Automatically 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

Automated Life Science API Discovery & Tool Creation Discover, create, validate, and integrate life science APIs into ToolUniverse. Four Phase Workflow Human approval gates after: discovery, creation, validation, and before PR. Phase 1: Discovery & Gap Analysis 1.1 Analyze Current Coverage Load ToolUniverse, categorize tools by domain (genomics, proteomics, drug discovery, clinical, omics, imaging, literature, pathways, systems biology). Count per category. 1.2 Identify Gap Domains Critical Gap : <5 tools in category Moderate Gap : 5 15 tools, missing key subcategories Emerging Gap : New technologies not represented Common gaps: single cell genomics, metabolomics, patient registries, microbial genomics, multi omics integration, synthetic biology, toxicology. 1.3 Web Search for APIs For each gap domain, run multiple queries: 1. "[domain] API REST JSON" — direct API search 2. "[domain] public database" — database discovery 3. "[domain] API 2025 OR 2026" — recent releases 4. "[domain] database" site:nar.oxfordjournals.org — NAR Database Issue Extract: base URL, endpoints, auth method, parameter schemas, rate limits. 1.4 Score and Prioritize Criterion Max Points Documentation Quality 20 API Stability 15 Authentication Simplicity 15 Coverage 15 Maintenance 10 Community 10 License 10 Rate Limits 5 High priority ( =70), Medium (50 69), Low (<50). 1.5 Generate Discovery Report Coverage analysis, prioritized candidates with scores, implementation roadmap. Phase 2: Tool Creation For each API, use Skill(skill="devtu create tool") or follow these patterns. Architecture Decision Multiple endpoints → multi operation tool (single class, multiple JSON wrappers) Single endpoint → single operation acceptable Key Steps 1. Design tool class following template — see [references/tool templates.md](references/tool templates.md) 2. Create JSON config with oneOf return schema 3. Find real test examples (use List endpoint → extract IDs → verify) 4. Register in default config.py Critical Requirements return schema MUST have oneOf (success + error schemas) test examples MUST use real IDs (NO placeholders) Tool name <= 55 characters NEVER raise exceptions in run() — return error dict Set timeout on all HTTP requests (30s) Phase 3: Validation Full guide: [references/validation guide.md](references/validation guide.md) Quick Validation Checklist 1. Schema : oneOf structure, data wrapper, error field 2. Placeholders : No TEST/DUMMY/PLACEHOLDER in test examples 3. Loading : 3 step check (class registered, config registered, wrappers generated) 4. Integration tests : python scripts/test new tools.py [api name] v → 100% pass Fix failures with Skill(skill="devtu fix tool") . Phase 4: Integration Use Skill(skill="devtu github") or: 1. Create branch: feature/add [api name] tools 2. Stage tool files + default config.py 3. Commit with descriptive message 4. Push and create PR with validation results Processing Patterns Pattern When to Use Batch (multiple APIs → single PR) Same domain, similar structure Iterative (one API at a time) Complex auth, novel patterns Discovery only (report, no tools) Planning roadmap Validation only (audit existing) PR review, quality check References Tool templates (Python class + JSON config): [references/tool templates.md](references/tool templates.md) Validation & integration guide : [references/validation guide.md](references/validation guide.md)

devtu-auto-discover-apis