Create ToolUniverse Skill
SkillDev toolsLets your agent create well-tested ToolUniverse skills using a test-driven approach.
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the Create ToolUniverse Skill skill
About this capability
Create high-quality ToolUniverse skills following test-driven, implementation-agnostic methodology.
What this skill tells your AI
The instructions your AI receives, as published by mims-harvard/tooluniverse in skills/create-tooluniverse-skill/SKILL.md and read by ahel’s review.
Systematic workflow for creating production-ready ToolUniverse skills.
Core Principles
Build on the 10 pillars from devtu-optimize-skills:
- 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 |
| 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 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 | 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 |
Signals
- GitHub stars
- 2k
- Forks
- 254
- Last commit
- Sep 2026
Advanced
- Catalog kind
- skill
- Gateway key
create-tooluniverse-skill- Source
- github.com/mims-harvard/tooluniverse