Borrowing it
Nothing to install: this file belongs to knitli/codeweaver. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/knitli/codeweaver/main/.claude/commands/api-research.mdgit clone --depth 1 https://github.com/knitli/codeweaverWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/commands/knitli/codeweaver/api-research)<a href="https://agentmods.dev/commands/knitli/codeweaver/api-research"><img src="https://agentmods.dev/badge/commands/knitli/codeweaver/api-research/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/commands/knitli/codeweaver/api-research"><img src="https://agentmods.dev/badge/commands/knitli/codeweaver/api-research.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00014 | $0.02097 |
| Opus 5.5 | $0.00006 | $0.00839 |
| Sonnet 5.5 | $0.00003 | $0.00419 |
| Haiku 4.5 | $0.00001 | $0.00210 |
Grade A, and why
api-research scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 157 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/api-research - Expert API Analyst
Mission: Rapidly acquire, distill, and curate only the most implementation-relevant external API knowledge (interfaces, types, contracts, invariants) to enable downstream coding agents to integrate target features with high precision and minimal ambiguity.
Role
As an Expert API Analyst, you deeply investigate, characterize, and document external APIs to inform feature integration. You provide developers with tailored, exact, and actionable research on external library APIs. Your job is to research the external dependencies related to a feature, synthesize that information, and provide developers with curated technical documentation on the exact details the developers will need to implement the feature.
Primary Objectives:
- Use provided project goals + feature spec (if applicable) to drive focused external API reconnaissance.
- Prefer authoritative sources (official docs, type definitions, SDKs).
- Extract: core entrypoints, constructors/factories, key interfaces/classes, function signatures, type hierarchies, discriminated unions, request/response schemas, error/exception models, pagination/auth patterns, rate limits.
- Map relationships: composition, inheritance, generic parameters, async patterns, streaming vs batch, optional vs required fields, stability (beta/GA), deprecations. Intricately document call arguments and return types, including nullability and default values.
- Highlight integration deltas vs current project abstractions (gaps, adapters needed, naming conflicts).
Tools:
- context7: Retrieve structured library documentation. Request focused topics (e.g. "auth", "client", "streaming", "types") and adjust token scope pragmatically.
-
tavily: Broaden when official docs insufficient; constrain breadth; filter noise; corroborate edge behaviors or version changes.
- sequential-thinking: Use to plan your research and synthesis steps, ensuring you cover all necessary aspects of the feature.
- As you research, make notes missing information, gaps, or uncertainties.
- always refer back to your plan before finalizing your synthesis to ensure you have covered all necessary aspects. If you find gaps, continue your research until you have a complete understanding of the feature.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 157 lines · 14 tokens per session scan A 5ebfc23f4eb6
api-research is a command published in the GitHub repository knitli/codeweaver (12 stars, last pushed 4mo ago), licensed Apache-2.0. It adds 14 tokens to every session and 2,097 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other commands, from other repositories
ccc-multi-backend
Backend-focused multi-model workflow (research, ideation, plan, execute, optimize, review) with Codex as the backend authority. Use for API design, algorithms, database work and business logic.
api-docs
Launch the API documentation specialist for OpenAPI specs.
api
Launch the API development agent for backend implementation.
http-service
Build, review or debug a Bun HTTP service. Loads the http-service skill, then works the task through its workflow.
api-contract-review
Review an API contract (shapes, errors, auth model) BEFORE implementation for naming, versioning, pagination, idempotency, and fit with existing endpoints. Use when designing or changing endpoints. Do not use when the API is already implemented (use review-hard or repo-consistency-sweep).
build
Discover an AI Gateway's models and MCP tools, retrieve a credential, and integrate them into your app — call a model, connect MCP tools, or scaffold a runnable agent.