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 skills/is-bo/fullstack-forge-skill/rag-implementationnpx skills add is-bo/fullstack-forge-skill --skill rag-implementationgit clone --depth 1 https://github.com/is-bo/fullstack-forge-skillWrote 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/is-bo/fullstack-forge-skill/rag-implementation)<a href="https://agentmods.dev/skills/is-bo/fullstack-forge-skill/rag-implementation"><img src="https://agentmods.dev/badge/skills/is-bo/fullstack-forge-skill/rag-implementation.svg" alt="Measured on agentmods" 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 | $0.01078 |
| Opus 5 | $0.00024 | $0.00539 |
| Sonnet 5 | $0.00010 | $0.00216 |
| Haiku 4.5 | $0.00005 | $0.00108 |
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 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.
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 wshobson-rag-implementation — 3 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 — 139 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 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
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 ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 139 lines · 49 tokens per session scan A 0dc4aeb9a999
rag-implementation is a skill published in the GitHub repository is-bo/fullstack-forge-skill (2 stars, last pushed yesterday), licensed Apache-2.0. It adds 49 tokens to every session and 1,078 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 95% identical to wshobson-rag-implementation, differing in 3 lines, and is treated as a copy.
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