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.
Works with
---
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
---
# 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.More AI & ML skills
full-output-enforcement
leonxlnx/taste-skill
Overrides default LLM truncation behavior. Enforces complete code generation, bans placeholder patterns, and handles token-limit splits cleanly. Apply to any task requiring exhaustive, unabridged output.
writing-shape
mattpocock/skills
Writing, exploit: shape raw material into an article, paragraph by paragraph.
writing-fragments
mattpocock/skills
Writing, explore: mine raw fragments, no structure yet.

