rag-implementation SKILL.md skillA
rag-implementation from dicklesworthstone/pi_agent_rust is an agent skill: a SKILL.md folder an AI agent loads when a task calls for it.
mdr add dicklesworthstone/pi_agent_rust/rag-implementation@git:20260205.788be98Pins git:20260205.788be98 in mdr.lock. Pin the fingerprint instead.
- Audit grade
- A, 17 of 17 checks passed
- Kind
- SKILL.md skill
- Repository
- dicklesworthstone/pi_agent_rust
- Path
tests/ext_conformance/artifacts/agents-wshobson/llm-application-dev/skills/rag-implementation/SKILL.md- Versions recorded
- 1
- Latest version
- git:20260205.788be98, committed Feb 5, 2026
- Size
- 16,840 bytes
- Repository stars
- 1,966
- License
- a license file GitHub does not identify
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.
Collected from public GitHub. Every version is saved exactly as it was and never changes. Install it at an exact version with the mdr tool, and every file is checked before it reaches your agent. The main file is checked against the SHA-256 fingerprint recorded here, and the others against the fingerprints GitHub keeps for its source commit. The automatic checks below grade the latest version, and the same checks always give the same file the same grade.
What the file says
# 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 | |-------|------------|----------| …
Read the whole file at its exact version.
How to install
mdr add dicklesworthstone/pi_agent_rust/rag-implementation@sha256:d5291ecdfa63326bThe line at the top of this page pins the label git:20260205.788be98, which is what most people want. This one pins the sha256 fingerprint of the contents instead, so it survives a label being moved. Either way the exact version is written to mdr.lock, and mdr install downloads and checks those contents again, so it installs them exactly or stops with an error.
[](https://markdownregistry.com/a/art_zjzknectn3dvwjt3)
6 badge image fetches in 30 days. What the badge shows.
Versions
The registry has recorded 1 version of this file, committed on Feb 5, 2026.
Audit of the latest version
- pass: Frontmatter block present
- pass: Frontmatter declares a name
- pass: Frontmatter declares a description
- pass: Size between 200 bytes and 200 KB (16840 bytes)
- pass: No zero-width or bidi control characters
- pass: No instruction hidden inside an HTML comment
- pass: No link to an exfiltration or paste host
- pass: No credential-shaped string
- pass: No instruction to send local credentials anywhere
- pass: No text hidden with inline styles
- pass: No prompt-injection phrasing
- pass: No curl or wget piped into a shell
- pass: No recursive delete of root, home or parent
- pass: No instruction to read or print local credentials
- pass: No base64 blob over 200 characters
- pass: No link to a raw IP address
- pass: No script tag
Source
dicklesworthstone/pi_agent_rust · 1,966 stars · license a license file GitHub does not identify · pushed 2026-11-06 · branch main
API
GET https://markdownregistry.com/api/v1/artifacts/art_zjzknectn3dvwjt3 GET https://markdownregistry.com/api/v1/resolve?ref=dicklesworthstone/pi_agent_rust/rag-implementation GET https://markdownregistry.com/api/v1/blob/d5291ecdfa63326bcfd2c83270cd4d5cd0a657414703daeb6e3485efa6a50e96
Your AI assistant does the legwork. You hear about the deals worth your word. Hand yours the standing instructions at modelranch.com and it joins the network that reads files like this one.
Guides: the SKILL.md format and its required fields · what to check before you install a skill · how to pin a skill to an exact version · where to find agent skills · keeping a team on the same skills · which agent file does what.
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