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 LuizEduPP/Rememb --skill rag-implementationgit clone --depth 1 https://github.com/LuizEduPP/RemembWrote 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/luizedupp/rememb/rag-implementation)<a href="https://agentmods.dev/skills/luizedupp/rememb/rag-implementation"><img src="https://agentmods.dev/badge/skills/luizedupp/rememb/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/luizedupp/rememb/rag-implementation"><img src="https://agentmods.dev/badge/skills/luizedupp/rememb/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 | $0.00049 | $0.03790 |
| Opus 5.5 | $0.00020 | $0.01516 |
| Sonnet 5.5 | $0.00010 | $0.00758 |
| Haiku 4.5 | $0.00005 | $0.00379 |
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 1mo 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 — 563 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.
When to Use
- 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
Core Components
1. Vector Databases
Purpose: Store and retrieve document embeddings efficiently
Options:
- Pinecone: Managed, scalable, serverless
- Weaviate: Open-source, hybrid search, GraphQL
- Milvus: High performance, on-premise
- Chroma: Lightweight, easy to use, local development
- Qdrant: Fast, filtered search, Rust-based
- pgvector: PostgreSQL extension, SQL integration
2. Embeddings
Purpose: Convert text to numerical vectors for similarity search
Models (2026):
| Model | Dimensions | Best For |
|---|---|---|
| voyage-3-large | 1024 | General-purpose semantic search |
| voyage-code-3 | 1024 | Code search |
| text-embedding-3-large | 3072 | High-accuracy embedding pipelines |
| text-embedding-3-small | 1536 | Cost-efficient embedding pipelines |
| bge-large-en-v1.5 | 1024 | Open source, local deployment |
| multilingual-e5-large | 1024 | Multi-language support |
3. Retrieval Strategies
Approaches:
- Dense Retrieval: Semantic similarity via embeddings
- Sparse Retrieval: Keyword matching (BM25, TF-IDF)
- Hybrid Search: Combine dense + sparse with weighted fusion
- Multi-Query: Generate multiple query variations
- HyDE: Generate hypothetical documents for better retrieval
4. Reranking
Purpose: Improve retrieval quality by reordering results
Methods:
- Cross-Encoders: BERT-based reranking (ms-marco-MiniLM)
- Cohere Rerank: API-based reranking
- Maximal Marginal Relevance (MMR): Diversity + relevance
- LLM-based: Use LLM to score relevance
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.
- 1mo ago First seen · 563 lines · 49 tokens per session scan A 9d79274a1d48
rag-implementation is a skill published in the GitHub repository LuizEduPP/Rememb (5 stars, last pushed 11d ago), licensed MIT. It adds 49 tokens to every session and 3,790 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-08-31.
Other skills, from other repositories
karpathy-llm-wiki
Use when building or maintaining a personal LLM-powered knowledge base. Triggers: ingesting sources into a wiki, querying wiki knowledge, linting wiki quality, 'add to wiki', 'what do I know about', or any mention of 'LLM wiki' or 'Karpathy wiki'.
setup
Configure the Qdrant Power after installation. Use this skill for missing uvx, missing environment variables, unapproved environment variables, unavailable Qdrant tools, "Failed to connect" errors, and setup requests.
RAG Workflow Planner
Designs a complete Retrieval-Augmented Generation (RAG) pipeline for a given use case, including chunking strategy, embedding model selection, and retrieval approach.
brain-search
Semantic search over the INITE Brain knowledge graph via the searchknowledge MCP tool. Use when the user wants to find facts, entities, or evidence about people/companies/objects in their tenant, especially when the question is fuzzy or natural-language. Supports bitemporal "as of" queries.
pn-rag-evaluation
Evaluate retrieval-augmented generation pipelines — golden sets, automated metrics (incl. RAGAS-style), human rubrics, regression gates in CI. Use when building or changing RAG (chunks, embeddings, rerankers, prompts) and need quality proof, not vibes.
ai-engineering-toolkit
6 production-ready AI engineering workflows: prompt evaluation (8-dimension scoring), context budget planning, RAG pipeline design, agent security audit (65-point checklist), eval harness building, and product sense coaching.