본문으로 건너뛰기
cccskills
무료GitHub에 공개

llm-provider-knowledge

Before stating any specific fact about an LLM provider's product — models, pricing, limits, context windows, SDK/API — for OpenAI, Gemini, Claude & others, verify against official docs, not memory.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

연동 도구OpenAIGemini
설치 방법 보기

포함된 파일(2)

  • SKILL.md7.3 KB
  • evals/triggers.json2.2 KB

SKILL.md(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

llm-provider-knowledge

Verify LLM-provider product facts against official documentation, never memory. This skill is the multi-provider sibling of Claude Code's bundled product-self-knowledge — extended to OpenAI, Google Gemini, Anthropic, Mistral, xAI, DeepSeek, Cohere, and Meta Llama, and portable to every host this package projects to (not just Claude Code).

This skill is a signpost, not a library. It routes you to the authoritative source and forces a source-cited answer. It does not cache model IDs, context windows, prices, or rate limits — those churn constantly, and a cached copy is exactly the stale "from memory" answer this skill exists to prevent.

When to use

  • Your reply would state a specific fact about a provider's product: a model ID or its context window, token pricing, a rate/quota limit, an SDK/API detail (endpoint, parameter, auth, batch, streaming, function-calling shape), or a consumer-app plan limit (ChatGPT/Gemini/Claude app tiers).
  • Coding against a provider SDK where a wrong model name, parameter, or limit would break at runtime.
  • Content or comparisons that assert provider capabilities or pricing.
  • Any time you would otherwise answer such a fact from training data — it may be outdated or wrong.

When NOT to fire

  • Ordinary SDK code that asserts no product fact (wiring a call whose model and params the user already gave).
  • The user already supplied the verified fact — use it; don't re-litigate.
  • Writing provider-specific prompt grammar → prompt-engineering-patterns.
  • Choosing which model to use for the host → model-recommendation (never recommend another vendor's model over the host's; this skill only reports facts, it does not steer model choice).
  • Image-provider selection → image-provider-routing.

Core principles

  1. Accuracy over guessing — if unsure, route to the docs; never assert.
  2. Distinguish products — a provider's API, its developer platform, and its consumer app are separate surfaces with separate facts and separate docs.
  3. Source everything — every product fact in the reply carries an official URL. No URL → not verified → don't state it as fact.
  4. Route, don't cache — hand off to the live docs; do not transcribe volatile specs into the reply as if durable.

Provider routing table

Route to the stable root and read live from there — do not deep-link to pages that churn. Prefer a provider's machine-readable index where one exists.

ProviderDocs rootAPI referenceProduct / support
OpenAI (GPT)platform.openai.com/docsplatform.openai.com/docs/api-referencehelp.openai.com
Google Geminiai.google.dev/gemini-api/docsai.google.dev/apisupport.google.com (Gemini app)
Anthropic (Claude)docs.claude.com/en/docs (index: /en/docs_site_map.md)docs.claude.com/en/api/overviewsupport.claude.com
Mistraldocs.mistral.aidocs.mistral.ai/apihelp.mistral.ai
xAI (Grok)docs.x.aidocs.x.ai/developersx.ai
DeepSeekapi-docs.deepseek.comapi-docs.deepseek.com/apiplatform.deepseek.com
Coheredocs.cohere.com (index: docs.cohere.com/llms.txt)docs.cohere.com/referencedashboard.cohere.com
Meta Llamallama.com (dev docs: ai.developer.meta.com/docs)ai.developer.meta.com/docsllama.com

Claude Code specifics (install, Node.js requirement, MCP, config): docs.anthropic.com/en/docs/claude-code/claude_code_docs_map.md.

Hosted access (not a distinct vendor): Azure OpenAI, AWS Bedrock, and Google Vertex AI resell the underlying vendor's models. Route to BOTH the underlying vendor's row above AND the cloud's own docs (learn.microsoft.com, docs.aws.amazon.com/bedrock, cloud.google.com/vertex-ai) — model IDs, quotas, and regions differ from the vendor's direct API.

