create-tooluniverse-skill
Create high-quality ToolUniverse skills following test-driven, implementation-agnostic methodology. Integrates tools from ToolUniverse's 1,264+ tool library, creates missing tools when needed, tests thoroughly, and produces skills with Python SDK + MCP support.
- License 许可
- MIT
- 最近更新
- 2026-11-02
- Stars
- ★ 0
- Forks
- 0
01 前言 Front Matter
- name
- create-tooluniverse-skill
- description
- Create high-quality ToolUniverse skills following test-driven, implementation-agnostic methodology. Integrates tools from ToolUniverse's 1,264+ tool library, creates missing tools when needed, tests thoroughly, and produces skills with Python SDK + MCP support.
02 资源文件 14
- 参考文档CODE_TEMPLATES.md4.5 KB
- 参考文档OPTIMIZE_INTEGRATION.md3.3 KB
- 参考文档PACKAGING_TEMPLATE.md1.1 KB
- 参考文档PARAMETER_VERIFICATION.md1.1 KB
- 参考文档QUICKSTART_TEMPLATE.md1.6 KB
- 参考文档references/devtu_optimize_integration.md8.5 KB
- 参考文档references/implementation_agnostic_format.md6.3 KB
- 参考文档references/skill_standards_checklist.md7.1 KB
- 参考文档references/tool_testing_workflow.md5.3 KB
- 脚本scripts/test_tools_template.py5.7 KB
- 参考文档SKILL_TEMPLATE.md2.3 KB
- 脚本test_tools_template.py5.7 KB
- 参考文档TESTING_GUIDE.md6.3 KB
- 参考文档VALIDATION_CHECKLIST.md1.4 KB
03 SKILL.md 正文
Create ToolUniverse Skill
Systematic workflow for creating production-ready ToolUniverse skills.
Core Principles
Build on the 10 pillars from devtu (optimize-skills mode):
- TEST FIRST - never document untested tools
- Verify tool contracts - don't trust function names
- Handle SOAP tools - add
operationparameter - Implementation-agnostic docs - no Python/MCP code in SKILL.md
- Foundation first - query aggregators before specialized tools
- Disambiguate carefully - resolve IDs properly
- Implement fallbacks - Primary -> Fallback -> Default
- Grade evidence - T1-T4 tiers on claims
- Quantified completeness - numeric minimums per section
- Synthesize - models and hypotheses, not just lists
See OPTIMIZE_INTEGRATION.md for detailed application of each pillar.
7-Phase Workflow
| Phase | Duration | Description |
|---|---|---|
| 1. Domain Analysis | 15 min | Understand use cases, data types, analysis phases |
| 2. Tool Discovery | 30-45 min | Search, read configs, test tools (MANDATORY) |
| 3. Tool Creation | 0-60 min | Create missing tools via devtu (create-tool mode) |
| 4. Implementation | 30-45 min | Write python_implementation.py with tested tools |
| 5. Documentation | 30-45 min | Write SKILL.md (agnostic) + QUICK_START.md |
| 6. Validation | 15-30 min | Run test suite, validate checklist, manual verify |
| 7. Packaging | 15 min | Create summary, update tracking |
Total: ~1.5-2 hours (without tool creation).
Phase 1: Domain Analysis
- Gather concrete use cases and expected outputs
- Identify inputs, outputs, and intermediate data types
- Break workflow into logical phases
- Review existing skills in
skills/for patterns
Phase 2: Tool Discovery and Testing
Search tools in /src/tooluniverse/data/*.json (186 tool files). For each tool, read its config to understand parameters and return schema. See PARAMETER_VERIFICATION.md for common pitfalls.
Create and run a test script using test_tools_template.py. For each tool: call with known-good params, verify response format, document corrections. See TESTING_GUIDE.md for the full test suite template and procedures.
Phase 3: Tool Creation (If Needed)
Invoke devtu (create-tool mode) when required functionality is missing and analysis is blocked. Use devtu (fix-tool mode) if new tools fail tests.
Phase 4: Implementation
Create skills/tooluniverse-[domain]/ with:
python_implementation.py- use only tested tools, try/except per phase, progressive report writingtest_skill.py- test each input type, combined inputs, error handling
Use templates from CODE_TEMPLATES.md.
Phase 5: Documentation
Write implementation-agnostic SKILL.md using SKILL_TEMPLATE.md. Write multi-implementation QUICK_START.md using QUICKSTART_TEMPLATE.md. Key rules: zero Python/MCP code in SKILL.md, equal treatment of both interfaces in QUICK_START.
See IMPLEMENTATION_AGNOSTIC.md for format guidelines with examples.
Phase 6: Validation
Run the comprehensive test suite (see TESTING_GUIDE.md). Validate against VALIDATION_CHECKLIST.md. Perform manual verification: load ToolUniverse fresh, copy-paste QUICK_START example, verify output works.
Phase 7: Packaging
Create summary document using PACKAGING_TEMPLATE.md. Update session tracking if creating multiple skills.
Skill Integration
| Skill | When to Use |
|---|---|
| devtu (create-tool mode) | Critical functionality missing |
| devtu (fix-tool mode) | Tool returns errors or unexpected format |
| devtu (optimize-skills mode) | Evidence grading, report optimization |
Quality Indicators
High quality: 100% test coverage before docs, agnostic SKILL.md, multi-implementation QUICK_START, fallback strategies, parameter corrections table, response format docs.
Red flags: Docs before testing, Python in SKILL.md, assumed parameters, no fallbacks, SOAP tools missing operation, no test script.
Reference Files
| File | Content |
|---|---|
SKILL_TEMPLATE.md |
Template for writing SKILL.md |
QUICKSTART_TEMPLATE.md |
Template for writing QUICK_START.md |
TESTING_GUIDE.md |
Test suite template and procedures |
VALIDATION_CHECKLIST.md |
Pre-release quality checklist |
PACKAGING_TEMPLATE.md |
Summary document template |
PARAMETER_VERIFICATION.md |
Tool parameter verification guide |
OPTIMIZE_INTEGRATION.md |
devtu (optimize-skills mode) 10-pillar integration |
IMPLEMENTATION_AGNOSTIC.md |
Implementation-agnostic format guide with examples |
CODE_TEMPLATES.md |
Python implementation and test templates |
test_tools_template.py |
Tool testing script template |
04 同仓库更多 Skills
clarify-requirements
Resolve genuine pre-execution ambiguity by scanning repo context, generating 1-6 grounded options, and asking 1-3 focused questions via AskUserQuestion. NOT for mid-task use (injection risk); NOT when the goal is already unambiguous; use to disambiguate vague prompts before any file edits.
openmm-md
Run FREE, open-source, Windows-native GPU molecular dynamics via the openmm-md MCP (OpenMM + CUDA on the local NVIDIA GPU). Use for protein / protein-ligand MD when you want a free stack or are on Windows without a Schrodinger license. Covers the full workflow (prepare, solvate, minimize, equilibrate, production, analyze, mmgbsa) AND an honest decision guide vs Schrodinger Desmond, including how to run Desmond legitimately on Linux or a cloud GPU with a real license. This is a deliberately LESSER free alternative and does NOT replace the schrodinger MCP tools. Do NOT use it to pick a free tool when a validated FEP+, WaterMap, IFD-MD, or Prime MM-GBSA result is actually required.
visual-review
Take screenshots of web pages and UI using Playwright for visual review and iteration
status
Diagnostic status report — shows metaswarm installation state, project setup, and potential issues
start
Use when starting work on any task, when the user mentions metaswarm, or when the user wants to begin tracked development work
setup
Interactive project setup — detects your project, configures metaswarm, writes project-local files