git:20260714.ba34d94 to git:20260730.4ef14f8

0 added, 1 removed. Audit A to A.

---

model_tier: inherit

name: llm-provider-knowledge

description: "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."

- source: package

domain: process

personas: []

workspaces:

- agent-config-maintainer

- engineering

- product

- founder

- small-business

- gtm

- ops

packs:

- meta

trust:

level: experimental

install:

removable: true

execution:

type: manual

---

# 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outdated or wrong.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 <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.