Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add event4u-app/agent-config --skill llm-provider-knowledgegit clone --depth 1 https://github.com/event4u-app/agent-configWrote 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/skills/event4u-app/agent-config/llm-provider-knowledge)<a href="https://agentmods.dev/skills/event4u-app/agent-config/llm-provider-knowledge"><img src="https://agentmods.dev/badge/skills/event4u-app/agent-config/llm-provider-knowledge/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/skills/event4u-app/agent-config/llm-provider-knowledge"><img src="https://agentmods.dev/badge/skills/event4u-app/agent-config/llm-provider-knowledge.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 | $0.00049 | $0.01862 |
| Opus 5 | $0.00024 | $0.00931 |
| Sonnet 5 | $0.00010 | $0.00372 |
| Haiku 4.5 | $0.00005 | $0.00186 |
Grade A, and why
llm-provider-knowledge 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 8d 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 — 149 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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
- Accuracy over guessing — if unsure, route to the docs; never assert.
- Distinguish products — a provider's API, its developer platform, and its consumer app are separate surfaces with separate facts and separate docs.
- Source everything — every product fact in the reply carries an official URL. No URL → not verified → don't state it as fact.
- Route, don't cache — hand off to the live docs; do not transcribe volatile specs into the reply as if durable.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 8d ago First seen · 149 lines · 49 tokens per session scan A f931c6d96253
llm-provider-knowledge is a skill published in the GitHub repository event4u-app/agent-config (10 stars, last pushed today), licensed MIT. It adds 49 tokens to every session and 1,862 once invoked, about $0.0002 per session on Opus 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 2026-09-04.
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