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 skills/itmediatech/rag-cli/rag-retrievalnpx skills add ItMeDiaTech/rag-cli --skill rag-retrievalgit clone --depth 1 https://github.com/ItMeDiaTech/rag-cliWhat 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 | $0.00596 |
| Opus 5 | $0.00000 | $0.00298 |
| Sonnet 5 | $0.00000 | $0.00119 |
| Haiku 4.5 | $0.00000 | $0.00060 |
Grade A, and why
rag-retrieval 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 2d 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 — 84 lines — stays where its author put it; the contents beside it link to each section on GitHub.
RAG Retrieval Skill
Query your local document knowledge base using semantic search and get AI-powered answers.
Overview
This skill enables RAG (Retrieval-Augmented Generation) queries against your locally indexed documents. It uses semantic search to find relevant documents and generates answers using Claude Haiku.
Usage
/skill rag-retrieval "How to configure the API?"
Features
- Semantic Search: Uses vector similarity to find relevant documents
- Hybrid Retrieval: Combines vector search with keyword matching for better accuracy
- Context-Aware Answers: Uses claude-haiku-4-5-20251001 to generate responses
- Citation Support: Shows sources for generated answers
- Performance Monitoring: Tracks query latency and accuracy
Arguments
query(required): Your question or search query--top-k(optional): Number of documents to retrieve (default: 5)--threshold(optional): Minimum similarity score (default: 0.7)--mode(optional): Search mode - "hybrid", "vector", or "keyword" (default: "hybrid")
Examples
Basic Query
/skill rag-retrieval "What is the authentication process?"
Retrieve More Context
/skill rag-retrieval "How to handle errors?" --top-k 10
Vector-Only Search
/skill rag-retrieval "API rate limits" --mode vector
Configuration
The skill uses the following configuration from config/default.yaml:
retrieval.top_k: Default number of documents to retrieveretrieval.hybrid_ratio: Balance between vector and keyword search (0.7 = 70% vector)claude.model: LLM model for response generationclaude.max_tokens: Maximum response length
Performance
Typical latencies:
- Vector search: <100ms
- End-to-end response: <5 seconds
- Indexing: ~0.5s per 100 documents
Requirements
- Indexed documents in
data/vectors/ - Valid Anthropic API key in environment
- At least 2GB RAM for vector operations
Troubleshooting
No Results Found
- Ensure documents are indexed:
python scripts/index.py --input data/documents - Lower the similarity threshold:
--threshold 0.5 - Try keyword mode if vector search fails
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 2d ago First seen · 84 lines · 0 tokens per session scan A 7cc1109e5ed7
rag-retrieval is a skill published in the GitHub repository ItMeDiaTech/rag-cli (51 stars, last pushed 5mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 596 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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