Procedure

  1. Identify the provider and the surface — API / developer platform / consumer app. A ChatGPT-Plus limit is not an OpenAI-API rate limit.
  2. Pick the row + column from the table; go to the stable root.
  3. Read the live docs for the exact fact (navigate from the root; follow the provider's own index / llms.txt where present).
  4. State the fact with its source URL. If the docs can't be reached or are ambiguous, say so and point the user at the root rather than guessing.
  5. Never transcribe a volatile spec as durable — frame it as "per <URL> as of now"; the source is authoritative, the reply is a pointer.

Output format

Every reply that states a provider product fact MUST include:

  1. The fact, scoped to the product surface — name the provider AND which surface (API / platform / consumer app) it applies to.
  2. The official source URL — the specific docs page the fact came from (or the stable root when you're directing the user to read it themselves).
  3. A freshness caveat when the fact is volatile (pricing, limits, model availability): "verify at <URL> — these change without notice."

Do NOT

  • Do NOT state a model ID, context window, price, or rate limit from memory — route to the docs and cite the URL, or say you're unsure.
  • Do NOT transcribe a volatile spec (pricing, limits, model availability) into the reply as if durable — frame it as "per <URL> as of now".
  • Do NOT conflate a provider's API with its consumer app — they carry different limits and different docs.
  • Do NOT steer the user to another vendor's model over the host's — that is model-recommendation; this skill reports facts neutrally.
  • Do NOT cache a provider's docs into this skill. When a root URL moves, fix the one table row; a mismatch a user reports is a signal to correct the row, never to start transcribing pages here.

Gotcha

  • Provider docs URLs churn (Meta's Llama dev-docs host redirected during this skill's authoring). That is why the skill links to stable roots and reads live — a deep-linked page memorised here would rot. If a root itself moves, fix the one table row; never start caching pages to compensate.
  • Do not conflate surfaces. "Gemini" the app and the Gemini API have different limits and different docs; the same for ChatGPT vs the OpenAI API and Claude.ai vs the Claude API.
  • Redundant with the harness on Claude Code. Claude Code's own product-self-knowledge also fires for Anthropic facts — both route to the same Anthropic docs, so the overlap is harmless. On every other host this is the only such skill.

리뷰

아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.

댓글

로그인 없이 쓸 수 있습니다. 사용 후기, 질문, 작동하지 않은 환경 등을 편하게 남겨 주세요.

아직 댓글이 없습니다. 첫 댓글을 남겨 보세요.

보내면 바로 공개됩니다. 이메일 주소나 전화번호 같은 개인 정보는 쓰지 마세요. URL은 2개까지입니다.

0 / 1,000자

이 양식은 reCAPTCHA로 보호되며 Google 개인정보처리방침 및 서비스 약관이 적용됩니다.

같은 저장소의 스킬

Use when reviewing UI for accessibility — WCAG 2.2 AA, keyboard nav, focus, ARIA, contrast, screen-reader semantics — even on 'is this a11y-OK?' or 'mach das barrierefrei'.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

event4u-app/agent-config122026년 10월 19일 업데이트

Use when defining or auditing the activation event — aha-moment selection, retention correlation, falsifiable definition. Triggers on 'what is our aha moment', 'redefine activation'.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

event4u-app/agent-config122026년 10월 19일 업데이트

Use when capturing an architectural decision — file naming, next ADR number, Status / Context / Decision / Consequences, index regen; fires even without saying 'ADR'.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

event4u-app/agent-config122026년 10월 19일 업데이트

Adversarial critique — devil's advocate, stress-test, honest teardown ('poke holes', 'be brutal', 'was hältst du davon'); explicit request only. Routine code or design review → code-review.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

event4u-app/agent-config122026년 10월 19일 업데이트

Use when reading, creating, or updating agent documentation, module docs, roadmaps, or AGENTS.md. Understands the full .augment/, agents/, and copilot-instructions structure.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

event4u-app/agent-config122026년 10월 19일 업데이트

Use for an adversarial red-team / blue-team / auditor review of an AI agent's CONFIG + behaviour (rules, skills, MCP, hooks, permissions) — attack-chain → defensive-gap list, not a code audit.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

event4u-app/agent-config122026년 10월 19일 업데이트

event4u-app의 스킬 모두 보기

이 스킬의 문제 신고