Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add commands/codingthefuturewithai/rag-retriever/rag-index-websitegit clone --depth 1 https://github.com/codingthefuturewithai/rag-retrieverWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/commands/codingthefuturewithai/rag-retriever/rag-index-website)<a href="https://agentmods.dev/commands/codingthefuturewithai/rag-retriever/rag-index-website"><img src="https://agentmods.dev/badge/commands/codingthefuturewithai/rag-retriever/rag-index-website.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00000 | $0.00436 |
| Opus 5 | $0.00000 | $0.00218 |
| Sonnet 5 | $0.00000 | $0.00087 |
| Haiku 4.5 | $0.00000 | $0.00044 |
Grade A, and why
rag-index-website scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
Index Website into RAG Retriever
Crawl and index a website into the RAG Retriever vector store for future semantic search.
Prerequisites
This command requires the RAG Retriever MCP server to be configured in your Claude Code setup. The server handles web crawling and content indexing into vector store collections.
Arguments
Use $ARGUMENTS to specify crawling parameters:
- URL (required)
- Max depth (optional - defaults to 2)
- Collection name (optional - creates new collection if not exists)
Examples:
- "https://docs.anthropic.com/claude-code"
- "https://python.org/docs 3"
- "https://fastapi.tiangolo.com fastapi_docs 2"
Implementation Approach
This command uses direct implementation to coordinate website crawling and indexing.
Your Task
-
Parse Arguments
- Extract URL from $ARGUMENTS (required)
- Parse optional max_depth (default: 2)
- Parse optional collection_name (default: generates from URL)
-
Pre-Crawl Analysis
- Validate the URL is accessible
- Estimate the scope of crawling based on max_depth
- Suggest appropriate collection name if not provided
- Use
list_collectionsto check for existing collections
-
Initiate Crawling
- Use
crawl_and_index_urlto start the crawling process - Monitor progress and provide updates
- Handle any errors or issues that arise
- Use
-
Post-Crawl Verification
- Use
list_collectionsto verify the collection was created/updated - Perform a test search to validate indexed content
- Provide summary of pages crawled and content indexed
- Use
Success Criteria
- Website successfully crawled and indexed
- Content stored in appropriate collection
- Verification that search functionality works with new content
- Clear summary of indexing results
Available MCP Tools
list_collections()- Check existing collectionscrawl_and_index_url(url, max_depth, collection_name)- Index website contentvector_search(query, collection_name)- Verify indexed content
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 5d ago First seen · 53 lines · 0 tokens per session scan A fef6fa951d06
rag-index-website is a command published in the GitHub repository codingthefuturewithai/rag-retriever (27 stars, last pushed 1y ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 436 tokens. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other commands, from other repositories
querying
Query documents from a search index using type-safe filters with support for pagination, sorting, field selection, scoring, and highlighting. Count matching documents efficiently without returning results.
agent-brain-index
Index documents for semantic search.
agent
Add an AI agent / RAG backend (@convex-dev/agent) to the Convex app.
ingest
Manually add knowledge to the Weaviate store.
web-ingest
Crawl the configured source and generate knowledge markdown files.
web-update
Re-ingest a URL or refresh a local knowledge file, then rebuild the search index.