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firecrawlfirecrawl/skills/skills/workflows/firecrawl-knowledge-base/SKILL.md

firecrawl-knowledge-base

Skill142 starsChanged 43 days agoISCView on GitHub

Build a knowledge base from web content with Firecrawl. Use for local reference docs, RAG-ready chunks, fine-tuning datasets, documentation mirrors, topic corpora, or LLM-ready markdown organized from web sources.

  • Reads credentials

We split this skill into the rules it gives an agent

5 rules. About 0 are common, 4 are distinctive, and 1 is very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈4

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 1

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Use this to turn URLs or topics into organized LLM-ready content.

    line 18
  2. Very specific:

    Ask at most 1-3 concise questions only if blocked, such as the source URL/topic, whether the output is reference/RAG/training/docs, or …

    line 24
  3. Distinctive:

    Use Firecrawl map for documentation sites, search for topic-based corpora, scrape pages into markdown, and preserve code examples and …

    line 28
  4. Distinctive:

    If appropriate, use sub-agents or equivalent parallel task runners:

    line 41
  5. Distinctive:

    Remove boilerplate navigation where possible.

    line 85

≈ “Common” is an estimate: at least 1 in 5 of the 308,232 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Firecrawl Knowledge Base

Use this to turn URLs or topics into organized LLM-ready content.

Onboarding Interview

Infer the source, goal, depth, and output location from context. If the source and goal are clear, proceed immediately.

Ask at most 1-3 concise questions only if blocked, such as the source URL/topic, whether the output is reference/RAG/training/docs, or training format if training is requested.

Firecrawl Collection Plan

Use Firecrawl map for documentation sites, search for topic-based corpora, scrape pages into markdown, and preserve code examples and tables.

For files, follow the Firecrawl download-style convention:

.firecrawl/
  <hostname>/
    <path>/
      index.md

Parallel Work

If appropriate, use sub-agents or equivalent parallel task runners:

  • one docs section per researcher
  • official docs, tutorials, community discussions, and references by source type
  • source scraping vs chunk generation vs manifest generation

Output Modes

  • Reference: markdown files, index.md, and sources.json.
  • RAG: markdown files plus chunk files and manifest.json.
  • Training: scraped source files plus training-data.jsonl and training-metadata.json.
  • Docs mirror: complete markdown mirror with a table of contents.

Final Deliverable

# Knowledge Base: [Source]

## Summary
[What was collected and why]

## Output Structure
[Files/directories created]

## Coverage
[Sections, source types, counts]

## Usage Notes
[How to use in RAG, docs, training, or agent context]

## Sources
[URLs collected]

## Rerun Inputs
workflow: firecrawl-knowledge-base
source: [url/topic]
goal: [reference/rag/train/docs]
depth: [quick/thorough/exhaustive]
output_dir: [.firecrawl/]

Quality Bar

  • Preserve code examples and formatting.
  • Remove boilerplate navigation where possible.
  • Include source URLs in frontmatter or metadata.

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Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.