ms-agent-framework-rag

A guide for building retrieval-augmented generation, or RAG, applications in C# with Microsoft Agent Framework. RAG lets an agent search indexed documents and use the results when answering or acting.

In plain words
What is it for?
Use it to create C# systems that index documents, perform semantic search, retrieve relevant information, and coordinate intelligent agents.
Why use it?
It explains how to connect document search, vector databases, language models, and agent coordination in one application.

Skill for Claude CodeCodex

Install

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.

agentmods
npx agentmods add skills/shuyu-labs/webcode/ms-agent-framework-rag
Any agent
npx skills add shuyu-labs/WebCode --skill ms-agent-framework-rag
Clone the repo
git clone --depth 1 https://github.com/shuyu-labs/WebCode

Made for: Claude Code, Codex.

Per session 64 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 979 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin unknown No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00064 $0.00979
Opus 5 $0.00032 $0.00490
Sonnet 5 $0.00013 $0.00196
Haiku 4.5 $0.00006 $0.00098

Measured 2d ago against content hash f4dff321cca3, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

ms-agent-framework-rag 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.

The scan reads SKILL.md. This mod also ships 1 executable file (scripts/example.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

skills/codex/ms-agent-framework-rag/SKILL.md · 161 lines

The source is not reproduced here

No licence file

A repository with no LICENSE is all rights reserved by default, so the body is not copied here. The metadata, the measurements and the link are.

Read it on GitHub

Files

What ships with it

6 files 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.

Changes

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.

  1. 2d ago First seen · 161 lines · 64 tokens per session scan A f4dff321cca3

Subscribe to this mod's changes

ms-agent-framework-rag is a skill published in the GitHub repository shuyu-labs/WebCode (284 stars, last pushed 2mo ago), with no licence file. It adds 64 tokens to every session and 979 once invoked, about $0.0003 per session on Opus 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 2026-08-30.

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