devtu-auto-discover-apis
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 workflow management. Use when expanding ToolUniverse coverage, adding new API integrations, or systematically discovering scientific resources.
What this skill does
# Automated Life Science API Discovery & Tool Creation
Discover, create, validate, and integrate life science APIs into ToolUniverse.
## Four-Phase Workflow
```
Gap Analysis → API Discovery → Tool Creation → Validation → Integration
↓ ↓ ↓ ↓ ↓
Coverage Web Search devtu-create devtu-fix Git PR
```
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)
Related in Backend & APIs
jfrog
IncludedInteract with the JFrog Platform via the JFrog CLI and REST/GraphQL APIs. Use this skill when the user wants to manage Artifactory repositories, upload or download artifacts, manage builds, configure permissions, manage users and groups, work with access tokens, configure JFrog CLI servers, search artifacts, manage properties, set up replication, manage JFrog Projects, run security audits or scans, look up CVE details, query exposures scan results from JFrog Advanced Security, manage release bundles and lifecycle operations, aggregate or export platform data, or perform any JFrog Platform administration task. Also use when the user mentions jf, jfrog, artifactory, xray, distribution, evidence, apptrust, onemodel, graphql, workers, mission control, curation, advanced security, exposures, or any JFrog product name.
cupynumeric-migration-readiness
IncludedPre-migration readiness assessor for porting NumPy to cuPyNumeric. Use BEFORE substantial porting work begins when the user asks whether code will scale on GPU, whether they should migrate to cuPyNumeric, which NumPy patterns transfer cleanly, what must be refactored before porting, or mentions pre-port assessment, scaling analysis, or refactor planning. Inspect the user's source code, look up NumPy usage, cross-reference the cuPyNumeric API support manifest, and distinguish distributed-scaling-friendly patterns from blockers such as unsupported APIs, scalar synchronization, host round-trips, Python/object-heavy control flow, shape/data-dependent branching, and in-place mutation hazards. Produce a verdict of READY, LIGHT REFACTOR, SIGNIFICANT REFACTOR, or NOT RECOMMENDED, with concrete refactor pointers.
alibabacloud-data-agent-skill
IncludedInvoke Alibaba Cloud Apsara Data Agent for Analytics via CLI to perform natural language-driven data analysis on enterprise databases. Data Agent for Analytics is an intelligent data analysis agent developed by Alibaba Cloud Database team for enterprise users. It automatically completes requirement analysis, data understanding, analysis insights, and report generation based on natural language descriptions. This tool supports: discovering data resources (instances/databases/tables) managed in DMS, initiating query or deep analysis sessions, real-time progress tracking, and retrieving analysis conclusions and generated reports. Use this Skill when users need to query databases, analyze data trends, generate data reports, ask questions in natural language, or mention "Data Agent", "data analysis", "database query", "SQL analysis", "data insights".
token-optimizer
IncludedReduce OpenClaw token usage and API costs through smart model routing, heartbeat optimization, budget tracking, and native 2026.2.15 features (session pruning, bootstrap size limits, cache TTL alignment). Use when token costs are high, API rate limits are being hit, or hosting multiple agents at scale. The 4 executable scripts (context_optimizer, model_router, heartbeat_optimizer, token_tracker) are local-only — no network requests, no subprocess calls, no system modifications. Reference files (PROVIDERS.md, config-patches.json) document optional multi-provider strategies that require external API keys and network access if you choose to use them. See SECURITY.md for full breakdown.
resend-cli
IncludedUse this skill when the task is specifically about operating Resend from an AI agent, terminal session, or CI job via the official resend CLI: installing/authenticating the CLI, sending/listing/updating/cancelling emails, batch sends, domains and DNS, webhooks and local listeners, inbound receiving, contacts, topics, segments, broadcasts, templates, API keys, profiles, or debugging Resend CLI/API failures. Trigger on mentions of Resend CLI, `resend`, `resend doctor`, `resend emails send`, `resend domains`, `resend webhooks listen`, `resend emails receiving`, or agent-friendly terminal automation.
alibabacloud-odps-maxframe-coding
IncludedUse this skill for MaxFrame SDK development and documentation navigation on Alibaba Cloud MaxCompute (ODPS). Helps answer MaxFrame API, concept, official example, and supported pandas API questions; create data processing programs; read/write MaxCompute tables; debug jobs (remote or local); and build custom DPE runtime images. Trigger when users mention MaxFrame, MaxCompute with MaxFrame, ODPS table processing, DPE runtime, MaxFrame docs/examples, DataFrame/Tensor operations, or GPU runtime setup. Works for both English and Chinese queries about Alibaba Cloud data processing with MaxFrame.