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multi-agent-system-pattern: Skill for Cursor

.cursor/skills/rag-governance/SKILL.md

rag-governance is a skill for Cursor from vpeetla-ai/multi-agent-system-pattern. It costs 54 tokens per session (292 once invoked), scanned A, original, MIT.

A set of rules and implementation guidance for access-aware RAG (retrieval-augmented generation), where a system finds relevant documents before generating an answer. It covers tenant and role filtering, hybrid search, reranking, citations, and human review for sensitive results.

In plain words
What is it for?
Use it when tuning retrieval, adding a Qdrant search adapter, connecting an enterprise RAG service, or testing access denial and expected citations.
Why use it?
It helps prevent unauthorized document chunks from entering search results and makes generated answers traceable to retrieved sources.

Skill for Cursor

Written for Cursor: installed under .cursor/.

This is vpeetla-ai/multi-agent-system-pattern's own configuration. It tells Cursor how to work on multi-agent-system-pattern itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything multi-agent-system-pattern configures →

not rated 2repo 2mo ago A scan 54 tokens original MIT
View source ↗ vpeetla-ai/multi-agent-system-pattern multi-agent-system-pattern.vercel.app
Reuse

Borrowing it

Nothing to install: this file belongs to vpeetla-ai/multi-agent-system-pattern. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/vpeetla-ai/multi-agent-system-pattern/main/.cursor/skills/rag-governance/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/vpeetla-ai/multi-agent-system-pattern

Made for: Cursor.

Wrote 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.

agentmods badge for rag-governance

README.md
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Your own site
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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.

agentmods 80×15 button for rag-governance

Your own site · 80×15
<a href="https://agentmods.dev/skills/vpeetla-ai/multi-agent-system-pattern/rag-governance"><img src="https://agentmods.dev/badge/skills/vpeetla-ai/multi-agent-system-pattern/rag-governance.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 54 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 292 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original 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 5.1 $0.00054 $0.00292
Opus 5.5 $0.00022 $0.00117
Sonnet 5.5 $0.00011 $0.00058
Haiku 4.5 $0.00005 $0.00029

Measured 2mo ago against content hash 4cd65b7adc6f, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

rag-governance 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.

Origin

Copies of this mod

1 near-identical copy found in the catalogue:

.cursor/skills/rag-governance/SKILL.md · 42 lines

What it actually says

RAG Governance

Principles

  1. Access before ranking — filter by tenant/role before vector search
  2. Citations required — every answer cites chunk IDs
  3. HITL for sensitive — low confidence or PII-tagged chunks → gateway pause

Hybrid retrieval

query → embed → vector + BM25 → merge (hybrid_alpha) → rerank → top_k

VAP strategy adapter

  • enterprise_rag strategy in venkat-ai-platform delegates to Enterprise RAG API
  • Env: ENTERPRISE_RAG_API_URL

LoopForge tuning (ODAEU)

  • top_k, hybrid_alpha, rerank_threshold versioned in RAG config tree
  • Lessons stored when eval fails

Reference

  • enterprise_rag_platform — Qdrant adapter, OTLP export
  • loop-engine-agent-platform/src/loop_engine/rag/

Tests

  • Golden queries with expected chunk IDs
  • Access denial: user without role gets empty retrieval, not leaked chunks
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. 2mo ago First seen · 42 lines · 54 tokens per session scan A 4cd65b7adc6f

Subscribe to this mod's changes

rag-governance is a skill published in the GitHub repository vpeetla-ai/multi-agent-system-pattern (2 stars, last pushed 2mo ago), licensed MIT. It adds 54 tokens to every session and 292 once invoked, about $0.0002 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.

Data and AIMulti-AgentPythonai-agentslanggraphtutorial
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