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Derive Client

OrganizationPopular
vercel-labs
derive-client

Reverse-engineer a website's internal API by recording browser traffic into a HAR file, then generate a standalone client or CLI that calls the endpoints directly, with no browser needed after the first recording. Use when asked to "derive a client", "build a CLI for <site>", "reverse engineer this site's API", "record network requests", "turn this site into an API", or when the same site will be automated repeatedly and direct HTTP calls would beat driving the browser every time.

Overview

Publishervercel-labs
Repositoryagent-browser
Skill namederive-client
Stars
42.8K
Forks
2.9K
Bundled files
Instructions only
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by vercel-labs on GitHub. Read the source before you install it.

Installation

Install the Derive Client AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/vercel-labs/agent-browser.git /tmp/agent-browser
mkdir -p .claude/skills
cp -r /tmp/agent-browser/skill-data/derive-client .claude/skills/derive-client
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Derive Client in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Derive Client on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Derive Client is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Derive an API client from a recorded session

Driving a browser is the right tool for the first visit and the wrong tool for the hundredth. This skill records a site's network traffic once while you use it, then turns the captured requests into a standalone client (script, CLI, or library) that talks to the site's internal API directly.

The recording alone contains everything needed: agent-browser embeds text response bodies (JSON/HTML/JS) in the HAR by default, so endpoint shapes can be studied offline after the browser is closed.

Workflow

1. Record     Start HAR capture, drive the flows you want in the client
2. Identify   Find the real API endpoints among the noise
3. Extract    Pull request shapes, response schemas, and auth material
4. Generate   Write the client, one function per flow
5. Verify     Call every endpoint for real before declaring done

1. Record

bash
agent-browser network har start          # embeds text response bodies by default
# ... drive the site: search, open a detail page, paginate, etc. ...
agent-browser network har stop /tmp/site.har
  • Exercise every flow the client should support, and run each one at least twice with different inputs (two search terms, two detail pages). Diffing the recorded URLs reveals which parts are parameters.
  • If the site needs login, log in before starting the HAR so credentials don't land in the recording unnecessarily. The session cookies are exported separately in step 3.
  • --content all embeds binary bodies too (base64); --content none disables embedding. Per-body cap is 2 MB.

While the session is still open, agent-browser network requests and network request <id> give the same data interactively — but only the HAR survives navigation and browser close, so prefer it for anything multi-page.

2. Identify endpoints

Query the HAR with jq:

bash
# All JSON API calls: method, URL, status
jq -r '.log.entries[]
  | select(.response.content.mimeType | test("json"))
  | "\(.request.method) \(.response.status) \(.request.url)"' /tmp/site.har

Ignore analytics and infrastructure noise: telemetry endpoints (/collect, /track, /beacon, /log), third-party domains (google-analytics, segment, sentry, datadog, intercom, hotjar), and static assets. The real API is usually first-party, JSON, and correlates with the actions you performed.

3. Extract shapes and auth

bash
# Full detail for one endpoint: request headers, POST body, response body
jq '.log.entries[] | select(.request.url | test("api/search"))
  | {request: {method: .request.method, headers: .request.headers,
     postData: .request.postData.text},
     response: .response.content.text}' /tmp/site.har
  • Response schema: read .response.content.text — this is the real payload, use it to derive types.
  • Auth: compare request headers across endpoints. Look for authorization, cookie, x-csrf-token, x-api-key, and site-specific x-* headers. Replay only the ones that matter — test by omission in step 5.
  • Cookies: export the live session with agent-browser cookies get --json > cookies.json for the client to load at runtime. Never hardcode cookie values into generated source.

4. Generate the client

  • One function per recorded flow (search(query), getItem(id)), typed from the observed response bodies.
  • Auth material (cookies, bearer tokens) loads from a file or environment variable, with a clear error telling the user to re-run the browser login when it expires.
  • Reproduce the headers the API actually requires — some sites 403 without a matching user-agent, referer, or x-requested-with.
  • Keep pagination, sort, and filter parameters that appeared in the recorded query strings as function options.

5. Verify

Call every generated function against the live API and compare the response shape with the recording. Common failures:

SymptomCauseFix
401/403Expired or missing sessionRe-login via agent-browser, re-export cookies
403/419 on writesCSRF token is per-session or per-formFetch the token endpoint first, or keep that flow browser-driven
Works then breaksSigned/expiring request paramsFall back to the browser for that step; derive the rest
Different shape than HARA/B tests or geo-dependent responsesRe-record and treat the union as optional fields

Caveats

  • Internal APIs are unversioned and change without notice — keep the HAR so the client can be re-derived.
  • Respect the site's terms of service and rate limits; add delays for bulk fetching.
  • HAR files contain live session credentials (cookies, tokens, POST bodies). Treat them like secrets: keep them out of version control and delete them when done.

Frequently asked questions

What does the Derive Client AI skill do?

Reverse-engineer a website's internal API by recording browser traffic into a HAR file, then generate a standalone client or CLI that calls the endpoints directly, with no browser needed after the first recording. Use when asked to "derive a client", "build a CLI for <site>", "reverse engineer this site's API", "record network requests", "turn this site into an API", or when the same site will be automated repeatedly and direct HTTP calls would beat driving the browser every time.

Why use Derive Client on TypingMind?

Because you install it once and use it with any model. Derive Client is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Derive Client in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/vercel-labs/agent-browser/tree/main/skill-data/derive-client. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Derive Client?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Derive Client?

As many as you like. As long as a model supports skills, you can use Derive Client with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Derive Client AI skill free?

Yes. It is published on GitHub by vercel-labs under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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