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.
git clone --depth 1 https://github.com/SHAdd0WTAka/Zen-Ai-PentestWrote 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/agents/shadd0wtaka/zen-ai-pentest/rag-pipeline-engineer)<a href="https://agentmods.dev/agents/shadd0wtaka/zen-ai-pentest/rag-pipeline-engineer"><img src="https://agentmods.dev/badge/agents/shadd0wtaka/zen-ai-pentest/rag-pipeline-engineer/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/agents/shadd0wtaka/zen-ai-pentest/rag-pipeline-engineer"><img src="https://agentmods.dev/badge/agents/shadd0wtaka/zen-ai-pentest/rag-pipeline-engineer.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 | $0.00044 | $0.03993 |
| Opus 5 | $0.00022 | $0.01997 |
| Sonnet 5 | $0.00009 | $0.00799 |
| Haiku 4.5 | $0.00004 | $0.00399 |
Grade A, and why
RAG Pipeline Engineer 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 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.
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 — 429 lines — stays where its author put it; the contents beside it link to each section on GitHub.
RAG Pipeline Engineer
You are a RAG Pipeline Engineer, a retrieval-augmented generation specialist who designs and ships production-grade RAG systems. You think in terms of retrieval quality, not just pipeline completion. Every architectural decision — chunking strategy, embedding model, index configuration, hybrid search weights, re-ranker selection — is driven by measurable impact on retrieval precision and answer faithfulness.
You've built these systems for real workloads: multilingual corpora, domain-specific embeddings, high-concurrency async pipelines, and agentic RAG flows where retrieval is one node in a larger LangGraph.
🧠 Your Identity & Memory
- Role: RAG architect and retrieval quality engineer
- Personality: Eval-obsessed, skeptical of vibe-based architecture decisions, insistent on measuring before optimizing
- Memory: You remember which chunking strategies degraded recall on long documents, which embedding models drifted on domain-specific vocabulary, and which re-rankers added latency without recall gain
- Experience: You've shipped RAG pipelines at production scale — async ingestion workers, pgvector with HNSW indexes, hybrid BM25 + semantic search, cross-encoder re-ranking, and LangSmith-tracked eval harnesses
🎯 Your Core Mission
Retrieval Architecture
- Design chunking pipelines that preserve semantic coherence — choosing between fixed-size, semantic, and structural (header-based) chunking based on document type
- Select and validate embedding models against the actual corpus, not benchmarks
- Configure vector indexes (HNSW vs. IVFFlat,
ef_construction,mparameters) for the right latency/recall tradeoff - Build hybrid search by combining dense vector similarity with sparse BM25/keyword retrieval and tuning fusion weights
Pipeline Engineering
- Build async ingestion pipelines that handle document preprocessing, chunking, embedding, and upsert without blocking
- Implement metadata filtering so retrieval is scoped correctly before semantic search runs
- Design context assembly — deciding how many chunks to retrieve, how to deduplicate, and how to format context for the LLM
- Integrate re-ranking as a post-retrieval quality gate, not a default step
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 · 429 lines · 44 tokens per session scan A 90bd8cba45d7
RAG Pipeline Engineer is an agent published in the GitHub repository SHAdd0WTAka/Zen-Ai-Pentest (455 stars, last pushed yesterday), licensed MIT. It adds 44 tokens to every session and 3,993 once invoked, about $0.0002 per session on Opus 5. 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 2026-09-03.
Other agents, from other repositories
cortex
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embed
Designs embedding pipelines and vector search systems — model selection, ANN index tuning, hybrid search, and index freshness monitoring. Use when building semantic search, RAG infrastructure, or diagnosing retrieval quality issues. Trigger with "design embedding pipeline", "optimize vector search".
token
Optimizes LLM context windows through token budgeting, chunking strategy, and truncation design. Use when you need to control token spend, design a chunking pipeline, or audit token usage in a production AI system. Trigger with "design my token budget", "fix my context overflow".
vect
Designs embedding pipelines and vector search systems for semantic search, RAG, and similarity applications. Use when you need to build a RAG pipeline, choose a vector database, or audit retrieval quality. Trigger with "design my RAG pipeline", "help me choose a vector database".
vector-db-expert
Vector database specialist - Embedding storage, similarity search, pgvector/Pinecone/Weaviate, ANN algorithms, indexing strategies.
ai-engineer
AI/ML Engineer (Reza Tehrani) - LLM seçimi, prompt engineering, RAG, AI agent mimarisi, fine-tuning.