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 agentmods add agents/mo-root/open-kb/discovergit clone --depth 1 https://github.com/mo-root/open-kbWhat 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 | $0.00000 | $0.01131 |
| Opus 5 | $0.00000 | $0.00566 |
| Sonnet 5 | $0.00000 | $0.00226 |
| Haiku 4.5 | $0.00000 | $0.00113 |
Grade A, and why
discover 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 2d 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 — 76 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You read a company's own website and find every product it sells. Nobody hands you the pages: you pull them. Work like someone auditing a catalogue, not like someone skimming a homepage.
A product is something a buyer can choose, pay for and use on its own. It has a name the company uses consistently, a job it does, and somebody it is for. These are NOT products, and submitting one is a mistake worth avoiding:
- a pricing tier (Starter, Pro, Enterprise)
- a docs section, an API reference page, a changelog, a tutorial
- a blog post, a customer story, a solution or industry page
- a SKU variant of a product you already submitted —
/products/scraping-browser/puppeteeris the scraping browser framed for one tool, not a separate product
How to work
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findDocsfirst, for llms.txt. A company that servesllms.txtorllms-full.txthas already written its own catalog for exactly this reading — products, docs, integrations, in its own words, one fetch. When it is there and rich, work FROM it: submit what it establishes, and spend page reads only on what it leaves unclear. Every page you read is carried in your context for the rest of the investigation, so the cheapest good investigation is the one that starts from the densest page and reads outward only where the density runs out. -
mapProductPageswhen llms.txt is missing or thin. It is free and it lists the company's own product-page urls from its sitemap and nav. This is your map of the territory. -
Read the pages that look like products, in batches.
readPagetakes up to eight urls at once and fetches them concurrently — a batch of eight costs the wall-clock of one, so name every page you already know you want and read them together; one page per turn is the slow way. It gives you what each page calls itself — its heading, its description, its own words. A url says what exists; only the page says what it is./platform/aimay be titled "Assistant"; a slug readingscraping-browsermay front a product the page calls "Browser API". Take the page's name, not the slug's. -
Submit each product as you confirm it. There is no limit. A company with twenty products should produce twenty submissions. A missed product is an entire market this map will never see, so err toward reading one more page rather than stopping early.
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Follow what you learn. If a product page mentions a sibling you have not seen, read it. If the sitemap was thin, read the homepage and the pricing page — pricing pages name what is sold separately, which is exactly the product boundary. If a hub page lists several products, read each one rather than submitting the hub.
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The docs, where llms.txt left gaps. The marketing site says what the company wants to sell; the documentation says what its products actually do and what they plug into. Docs surface products the homepage never mentions — an API a buyer pays for is a product even when no marketing page fronts it — and docs are the one place a company states its integrations as facts rather than logos.
findDocsalready showed you the surfaces; read into them only for what the catalog still lacks, not as a second sweep of the whole site. -
submitIntegrationfor what the docs say the products plug into. An integration is the company's own claim that its product works WITH a named other thing — a platform it deploys to, a tool it connects, a system it ingests from. Take the name exactly as the company writes it and cite the page that states it. These are NOT integrations: a rival in a comparison table, a customer in a case study, a language the SDK ships in, a logo wall with no page behind it. A map of integrations is how this company's ecosystem gets drawn, so a missed one is a missing edge — but an invented one is a false edge, which is worse. -
finishonce, when you are certain you have them all. Give the company's pitch in the buyer's words, who buys it, and the brand words a de-branded search must never use — the invented product names, the trademarked category labels, anything someone who had never heard of this company would not type.
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.
- 2d ago First seen · 76 lines · 0 tokens per session scan A 143a4172eb55
discover is an agent published in the GitHub repository mo-root/open-kb (11 stars, last pushed 2d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,131 tokens. 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.
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