create-tooluniverse-skill

$npx mdskill add mims-harvard/ToolUniverse/create-tooluniverse-skill

Create production-ready ToolUniverse skills using test-driven methodology.

  • Solves the need for systematic, high-quality skill creation.
  • Depends on ToolUniverse tools and devtu-create-tool for missing tools.
  • Follows a 7-phase workflow with 10 core principles for quality.
  • Delivers implementation-agnostic SKILL.md and tested python_implementation.py.

SKILL.md

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---
name: create-tooluniverse-skill
description: Create high-quality ToolUniverse skills following test-driven, implementation-agnostic methodology.
disable-model-invocation: true
---

# Create ToolUniverse Skill

Systematic workflow for creating production-ready ToolUniverse skills.

## Core Principles

Build on the 10 pillars from `devtu-optimize-skills`:
1. TEST FIRST - never document untested tools
2. Verify tool contracts - don't trust function names
3. Handle SOAP tools - add `operation` parameter
4. Implementation-agnostic docs - no Python/MCP code in SKILL.md
5. Foundation first - query aggregators before specialized tools
6. Disambiguate carefully - resolve IDs properly
7. Implement fallbacks - Primary -> Fallback -> Default
8. Grade evidence - T1-T4 tiers on claims
9. Quantified completeness - numeric minimums per section
10. 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 |
| 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` when required functionality is missing and analysis is blocked. Use `devtu-fix-tool` 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 writing
- `test_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** | Critical functionality missing |
| **devtu-fix-tool** | Tool returns errors or unexpected format |
| **devtu-optimize-skills** | 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 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 |

More from mims-harvard/ToolUniverse

SkillDescription
devtu-auto-discover-apisAutomatically discover life science APIs online, create ToolUniverse tools, validate them, and prepare integration PRs. Performs gap analysis to identify missing tool categories, web searches for APIs, automated tool creation using devtu-create-tool patterns, validation with devtu-fix-tool, and git workflow management. Use when expanding ToolUniverse coverage, adding new API integrations, or systematically discovering scientific resources.
devtu-benchmark-harnessContinuous improvement system for ToolUniverse tools, skills, and plugin. Run benchmarks, diagnose failures, route fixes to devtu skills, retest. Use after skill optimization, tool additions, or as regression check.
devtu-code-optimization>
devtu-create-toolCreate new scientific tools for ToolUniverse framework with proper structure, validation, and testing. Use when users need to add tools to ToolUniverse, implement new API integrations, create tool wrappers for scientific databases/services, expand ToolUniverse capabilities, or follow ToolUniverse contribution guidelines. Supports creating tool classes, JSON configurations, validation, error handling, and test examples.
devtu-docs-qualityTOP PRIORITY skill — find and immediately fix or remove every piece of wrong, outdated, or redundant information in ToolUniverse docs. Wrong code, broken links, incorrect counts, and overlapping instructions must be fixed or removed — never left in place. Runs five phases: (D) static method scan, (C) live code execution, (A) automated validation, (B) ToolUniverse audit, (E) less-is-more simplification. Core philosophy: each concept appears exactly once; remove don't add; no emojis; single setup entry point. Use when reviewing docs, before releases, after API changes, or when asked to audit, fix, or simplify documentation.
devtu-fix-toolFix failing ToolUniverse tools by diagnosing test failures, identifying root causes, implementing fixes, and validating solutions. Use when ToolUniverse tools fail tests, return errors, have schema validation issues, or when asked to debug or fix tools in the ToolUniverse framework.
devtu-githubGitHub workflow for ToolUniverse - push code safely by moving temp files, activating pre-commit hooks, running tests, and cleaning staged files. Use when pushing to GitHub, fixing CI failures, or cleaning up before commits.
devtu-optimize-descriptionsOptimize tool descriptions in ToolUniverse JSON configs for clarity and usability. Reviews descriptions for missing prerequisites, unexpanded abbreviations, unclear parameters, and missing usage guidance. Use when reviewing tool descriptions, improving API documentation, or when user asks to check if tools are easy to understand.
devtu-optimize-skillsOptimize ToolUniverse skills for better report quality, evidence handling, and user experience. Apply patterns like tool verification, foundation data layers, disambiguation-first, evidence grading, quantified completeness, and report-only output. Use when reviewing skills, improving existing skills, or creating new ToolUniverse research skills.
devtu-self-evolve>