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-ingest-contentgit 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-ingest-content)<a href="https://agentmods.dev/commands/codingthefuturewithai/rag-retriever/rag-ingest-content"><img src="https://agentmods.dev/badge/commands/codingthefuturewithai/rag-retriever/rag-ingest-content.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 | $0.01495 |
| Opus 5 | $0.00000 | $0.00747 |
| Sonnet 5 | $0.00000 | $0.00299 |
| Haiku 4.5 | $0.00000 | $0.00150 |
Grade A, and why
rag-ingest-content 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.
How it starts
The opening of the file, as written. The whole thing — 189 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Ingest Content into RAG Retriever
Guide users through advanced content ingestion using CLI-only capabilities for rich media, local files, and enterprise integrations.
Prerequisites
This command requires CLI access for advanced content ingestion. MCP server only supports basic web crawling.
Arguments
Use $ARGUMENTS to specify content type and source:
- "website URL" - Web crawling (available in MCP)
- "local PATH" - Local files and directories (CLI only)
- "images PATH" - Image analysis and ingestion (CLI only)
- "github REPO_URL" - GitHub repository ingestion (CLI only)
- "confluence SPACE_KEY" - Confluence space ingestion (CLI only)
- "help" - Show all available ingestion options
Examples:
- "website https://docs.python.org" - Crawl website
- "local ~/documents" - Process local directory
- "images ~/screenshots" - Analyze image directory
- "github https://github.com/user/repo" - Index GitHub repository
- "confluence TECH" - Load Confluence space
Implementation Approach
This command uses direct implementation to guide users through appropriate ingestion workflows based on content type.
Your Task
1. Parse Arguments and Identify Content Type
- Extract content type and source from $ARGUMENTS
- Determine appropriate ingestion method
- Provide guidance if content type is unclear
2. Content-Specific Ingestion Guidance
Website Crawling
- Can use MCP:
crawl_and_index_url(url, max_depth, collection_name) - Also available via CLI:
rag-retriever --fetch-url URL --max-depth N --collection NAME - Recommend appropriate crawl depth and collection naming
- Provide post-crawl verification steps
Local File Processing
- CLI ONLY - Not available through MCP
- Single file:
rag-retriever --ingest-file PATH --collection NAME - Directory:
rag-retriever --ingest-directory PATH --collection NAME - Supports: PDF, markdown, text, structured documents
- Explain file type support and processing capabilities
Image Analysis and Ingestion
- CLI ONLY - Requires OpenAI Vision API
- Single image:
rag-retriever --ingest-image PATH --collection NAME - Directory:
rag-retriever --ingest-image-directory PATH --collection NAME - Explain image analysis capabilities (diagrams, screenshots, charts)
- Provide tips for optimal image quality
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 · 189 lines · 0 tokens per session scan A 8df18264fdc3
rag-ingest-content 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 1,495 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.
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