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CrawlAI RAG

REVIEW · 69
Community indexed

CrawlAI RAG is an AI-powered website intelligence platform that allows users to crawl entire websites, index their content, and ask natural-language questions using Retrieval-Augmented Generation (RAG). It transforms static websites into queryable knowledge bases.

Downloads0
Stars147
Version1.0.0
Quality69/100 · Promising
Trust69/100 · Sandbox only
Audit78/100 · Needs review

Supply asset profile

Research and knowledge work

Deep research, source comparison, literature review, RAG, knowledge search, and reports.

Browse track

Scenario

RAG and knowledge

I need my agent to build a RAG workflow over documents and retrieve reliable context.

Agent fit

Claude Code + CLI + Codex

Codex, Claude Code, Cursor, CLI, or custom agents.

Install

Ready

npx skills add AnkitNayak-eth/CrawlAI-RAG

Maintenance

active

5mo since push

Risk

Needs review

Quality score needs review

GitHub quality

147

69/100 quality · 77/100 trust

Coverage tags

ResearchRAG and knowledgerag-knowledgeragretrieval

Review notes

Quality score needs review · Stars/forks activity: 147 stars, 35 forks; issue activity unavailable in current metadata

Agent adoption scorecard

Trust, audit, and install readiness at a glance

These scores combine public repository metadata, OpenAgentSkill review signals, maintenance freshness, and install readiness. They are a shortlist signal, not a replacement for human review.

Quality

Promising
69

Useful candidate, but compare it with alternatives before adopting.

Trust

Sandbox only
69

Useful candidate with missing or mixed trust signals. Keep it in an isolated workspace until the outcome loop proves task fit.

Audit

Needs review
78

Install readiness, security metadata, maintenance, and adoption risk.

Trust Score v5

Human review before install

Run only in a sandbox and compare close alternatives before using it for real work.

PythonRAGCodexClaude CodeCursor

Stars

147 GitHub stars

Repo activity

147 stars, 35 forks

Maintenance

5mo since push

License

MIT

Install

npx skills add AnkitNayak-eth/CrawlAI-RAG

Install safety

standard package or runtime install path

Permission surface

filesystem or document access, network or browser access

Agent outcomes

No agent outcome data yet

Docs

Strong README/SKILL.md context

Risk summary

Review before production

  • Quality score needs review
  • Stars/forks activity: 147 stars, 35 forks; issue activity unavailable in current metadata

Install readiness

Install path available

  • Install path is available
  • Repository evidence is available
  • License is declared
  • No Agent Proven outcome evidence yet

Agent-readable metadata

Machine-readable decision data for this skill.

Use this block or the embedded JSON to decide whether an agent should install this skill, choose an alternative, or ask for human review first.

Open JSON

Suited tasks

  • RAG and knowledge workflows
  • Claude Code teams
  • builders willing to evaluate younger projects
  • Chunk documents

Suited agents

PythonRAGCodexClaude CodeCursorOpenAgentSkill CLICLI

Install decision

Command
npx skills add AnkitNayak-eth/CrawlAI-RAG
Policy
review
Human review
yes

Trust and risk

Trust
69/100
Audit
78/100
Risk level
Needs review

Outcome loop

Endpoint
/api/agent/outcome
Event ID
resolve
Outcomes
5

Install command

npx skills add AnkitNayak-eth/CrawlAI-RAG
Public auditEval reportResolve APIInstall handoff

Do not use when

  • teams that need a vendor-supported SLA
  • high-compliance environments without internal security review
  • No OpenAgentSkill engagement data yet
  • Quality score needs review
  • Stars/forks activity: 147 stars, 35 forks; issue activity unavailable in current metadata

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Agent safety v2

62/100 · Review before install

Reviewed with permission notesreview

Usable candidate, but the agent should surface permission and audit notes before installation.

Require human approval before installing into a real workspace.

Resolve via API

medium

Network access

Skill likely fetches remote pages, APIs, repositories, or external services.

medium

Filesystem access

Skill may read or write project files, documents, generated artifacts, or local workspace state.

  • Quality score needs review

Install targets

Install this skill in your agent workflow

Copy the registry command or an agent-specific install prompt for Codex, Claude Code, and Cursor.

skill install

OpenAgentSkill CLI

Use the registry command when your workflow supports the OpenAgentSkill installer.

$ npx skills add AnkitNayak-eth/CrawlAI-RAG

Agent resolve plan

Let an agent verify fit before installing.

The Resolve API returns the selected skill, alternatives, safety policy, audit notes, install target, and copy-paste prompt an agent can follow without scraping this page.

