Borrowing it
Nothing to install: this file belongs to qduc/code-rag. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/qduc/code-rag/main/AGENTS.mdgit clone --depth 1 https://github.com/qduc/code-ragWrote 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/instructions/qduc/code-rag/agents-md)<a href="https://agentmods.dev/instructions/qduc/code-rag/agents-md"><img src="https://agentmods.dev/badge/instructions/qduc/code-rag/agents-md/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/instructions/qduc/code-rag/agents-md"><img src="https://agentmods.dev/badge/instructions/qduc/code-rag/agents-md.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 | $0.01347 | $0.01347 |
| Opus 5.5 | $0.00539 | $0.00539 |
| Sonnet 5.5 | $0.00269 | $0.00269 |
| Haiku 4.5 | $0.00135 | $0.00135 |
Grade A, and why
code-rag AGENTS.md 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 2mo 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 — 124 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Code-RAG: Quick Start for Developers
What is Code-RAG?
Code-RAG is a CLI tool that makes codebases searchable using semantic search. It converts source code into vector embeddings, stores them in a database, and lets you query them using natural language.
Example: Instead of grepping for function names, ask "authentication logic" and find all relevant auth code.
Architecture Overview
┌─────────────┐
│ CLI / MCP │ Entry points
└──────┬──────┘
│
┌──────▼──────┐
│ API │ Orchestration layer (CodeRAGAPI)
└──────┬──────┘
│
┌────────────┼────────────┬────────────┐
│ │ │ │
┌───▼───┐ ┌───▼───┐ ┌───▼───┐ ┌───▼───┐
│Process│ │Search │ │Manage │ │ Embed │
└───┬───┘ └───┬───┘ └───┬───┘ └───┬───┘
│ │ │ │
┌───▼───┐ ┌───▼───┐ ┌───▼───┐ ┌───▼───┐
│Chunker│ │Rerank │ │Index │ │Storage│
└───────┘ └───────┘ └───────┘ └───────┘
Key Design: Orchestrated Plugin architecture. The CodeRAGAPI centralizes logic, while specialized components handle chunking, indexing, search analysis, and storage.
Components
1. API Layer (src/code_rag/api.py)
- What: The central hub for all Code-RAG operations.
- How: Integrates embedding, database, reranking, and indexing logic. Used by both CLI and MCP.
- Features: Session tracking, auto-generated collection names, and unified indexing flow.
2. File Processor & Chunker
- What: Discovers source files and breaks them into logical chunks.
- How: Uses
SyntaxChunker(tree-sitter based) for code-aware splitting, falling back to line-based. - Output: Text chunks with rich metadata (file path, line numbers, symbol names).
3. Metadata Index (src/code_rag/index/metadata_index.py)
- What: Tracks state of indexed files for incremental updates.
- How: Stores
mtime,size, andsha256hashes. - Benefit: Only re-indexes modified files, significantly speeding up subsequent runs.
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.
- 2mo ago First seen · 124 lines · 1,347 tokens per session scan A 984bda5b5776
code-rag AGENTS.md is an instructions file published in the GitHub repository qduc/code-rag (1 stars, last pushed 5mo ago), licensed MIT. It adds 1,347 tokens to every session, about $0.0054 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.
code-rag AGENTS.md compared with instructions from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| code-rag AGENTS.mdqduc/code-rag | Instructions file | 1 | 5mo ago | MIT | A | 1,347 | Codex, OpenCode |
| vscode buildNext.instructions.mdmicrosoft/vscode | Instructions file | 195k | yesterday | MIT | A | 9,719 | GitHub Copilot |
| next.js AGENTS.mdvercel/next.js | Instructions file | 143k | today | MIT | A | 7,461 | Codex, OpenCode |
| langchain AGENTS.mdlangchain-ai/langchain | Instructions file | 149k | yesterday | MIT | A | 4,680 | Codex, OpenCode |
| vscode oss-third-party-notices.instructions.mdmicrosoft/vscode | Instructions file | 195k | yesterday | MIT | A | 5,516 | GitHub Copilot |
| vscode copilot-instructions.mdmicrosoft/vscode | Instructions file | 195k | yesterday | MIT | A | 3,093 | GitHub Copilot |
| deepseek-harness AGENTS.mddeepseek-ai/deepseek-harness | Instructions file | 274k | 2d ago | MIT | A | 4,313 | Codex, OpenCode |