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: Test cases with assertions → graded by agents/grader.md Benchmark comparing with-skill vs baseline → benchmark.json Description optimization with train/test split → prevents overfitting 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).