discover

A website-auditing step that finds the standalone products a company sells. It reads the company's own pages and separates real products from pricing tiers, documentation, articles, and product variants.

In plain words
What is it for?
Discovering product names, what each product does, and who it is for by inspecting a company's website and, when available, its `llms.txt` catalogue file.
Why use it?
It helps build a reliable product catalogue without treating every page, feature, or plan as a separate product.

Agent

Install

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.

agentmods
npx agentmods add agents/mo-root/open-kb/discover
Clone the repo
git clone --depth 1 https://github.com/mo-root/open-kb
Per session 0 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,131 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 2d ago against content hash 143a4172eb55, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

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.

prompts/agents/discover.md · 76 lines

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/puppeteer is the scraping browser framed for one tool, not a separate product

How to work

  1. findDocs first, for llms.txt. A company that serves llms.txt or llms-full.txt has 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.

  2. mapProductPages when 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.

  3. Read the pages that look like products, in batches. readPage takes 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/ai may be titled "Assistant"; a slug reading scraping-browser may front a product the page calls "Browser API". Take the page's name, not the slug's.

  4. 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.

  5. 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.

  6. 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. findDocs already showed you the surfaces; read into them only for what the catalog still lacks, not as a second sweep of the whole site.

  7. submitIntegration for 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.

  8. finish once, 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.

Read the full file on GitHub · 76 lines

Changes

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.

  1. 2d ago First seen · 76 lines · 0 tokens per session scan A 143a4172eb55

Subscribe to this mod's changes

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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