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.
Install
npx skills add https://github.com/firecrawl/skills/tree/main/skills/workflows/firecrawl-knowledge-base
claude plugin marketplace add https://llmmart.ai/marketplace.json && claude plugin install firecrawl-skills@llmmart
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, andsources.json. - RAG: markdown files plus chunk files and
manifest.json. - Training: scraped source files plus
training-data.jsonlandtraining-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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