skill-anything

# 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: ```bash 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).