Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/tdimino/claude-code-minoannpx agentmods add skills/tdimino/claude-code-minoan/rlamaWrote 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/skills/tdimino/claude-code-minoan/rlama)<a href="https://agentmods.dev/skills/tdimino/claude-code-minoan/rlama"><img src="https://agentmods.dev/badge/skills/tdimino/claude-code-minoan/rlama/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/tdimino/claude-code-minoan/rlama"><img src="https://agentmods.dev/badge/skills/tdimino/claude-code-minoan/rlama.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00070 | $0.04741 |
| Opus 5 | $0.00035 | $0.02371 |
| Sonnet 5 | $0.00014 | $0.00948 |
| Haiku 4.5 | $0.00007 | $0.00474 |
Grade C, and why
rlama scanned grade C with 2 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 8d 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.
Recursive force deletehighDestructive command
rm -rf with a variable or a broad path is one typo away from removing the wrong tree.
rm -rf ~/.rlama/<rag-name> Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -X POST http://localhost:11249/rag \ How it starts
The opening of the file, as written. The whole thing — 570 lines — stays where its author put it; the contents beside it link to each section on GitHub.
RLAMA - Local RAG System
RLAMA (Retrieval-Augmented Language Model Adapter) provides fully local, offline RAG for semantic search over your documents.
When to Use This Skill
- Building knowledge bases from local documents
- Searching personal notes, research papers, or code documentation
- Document-based Q&A without sending data to the cloud
- Indexing project documentation for quick semantic lookup
- Creating searchable archives of PDFs, markdown, or code files
Prerequisites
RLAMA requires Ollama running locally:
# Verify Ollama is running
ollama list
# If not running, start it
brew services start ollama # macOS
# or: ollama serve
Quick Reference
Query a RAG (Default: Retrieve-Only)
Always use retrieve-only mode by default. Claude synthesizes far better answers than local 7B models. The raw chunks give Claude direct evidence to reason over and cite.
# DEFAULT: Retrieve top 10 chunks — Claude reads and synthesizes
python3 ~/.claude/skills/rlama/scripts/rlama_retrieve.py <rag-name> "your query"
# More chunks for broad queries
python3 ~/.claude/skills/rlama/scripts/rlama_retrieve.py <rag-name> "your query" -k 20
# JSON output for programmatic use
python3 ~/.claude/skills/rlama/scripts/rlama_retrieve.py <rag-name> "your query" --json
# Force rebuild embedding cache
python3 ~/.claude/skills/rlama/scripts/rlama_retrieve.py <rag-name> "your query" --rebuild-cache
# List RAGs with cache status
python3 ~/.claude/skills/rlama/scripts/rlama_retrieve.py --list
First run per collection builds an embedding cache (~60s for 4K chunks). Subsequent queries are <1s.
Local LLM Query (Fallback Only)
Use rlama run only when Claude is not in the loop (e.g., standalone CLI usage, cron jobs, scripts):
# Local model generates the answer (weaker than Claude synthesis)
rlama run <rag-name> --query "your question here"
# With more context chunks
rlama run <rag-name> --query "explain the authentication flow" --context-size 30
# Show source documents
rlama run <rag-name> --query "what are the API endpoints?" --show-context
What ships with it
13 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.
- README.md 5.5 KB
- references/rlama-commands.md 7.7 KB
- scripts/rlama_batch_ingest.py 10 KB runs code
- scripts/rlama_bench.py 16 KB runs code
- scripts/rlama_dedupe.py 11 KB runs code
- scripts/rlama_list.py 7.9 KB runs code
- scripts/rlama_logger.py 14 KB runs code
- scripts/rlama_manage.py 16 KB runs code
- scripts/rlama_query.py 6.4 KB runs code
- scripts/rlama_rebuild_deduped.py 6.7 KB runs code
- scripts/rlama_resilient.py 28 KB runs code
- scripts/rlama_retrieve.py 30 KB runs code
- scripts/rlama_status.py 9.3 KB runs code
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.
- 8d ago First seen · 570 lines · 70 tokens per session scan C f044a0a093f5
rlama is a skill published in the GitHub repository tdimino/claude-code-minoan (41 stars, last pushed yesterday), licensed MIT. It adds 70 tokens to every session and 4,741 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it C with 2 findings (recursive force delete, makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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