skill-anything

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:

Setting	Default	Description
pipeline.auto_mode	true	Run all phases or pause for review
target.type	auto	Force target type: api, cli, library, workflow, service
platforms.enabled	all 4	Which platforms to package for
platforms.primary	claude-code	Primary output platform
eval.max_optimization_iterations	5	Max description optimization rounds
obfuscation.enabled	false	Obfuscate original scripts with PyArmor

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

Platform Output

Platform	Install Path	Package Format
Claude Code	~/.claude/skills/<name>/	Directory
OpenClaw	~/.openclaw/skills/<name>/	Directory
Codex	~/.codex/skills/<name>/	Directory + openai.yaml
Generic	anywhere	.skill zip

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

Script	Phase	Purpose
analyze_target.py	1	Auto-detect and analyze target
design_skill.py	2	Generate skill architecture from analysis
init_skill.py	3	Scaffold skill directory from templates
generate_tests.py	4	Auto-generate test cases and trigger queries
run_eval.py	5	Test description triggering accuracy
aggregate_benchmark.py	5	Aggregate benchmark statistics
generate_report.py	5-6	Generate HTML optimization report
improve_description.py	6	AI-powered description improvement
run_loop.py	6	Full eval + improve optimization loop
quick_validate.py	7	Validate SKILL.md structure
package_skill.py	7	Package for single platform
package_multiplatform.py	7	Package for all enabled platforms
obfuscate.py	-	PyArmor wrapper for code protection

Agents

Read these when spawning specialized subagents:

Agent	Purpose
agents/analyzer.md	Phase 1: Target analysis instructions
agents/designer.md	Phase 2: Skill architecture design
agents/implementer.md	Phase 3: Skill content writing
agents/grader.md	Phase 5: Eval assertion grading
agents/comparator.md	Phase 5: Blind A/B output comparison
agents/optimizer.md	Phase 6: Description optimization orchestration
agents/packager.md	Phase 7: Multi-platform packaging instructions

Target Types

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

Type	Detection	Analysis Method
API	URL with /api, OpenAPI spec, swagger	Fetch spec, extract endpoints
CLI	Executable name, --help output	Run help, parse subcommands
Library	Package name, import path	Read docs, parse public API
Workflow	Step descriptions, sequence	Parse steps, map data flow
Service	URL, web interface	Scrape docs, identify actions

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).