Borrowing it
Nothing to install: this file belongs to soumendrak/ragwatch. 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/soumendrak/ragwatch/main/AGENTS.mdgit clone --depth 1 https://github.com/soumendrak/ragwatchWrote 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/soumendrak/ragwatch/agents-md)<a href="https://agentmods.dev/instructions/soumendrak/ragwatch/agents-md"><img src="https://agentmods.dev/badge/instructions/soumendrak/ragwatch/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/soumendrak/ragwatch/agents-md"><img src="https://agentmods.dev/badge/instructions/soumendrak/ragwatch/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 | $0.02932 | $0.02932 |
| Opus 5.5 | $0.01173 | $0.01173 |
| Sonnet 5.5 | $0.00586 | $0.00586 |
| Haiku 4.5 | $0.00293 | $0.00293 |
Grade A, and why
ragwatch 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 28d 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 — 214 lines — stays where its author put it; the contents beside it link to each section on GitHub.
RAGWatch — AGENTS.md
Version: v0.3 (enterprise hardening + runtime wiring) Tagline: "Quality scores in your RAG traces — computed, not just recorded"
Project Overview
- Name: RAGWatch
- Purpose: OpenTelemetry-native RAG observability Python SDK with semantic quality scores
- Key Differentiator:
chunk_relevance_score+user.feedback_score— computed semantic quality - Instrumentation: Explicit decorators only (use OpenLLMetry for auto-instrumentation)
- RAG Types: Linear RAG (v0.1), multi-stage via composed decorators
- Efficiency: ~1-5 ms overhead per request
- Scope: SDK only. Users bring their own OTel backend.
- Install:
uv add ragwatch(recommended) orpip install ragwatch(core);uv add ragwatch --extra langgraphorpip install ragwatch[langgraph]
Architecture
Instrumentation Strategy
Explicit decorators only — no auto-instrumentation in v0.1.
| Layer | Instrumentation | Reason |
|---|---|---|
| LLM calls | None (use OpenLLMetry) | They do it better; compose via OTel |
| Embedding generation | Explicit | Query embedding stored for relevance score |
| Vector search / retrieval | Explicit | chunk_relevance_score computed here |
| User feedback | Explicit | record_feedback() is user-initiated |
| Agent nodes / workflows | Explicit | User decides span boundaries |
Linear RAG Stages
| Stage | Span Name | Key Attributes |
|---|---|---|
| Embedding | ragwatch.embedding.generate |
model.name, embedding.dimensions, duration_ms |
| Vector Search | ragwatch.retrieval.search |
top_k, chunks.returned, chunk.relevance_score |
| Response | ragwatch.response.emit |
response.length, user.feedback_score |
chunk_relevance_score Plumbing (single-process only)
embedding.pycomputes query embedding → stores in OTelContext(not baggage)context.pymanagesContextVarstorage for query embeddingretrieval.pyreads context → computes cosine similarity → setschunk.relevance_score- Supported dimensions: up to 512-dim
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.
- 28d ago Changed · +2 tokens per session 143a69645ca9
- 2mo ago First seen · 214 lines · 2,930 tokens per session scan A 1c41f6cfe5d2
ragwatch AGENTS.md is an instructions file published in the GitHub repository soumendrak/ragwatch (5 stars, last pushed 3d ago), licensed MIT. It adds 2,932 tokens to every session, about $0.0117 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.
ragwatch AGENTS.md compared with instructions from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| ragwatch AGENTS.mdsoumendrak/ragwatch | Instructions file | 5 | 3d ago | MIT | A | 2,932 | 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 | yesterday | 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 |