LLM Provider Knowledge

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• Any time you would otherwise answer such a fact from training data — it may be outdated or wrong.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.

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 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 — 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 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.