture.json
python -m scripts.init_skill my-skill --template cli --output ./out
python -m scripts.generate_tests --analysis analysis.json --skill-path ./out/my-skill
python -m scripts.run_eval --eval-set evals.json --skill-path ./out/my-skill
python -m scripts.run_loop --eval-set trigger-evals.json --skill-path ./out/my-skill --model <model>
python -m scripts.package_multiplatform ./out/my-skill --platforms claude-code,openclaw,codex

Configuration

Edit config.yaml to customize the pipeline. Key settings:

See references/schemas.md for the complete configuration schema.

Platform Output

See references/platform-formats.md for platform-specific format details.

Evaluation and Benchmarking

SkillAnything uses the same eval system as the Anthropic skill-creator:

1. Test cases with assertions → graded by agents/grader.md
2. Benchmark comparing with-skill vs baseline → benchmark.json
3. Description optimization with train/test split → prevents overfitting
4. Interactive viewer via eval-viewer/generate_review.py

The eval loop is optional (skip_eval: true in config) for rapid prototyping.

Scripts Reference

Agents

Read these when spawning specialized subagents:

Target Types

SkillAnything auto-detects the target type and adapts its analysis:

Troubleshooting

• Phase 1 fails: Target not found or inaccessible → provide --target-type override
• Low eval scores: Description too vague → run Phase 6 optimization
• Platform packaging errors: Missing required fields → check references/platform-formats.md
• PyArmor not found: Install with pip install pyarmor

License

MIT License. See NOTICE for third-party attributions (CLI-Anything, Dazhuang Skill Creator, Anthropic Skill Creator).

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:

Target Auto-Detection

Supported Platforms

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

Attribution

Built on the shoulders of giants:

| Project | License | What We Used | |---------|--------