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 agents/getreka/reka/rag-opsgit clone --depth 1 https://github.com/getreka/rekaWhat 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 | $0.00042 | $0.00518 |
| Opus 5 | $0.00021 | $0.00259 |
| Sonnet 5 | $0.00008 | $0.00104 |
| Haiku 4.5 | $0.00004 | $0.00052 |
Grade A, and why
rag-ops scanned grade A with 1 finding 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
| RAG API | 3100 | curl localhost:3100/health | The source is not reproduced here
No licence file
A repository with no LICENSE is all rights reserved by default, so the body is not copied here. The metadata, the measurements and the link are.
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 · 64 lines · 42 tokens per session scan A 6b41c0f95b1b
rag-ops is an agent published in the GitHub repository getreka/reka (1 stars, last pushed 2mo ago), with no licence file. It adds 42 tokens to every session and 518 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other agents, from other repositories
llm-workflow-engineer
Use when building LangGraph workflows, agents, RAG systems, structured-output nodes, streaming chat surfaces, or anything LLM-driven with state. Carries the factory's LLM conventions — TypedDict state schemas, node factory closures, named conditional-edge routers, JSON-schema structured output, local-prompt-fallback…
rag-specialist
Expert consultant for retrieval-augmented generation, knowledge bases, intelligent document processing, and semantic search systems. Use proactively for RAG architecture analysis, knowledge system design, document processing strategies, and semantic search optimization. Provides consultation and recommendations only …
vision
Senior AI Engineer — LLMs, autonomous agents, RAG, fine-tuning, model evaluation, AI integrations. Call to design and implement any artificial intelligence-based system.
ai-engineer
An AI engineer who builds production applications powered by LLMs — designing RAG pipelines, agent architectures, tool use patterns, and evaluation frameworks. Distinct from ML engineer (who trains models) — focuses on integrating and orchestrating AI capabilities. Use for LLM application architecture, RAG design…
ai-ml
AI/ML 통합 전문가 + LLM API 최신 모델/SDK 코딩 가이드. RAG 시스템, 문서 분석, OpenAI/Anthropic/Gemini/Ollama 최신 API 보장. "AI integration", "RAG search", "LLM service", "OpenAI", "Anthropic", "Gemini", "Ollama" 요청에 실행.
ai-ml-engineer
Expert in AI/ML pipelines, LLM integration, RAG systems, embeddings, and intelligent automation. Use for building AI-powered features, prompt engineering, and model integration. Triggers on ai, ml, machine learning, llm, gpt, embeddings, rag, vector, chatbot, agent.