Open text plan

Resolve JSON

/api/agent/resolve?task=Use%20CrawlAI%20RAG%20for%20an%20agent%20workflow&agent=codex&max_risk=medium

Resolve text

/api/agent/resolve?task=Use%20CrawlAI%20RAG%20for%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text

Install handoff

/api/skills/ankitnayak-eth-crawlai-rag/install

Agent should check

  • Task fit and alternatives from Resolve API.
  • Audit score, trust score, and safety policy warnings.
  • Install target compatibility for Codex, Claude Code, Cursor, or CLI.

Copy prompt

Task: Use CrawlAI RAG in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20CrawlAI%20RAG%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/ankitnayak-eth-crawlai-rag/install
Install command: npx skills add AnkitNayak-eth/CrawlAI-RAG
Before running it, summarize audit warnings, required permissions, and the fallback skill if install is risky.

Agent handoff

Give an agent the install path, not another directory page.

Use the public install endpoint to fetch the command, safety checklist, target prompts, and canonical links for this skill.

Open install API

Install handoff

/api/skills/ankitnayak-eth-crawlai-rag/install

LLM text format

/api/skills/ankitnayak-eth-crawlai-rag/install?format=text

Find alternatives

/api/skills/search?q=CrawlAI%20RAG&limit=3

Agent prompt

Use CrawlAI RAG for this task. Review https://www.openagentskill.com/api/skills/ankitnayak-eth-crawlai-rag/install, then install with: npx skills add AnkitNayak-eth/CrawlAI-RAG

Registry metadata

Agent-readable profile for automatic skill selection.

This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.

Open manifest

Manifest

/api/registry/manifest/ankitnayak-eth-crawlai-rag

LLM text

/api/registry/manifest/ankitnayak-eth-crawlai-rag?format=text

Install alias

/api/registry/install/ankitnayak-eth-crawlai-rag

Recommend

/api/registry/recommend?task=Use%20CrawlAI%20RAG%20in%20an%20agent%20workflow&limit=3

Agent fit

68/100

RAG and knowledge

Use-case tags

RAG and knowledgeWeb scrapingCoding agents

Platforms

Python, RAG, Claude Code

Audit report

Needs review · 78/100

Review install readiness, maintenance, trust, quality, and metadata warnings before adding this skill to an agent workflow.

View audit reportView eval report

Agent decision cockpit

Fallback candidate for RAG and knowledge

Prototype with this skill first; keep a fallback candidate ready.

68
Readiness
Prototype
Stage

Role in stack

Fallback candidate

Primary fit

RAG and knowledge

Trust label

Prototype first

Install path

Command ready

Use when

  • RAG and knowledge workflows
  • Claude Code teams
  • builders willing to evaluate younger projects

Evidence

  • recent repository activity
  • install command or GitHub repo available
  • 69/100 quality profile

Review first

  • No OpenAgentSkill engagement data yet

Implementation path

  1. 1Install it in a sandbox agent and run one RAG and knowledge task end to end.
  2. 2Compare output quality, latency, and failure behavior against at least one alternative.
  3. 3Promote it into production only after reviewing repository permissions, license, and maintenance signals.

Trust profile

Sandbox only

Useful candidate with missing or mixed trust signals. Keep it in an isolated workspace until the outcome loop proves task fit.

69
Trust score

GitHub adoption

INFO

147 GitHub stars

Stars/forks activity

CHECK

147 stars, 35 forks; issue activity unavailable in current metadata

Recent maintenance

INFO

5mo since push

License clarity

PASS

MIT

Good signals

  • AI review approved
  • Install path is available
  • Repository evidence is available
  • Install command has no obvious high-risk pattern
  • Outcome loop is ready but needs first real agent run

Review before install

  • Quality score needs review
  • Stars/forks activity: 147 stars, 35 forks; issue activity unavailable in current metadata
  • No real agent outcome reports yet
  • Human review required before unattended installation

Recommended action

Run only in a sandbox and compare close alternatives before using it for real work.

Quality profile

Promising candidate for agent workflows

Useful candidate, but compare it with alternatives before adopting.

69
GitHub stars
147
Freshness
5mo ago
Install ready
Yes
License
MIT

Workflow fit

Use this skill in these scenarios

Search private knowledge

RAG and knowledge

I need my agent to build a RAG workflow over documents and retrieve reliable context.

Collect structured data

Web scraping

I need my agent to scrape websites and extract structured data from pages.

Build and ship code

Coding agents

I need a coding agent that can understand a repository, edit code, and review pull requests.

