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Optimizing ToolUniverse Skills
Best practices for high-quality research skills with evidence grading and source attribution.
Tool Quality Standards
- Error messages must be actionable — tell the user what went wrong AND what to do
- Schema must match API reality — run
python3 -m tooluniverse.cli run <Tool> '<json>' to verify
- Coverage transparency — state what data is NOT included
- Input validation before API calls — don't silently send invalid values
- Cross-tool routing — name the correct tool when query is out-of-scope
- No silent parameter dropping — if a parameter is ignored, say so
Core Principles (13 Patterns)
Full details: references/optimization-patterns.md
| Pattern | Key Idea
---|---|---
1 | Tool Interface Verification | get_tool_info() before first call; maintain corrections table
2 | Foundation Data Layer | Query aggregator (Open Targets, PubChem) FIRST
3 | Versioned Identifiers | Capture both ENSG00000123456 and .12 version
4 | Disambiguation First | Resolve IDs, detect collisions, build negative filters
5 | Report-Only Output | Narrative in report; methodology in appendix only if asked
6 | Evidence Grading | T1 (mechanistic) → T2 (functional) → T3 (association) → T4 (mention)
7 | Quantified Completeness | Numeric minimums per section (>=20 PPIs, top 10 tissues)
8 | Mandatory Checklist | All sections exist, even if "Limited evidence"
9 | Aggregated Data Gaps | Single section consolidating all missing data
10 | Query Strategy | High-precision seeds → citation expansion → collision-filtered broad
11 | Tool Failure Handling | Primary → Fallback 1 → Fallback 2 → document unavailable
12 | Scalable Output | Narrative report + JSON/CSV bibliography
13 | Synthesis Sections | Biological model + testable hypotheses, not just paper lists
Optimized Skill Workflow
Phase -1: Tool Verification (check params)
Phase 0: Foundation Data (aggregator query)
Phase 1: Disambiguation (IDs, collisions, baseline)
Phase 2: Specialized Queries (fill gaps)
Phase 3: Report Synthesis (evidence-graded narrative)
Testing Standards
Full details: references/testing-standards.md
Critical rule : NEVER write skill docs without testing all tool calls first.
- 30+ tests per skill, 100% pass rate
- All tests use real data (no placeholders)
- Phase + integration + edge case tests
- SOAP tools (IMGT, SAbDab, TheraSAbDab) need
operation parameter
- Distinguish transient errors (retry) from real bugs (fix)
- API docs are often wrong — always verify with actual calls
Common Anti-Patterns
| Anti-Pattern | Fix |
|---|
| "Search Log" reports | Keep methodology internal; report findings only |
| Missing disambiguation | Add collision detection; build negative filters |
| No evidence grading | Apply T1-T4 grades; label each claim |
| Empty sections omitted | Include with "None identified" |
| No synthesis | Add biological model + hypotheses |
| Silent failures | Document in Data Gaps; implement fallbacks |
| Wrong tool parameters | Verify via get_tool_info() before calling |
| GTEx returns nothing | Try versioned ID ENSG*.version |
| No foundation layer | Query aggregator first |
| Untested tool calls | Test-driven: test script FIRST |
Quick Fixes for User Complaints
| Complaint | Fix |
|---|
| "Report too short" | Add Phase 0 foundation + Phase 1 disambiguation |
| "Too much noise" | Add collision filtering |
| "Can't tell what's important" | Add T1-T4 evidence tiers |
| "Missing sections" | Add mandatory checklist with minimums |
| "Too long/unreadable" | Separate narrative from JSON |
| "Just a list of papers" | Add synthesis sections |
| "Tool failed, no data" | Add retry + fallback chains |
Skill Template
---
name: [domain]-research
description: [What + when triggers]
---
# [Domain] Research
## Workflow
Phase -1: Tool Verification → Phase 0: Foundation → Phase 1: Disambiguate
→ Phase 2: Search → Phase 3: Report
## Phase -1: Tool Verification
[Parameter corrections table]
## Phase 0: Foundation Data
[Aggregator query]
## Phase 1: Disambiguation
[IDs, collisions, baseline]
## Phase 2: Specialized Queries
[Query strategy, fallbacks]
## Phase 3: Report Synthesis
[Evidence grading, mandatory sections]
## Output Files
- [topic]_report.md, [topic]_bibliography.json
## Quantified Minimums
[Numbers per section]
## Completeness Checklist
[Required sections with checkboxes]
Additional References
Weekly Installs
151
Repository
mims-harvard/to…universe
GitHub Stars
1.2K
First Seen
Feb 4, 2026
Security Audits
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Installed on
codex146
opencode145
gemini-cli141
github-copilot139
amp134
kimi-cli133
