firecrawl-knowledge-base

작성자: firecrawl

Firecrawl을 사용하여 웹 콘텐츠로 지식 베이스를 구축하세요. 로컬 참조 문서, RAG 준비 청크, 파인튜닝 데이터셋, 문서 미러, 주제 코퍼스 또는 웹 소스에서 정리된 LLM 준비 마크다운에 사용할 수 있습니다.

npx skills add https://github.com/firecrawl/firecrawl-workflows --skill firecrawl-knowledge-base

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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/]