/firecrawl-knowledge-base
Firecrawl API key for hosted Firecrawl requests.
$ npx -y skills add firecrawl/firecrawl-workflows --skill firecrawl-knowledge-base --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
- Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
- You can call itInvoke it directly when you want it.
- Slash command
/firecrawl-knowledge-base
Context preview
The summary Claude sees to decide when to auto-load this skill.
Firecrawl API key for hosted Firecrawl requests.
SKILL.md
firecrawl-knowledge-base.SKILL.mdname: 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: trueFirecrawl 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.mdParallel 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.
Read more
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: trueFirecrawl 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.mdParallel 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.
Source of truth for Firecrawl workflow skills — outcome-focused skills for AI coding agents: research reports, SEO audits, QA reports, knowledge bases, lead lists, and agent-ready website design systems.

