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 ItamarZand88/awesome-agent-conventions --skill wshobson-rag-implementationgit clone --depth 1 https://github.com/ItamarZand88/awesome-agent-conventionsWrote 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/itamarzand88/awesome-agent-conventions/wshobson-rag-implementation)<a href="https://agentmods.dev/skills/itamarzand88/awesome-agent-conventions/wshobson-rag-implementation"><img src="https://agentmods.dev/badge/skills/itamarzand88/awesome-agent-conventions/wshobson-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/itamarzand88/awesome-agent-conventions/wshobson-rag-implementation"><img src="https://agentmods.dev/badge/skills/itamarzand88/awesome-agent-conventions/wshobson-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 | $0.00000 | $0.01122 |
| Opus 5.5 | $0.00000 | $0.00449 |
| Sonnet 5.5 | $0.00000 | $0.00224 |
| Haiku 4.5 | $0.00000 | $0.00112 |
Grade A, and why
wshobson-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.
This is a copy
95% identical to rag-implementation — 1 line 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 — 140 lines — stays where its author put it; the contents beside it link to each section on GitHub.
name: rag-implementation description: Build Retrieval-Augmented Generation (RAG) systems for LLM applications with vector databases and semantic search. Use when implementing knowledge-grounded AI, building document Q&A systems, or integrating LLMs with external knowledge bases.
RAG Implementation
Master Retrieval-Augmented Generation (RAG) to build LLM applications that provide accurate, grounded responses using external knowledge sources.
When to Use This Skill
- 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 | Claude apps (Anthropic recommended) |
| voyage-code-3 | 1024 | Code search |
| text-embedding-3-large | 3072 | OpenAI apps, high accuracy |
| text-embedding-3-small | 1536 | OpenAI apps, cost-effective |
| bge-large-en-v1.5 | 1024 | Open source, local deployment |
| multilingual-e5-large | 1024 | Multi-language support |
3. Retrieval Strategies
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 · 140 lines · 0 tokens per session scan A ea9920114973
wshobson-rag-implementation is a skill published in the GitHub repository ItamarZand88/awesome-agent-conventions (35 stars, last pushed 3mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,122 tokens. A static security scan graded it A with 0 findings. It is 95% identical to rag-implementation, differing in 1 line, and is treated as a copy.
Other skills, from other repositories
llm-app-development
LLM app development with RAG, prompt engineering, vector databases, and AI agents.
pageindex
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llamaindex
Skill "llamaindex" from ashish7802/awesome-api-skills, covering llamaindex skill, ecosystem graph preview, recommended next skills, quick start and production patterns.
pinecone
Pinecone stores vector embeddings for fast similarity search, serving as the memory layer for RAG (Retrieval-Augmented Generation) applications.
qdrant
Qdrant is a production-grade vector similarity search engine with extended payload-based filtering, hybrid search (dense + sparse vectors), and multi-tenant collection partitioning.