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 skills add bugrabilge/bilge-development-kit --skill rag-implementationgit clone --depth 1 https://github.com/bugrabilge/bilge-development-kitWrote 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/bugrabilge/bilge-development-kit/rag-implementation)<a href="https://agentmods.dev/skills/bugrabilge/bilge-development-kit/rag-implementation"><img src="https://agentmods.dev/badge/skills/bugrabilge/bilge-development-kit/rag-implementation/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/bugrabilge/bilge-development-kit/rag-implementation"><img src="https://agentmods.dev/badge/skills/bugrabilge/bilge-development-kit/rag-implementation.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 | $0.00042 | $0.02777 |
| Opus 5 | $0.00021 | $0.01388 |
| Sonnet 5 | $0.00008 | $0.00555 |
| Haiku 4.5 | $0.00004 | $0.00278 |
Grade A, and why
rag-implementation 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.
This is a copy
84% identical to rag-implementation — 25 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 424 lines — stays where its author put it; the contents beside it link to each section on GitHub.
RAG Implementation
Master Retrieval-Augmented Generation (RAG) to build LLM applications that provide accurate, grounded responses using external knowledge sources.
Use this skill when
- Building Q&A systems over proprietary documents
- Creating chatbots with current, factual information
- Implementing semantic search with natural language queries
- Reducing hallucinations with grounded responses
- Enabling LLMs to access domain-specific knowledge
- Building documentation assistants
- Creating research tools with source citation
Do not use this skill when
- You only need purely generative writing without retrieval
- The dataset is too small to justify embeddings
- You cannot store or process the source data safely
Instructions
- Define the corpus, update cadence, and evaluation targets.
- Choose embedding models and vector store based on scale.
- Build ingestion, chunking, and retrieval with reranking.
- Evaluate with grounded QA metrics and monitor drift.
Safety
- Redact sensitive data and enforce access controls.
- Avoid exposing source documents in responses when restricted.
Core Components
1. Vector Databases
Purpose: Store and retrieve document embeddings efficiently
Options:
- Pinecone: Managed, scalable, fast queries
- Weaviate: Open-source, hybrid search
- Milvus: High performance, on-premise
- Chroma: Lightweight, easy to use
- Qdrant: Fast, filtered search
- FAISS: Meta's library, local deployment
2. Embeddings
Purpose: Convert text to numerical vectors for similarity search
Models:
- text-embedding-ada-002 (OpenAI): General purpose, 1536 dims
- all-MiniLM-L6-v2 (Sentence Transformers): Fast, lightweight
- e5-large-v2: High quality, multilingual
- Instructor: Task-specific instructions
- bge-large-en-v1.5: SOTA performance
3. Retrieval Strategies
Approaches:
- Dense Retrieval: Semantic similarity via embeddings
- Sparse Retrieval: Keyword matching (BM25, TF-IDF)
- Hybrid Search: Combine dense + sparse
- Multi-Query: Generate multiple query variations
- HyDE: Generate hypothetical documents
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 · 424 lines · 42 tokens per session scan A 35b1404b555d
rag-implementation is a skill published in the GitHub repository bugrabilge/bilge-development-kit (10 stars, last pushed 4mo ago), licensed MIT. It adds 42 tokens to every session and 2,777 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 84% identical to rag-implementation, differing in 25 lines, and is treated as a copy.
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Implement efficient similarity search with vector databases. Use when building semantic search, implementing nearest neighbor queries, or optimizing retrieval performance.
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