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Firecrawl Alexandria

Organization
firecrawl
firecrawl-alexandria

Find a direct path to structured data through ready-made workflows, data APIs, and indexes. Follow the search skill to discover and inspect tools, then the scrape skill to execute them.

Overview

Publisherfirecrawl
Repositorycli
Skill namefirecrawl-alexandria
Stars
642
Forks
110
Bundled files
Instructions only
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 firecrawl on GitHub. Read the source before you install it.

Installation

Install the Firecrawl Alexandria 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.
    https://github.com/firecrawl/cli/tree/main/skills/firecrawl-alexandria
  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/firecrawl/cli.git /tmp/cli
mkdir -p .claude/skills
cp -r /tmp/cli/skills/firecrawl-alexandria .claude/skills/firecrawl-alexandria
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Firecrawl Alexandria 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 Firecrawl Alexandria 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 Firecrawl Alexandria 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.

A direct path to structured data

Alexandria brings ready-made website workflows, API providers, and specialized indexes into Firecrawl search and scrape. Semantic discovery finds capabilities by the data you need; domain matching connects web results to tools that may retrieve richer structured data beyond the page. Discover a tool that fits the task and get structured results directly, reducing the browsing, parsing, and repeated requests needed to assemble the data yourself.

  • Search to find web results and relevant tools, then inspect only the contracts needed for the task.
  • Scrape to execute a selected tool or read a URL. For large retained results, use its remote Bash guidance to select the data you need.

Use ordinary web results when they answer the question; use a provider tool when its coverage and inputs fit.

Alexandria feedback

Alexandria coverage grows from what agents report. If you choose to report how the catalogue served a task, send at most one firecrawl alexandria feedback per website you needed data from after finishing the task. It is free: no job ID, no time window, no credit refund.

Feedback can describe any of these outcomes:

  • A tool answered the need, fully or partly.
  • A tool ran but returned wrong or incomplete data, or failed.
  • No provider covers the website, or a provider exists but lacks the capability you needed, and you fell back to web search, scrape, or Agent.

Opt out: if FIRECRAWL_NO_ENDPOINT_FEEDBACK=1 (or FIRECRAWL_DISABLE_ENDPOINT_FEEDBACK=1) is set, the CLI silently skips the call and never sends anything. Respect that; do not try to work around it. (Team admins can also disable this server-side; the API returns feedbackErrorCode: "TEAM_OPTED_OUT" and the CLI exits 0 silently.)

Rules to know before you call this:

  • --url is the website the user needed data from, not the provider and not a Firecrawl page. --requested-functionality is what they needed from it, in one sentence. These two fields are the most important: they aggregate across teams and tell us which sites and workflows to add next.
  • --objective is the underlying goal behind the session: what you or your user were ultimately trying to accomplish, in one sentence (for example, "Shortlist federal IT contracts to bid on this quarter"). It is broader than --requested-functionality, which covers only this website.
  • --rationale explains the rating from observed results: which provider or capability served or failed the need, and how. Two or three sentences, no raw results pasted in.
  • --provider-feedback is a JSON array of {name, issue, why} for providers that were missing, thin, or unavailable. Issues: missing_provider (no provider covers the site), insufficient_coverage (exists, but data was thin, stale, or partial for this market or segment), provider_unavailable (could not be called), other.
  • --capability-feedback is a JSON array of {name, provider, issue, why, requestedFunctionality?} for capabilities that were missing, wrong, or failed. Issues: new_capability_request (ask the provider to add one; requestedFunctionality required), missing_capability (provider exists but lacks it), insufficient_functionality (exists but cannot take the input or filter you needed), incorrect_result, execution_error, other. Use name and provider exactly as discovery returned them; for a capability that does not exist yet, name what it should be.
  • Rate honestly: good when a tool answered the need, partial when it answered some of it or with gaps, bad when nothing available answered it or what ran was wrong or failed.
  • --silent & is the right pattern: exit code 0 even on failure, so a rejected call never crashes your pipeline.
bash
# Example: send at most once per website after the task is done. Replace the
# placeholders with what actually happened; drop --provider-feedback or
# --capability-feedback when there is nothing to report at that level.
firecrawl alexandria feedback \
  --rating "<good|partial|bad>" \
  --url "https://sam.gov" \
  --requested-functionality "Active contracts by agency with their attachments" \
  --objective "Shortlist federal IT contracts to bid on this quarter" \
  --rationale "sam-gov/contracts returned the contract list, but no capability exposes attachment links, so those were scraped from the web instead." \
  --capability-feedback '[{"name":"attachments","provider":"sam-gov","issue":"new_capability_request","why":"Attachments were the point of the task","requestedFunctionality":"Given a contract ID, return attachment URLs and document text"}]' \
  --silent &

If you report a site with no provider coverage, use --provider-feedback '[{"name":"<site or provider>","issue":"missing_provider","why":"<what was needed>"}]' and rate bad; that is the signal we use to onboard new providers.

--silent suppresses output and & runs it in the background so feedback never blocks you. Run firecrawl alexandria feedback --help for every option.

Frequently asked questions

What does the Firecrawl Alexandria AI skill do?

Find a direct path to structured data through ready-made workflows, data APIs, and indexes. Follow the search skill to discover and inspect tools, then the scrape skill to execute them.

Why use Firecrawl Alexandria on TypingMind?

Because you install it once and use it with any model. Firecrawl Alexandria 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 Firecrawl Alexandria in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/firecrawl/cli/tree/main/skills/firecrawl-alexandria. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Firecrawl Alexandria?

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 Firecrawl Alexandria?

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

Is the Firecrawl Alexandria AI skill free?

It is published on GitHub by firecrawl. Check the repository for licensing terms. 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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