Optimizing ToolUniverse Skills

Best practices for high-quality research skills with evidence grading and source attribution.

Tool Quality Standards

1. Error messages must be actionable — tell the user what went wrong AND what to do
2. Schema must match API reality — run python3 -m tooluniverse.cli run <Tool> '<json>' to verify
3. Coverage transparency — state what data is NOT included
4. Input validation before API calls — don't silently send invalid values
5. Cross-tool routing — name the correct tool when query is out-of-scope
6. No silent parameter dropping — if a parameter is ignored, say so

Core Principles (13 Patterns)

Full details: references/optimization-patterns.md