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firecrawl-knowledge-base

firecrawl-knowledge-base

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.

Independent GitHub snapshot · Automated classification · Original documentation governs

Skill 문서

GitHub

name: firecrawl-knowledge-base description: 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. license: ISC metadata: author: firecrawl version: "0.1.0" homepage: https://www.firecrawl.dev source: https://github.com/firecrawl/firecrawl-workflows inputs:

  • name: FIRECRAWL_API_KEY description: Firecrawl API key for hosted Firecrawl requests. required: true

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.
저장한 Skills