Claude Cursor Skill

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.

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Download firecrawl-skills-skills_workflows_firecrawl-knowledge-base-8507156.zip · 1 KB
Part of firecrawl/skills — 33 skills

Install

skills CLI npx skills add https://github.com/firecrawl/skills/tree/main/skills/workflows/firecrawl-knowledge-base
Claude Code claude plugin marketplace add https://llmmart.ai/marketplace.json && claude plugin install firecrawl-skills@llmmart
Git git clone https://github.com/firecrawl/skills.git

The skills CLI installs just this skill, for any of its supported agents. Claude Code installs the whole firecrawl/skills collection as a plugin from our marketplace. Git is the plain clone.

Skill manifest

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.
Files (skills)
  • SKILL.md 2.4 KB
    ---
    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:
    
    ```text
    .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
    
    ```markdown
    # 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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