Stack fit

Add it to a complete workflow

Ingest, retrieve, and cite

RAG knowledge base

A stack for document-heavy agents that ingest files, create searchable knowledge, retrieve relevant context, and answer with grounded sources.

Turn skills into distribution

Content growth agent

A stack for turning newly indexed skills into SEO briefs, social drafts, comparison pages, and reusable publishing workflows.

Scrape, clean, and reuse web data

Web data pipeline

A practical stack for agents that crawl public pages, extract clean content, normalize data, and hand it to downstream research or RAG workflows.

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Overview

CrawlAI RAG is an AI-powered website intelligence platform that allows users to crawl entire websites, index their content, and ask natural-language questions using Retrieval-Augmented Generation (RAG). It transforms static websites into queryable knowledge bases.

Imported by the skill-only GitHub discovery pipeline because it matches agent skill, automation, domain workflow, RAG, document-processing, data, finance, security, or developer-tool signals. Protocol-server projects are excluded from automated imports.

Platform Compatibility

pythonFULL
ragFULL

Technical Details

Version
1.0.0
License
MIT
Last Updated
6/21/2026
Published
6/21/2026

Frameworks & Tools

PythonRAG

Decision snapshot

Fallback candidate

68
Ready
Prototype
Stage

recent repository activity

Audit snapshot

Install review

Install and adoption review

78
Needs review
Security
85/100
Maintenance
76/100
Install
92/100
Open full auditOpen eval report

Agent-proven evidence

Agent Proven evidence

Outcome reports after resolve, review, install, and one narrow run.

0
Proven
Needs first agent runAuto-install: review firstLast: Unknown
Success rate
—
Recent failure
—
Outcomes
0
Output quality
—
Failed
0
Not relevant
0
Installs
0
Risk blocked
0
Setup needed
0
Production
0

No agent outcome data yet. The first agent run can report success, setup needs, risk blocks, failure, or not-relevant through /api/agent/outcome.

Agent-Proven rankingOutcome contract

Install

Add to agent workflow

Free and open source. Review the audit before production use.

Compare AlternativesAuto-resolve PlanView on GitHubDocumentation

Growth loop

Share kit

X

Scenario-led draft for CrawlAI RAG, ready for a manual X post.

Curator note
Most web agents fail in the boring part: messy pages, missing context, repeatable extraction.

CrawlAI RAG gives agents a cleaner path to browse, extract, and monitor web pages.

147 stars

https://www.openagentskill.com/skills/ankitnayak-eth-crawlai-rag?ref=x
#AIAgents
Open X draft
Optional reply with install command
Listing + install path for CrawlAI RAG:
https://www.openagentskill.com/skills/ankitnayak-eth-crawlai-rag?ref=x

Install: npx skills add AnkitNayak-eth/CrawlAI-RAG
Open reply draft

Listing source

Community indexed

Claimable

This listing was indexed from public sources and is not marked official until a maintainer claim is approved.

Indexed by
OpenAgentSkill community index

Attribution links to the public repository or creator profile. Creators can claim the listing to update ownership signals.

Claim this skill

Owner claim

Claim this skill listing

This community indexed listing is attributed to AnkitNayak-eth but is not marked official yet. Claim it to add a verified owner signal and make future launch, install, and audit updates easier to trust.

README badge

Add this badge to your GitHub README to show the listing, trust score, and install handoff.

[![OpenAgentSkill](https://www.openagentskill.com/api/badge/ankitnayak-eth-crawlai-rag)](https://www.openagentskill.com/skills/ankitnayak-eth-crawlai-rag)
Preview badgeAudit badge

Author

A

AnkitNayak-eth

@ankitnayak-eth

Tags

ragretrievalknowledgeagentcrawlerrag-chatbotrag-pipelinepythongithub

Platform Fit

Claude Code

Health Signals

GitHub stars
147
Quality score
42/100
Last GitHub push
Feb 15, 2026
Framework hints
2
OpenAgentSkill views
0
Install copies
0
Outbound clicks
0

Community Signal

Share whether this skill looks useful for your agent workflow. Aggregated feedback improves rankings over time.

Trust & Safety

Sandbox only

69
  • GitHub adoption147 GitHub starsINFO
  • Stars/forks activity147 stars, 35 forks; issue activity unavailable in current metadataCHECK
  • Recent maintenance5mo since pushINFO
  • License clarityMITPASS
  • README/SKILL.md completenessMetadata includes enough usage and workflow contextPASS
  • Dependency/runtime risknetwork or browser surfacePASS

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