Systematic workflow for creating production-ready ToolUniverse skills.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versioncreate-tooluniverse-skillExecute the skills CLI command in your project's root directory to begin installation:
Fetches create-tooluniverse-skill from mims-harvard/tooluniverse and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate create-tooluniverse-skill. Access via /create-tooluniverse-skill in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
Submit your Claude Code skill and start earning
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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Systematic workflow for creating production-ready ToolUniverse skills.
Build on the 10 pillars from devtu-optimize-skills:
operation parameterSee OPTIMIZE_INTEGRATION.md for detailed application of each pillar.
| 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).
skills/ for patternsSearch 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.
Invoke devtu-create-tool when required functionality is missing and analysis is blocked. Use devtu-fix-tool if new tools fail tests.
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 handlingUse templates from CODE_TEMPLATES.md.
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.
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.
Create summary document using PACKAGING_TEMPLATE.md. Update session tracking if creating multiple skills.
| 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 |
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.
| 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 |
Make data-driven prioritization decisions faster
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
create-tooluniverse-skill has been reliable in day-to-day use. Documentation quality is above average for community skills.
Keeps context tight: create-tooluniverse-skill is the kind of skill you can hand to a new teammate without a long onboarding doc.
Keeps context tight: create-tooluniverse-skill is the kind of skill you can hand to a new teammate without a long onboarding doc.
create-tooluniverse-skill reduced setup friction for our internal harness; good balance of opinion and flexibility.
I recommend create-tooluniverse-skill for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
create-tooluniverse-skill fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
create-tooluniverse-skill fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
We added create-tooluniverse-skill from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Useful defaults in create-tooluniverse-skill — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
Registry listing for create-tooluniverse-skill matched our evaluation — installs cleanly and behaves as described in the markdown.
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