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.
npx agentmods add skills/vpeetla-ai/react-agent-pattern/rag-governancenpx skills add vpeetla-ai/react-agent-pattern --skill rag-governancegit clone --depth 1 https://github.com/vpeetla-ai/react-agent-patternWrote 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/skills/vpeetla-ai/react-agent-pattern/rag-governance)<a href="https://agentmods.dev/skills/vpeetla-ai/react-agent-pattern/rag-governance"><img src="https://agentmods.dev/badge/skills/vpeetla-ai/react-agent-pattern/rag-governance.svg" alt="Measured on agentmods" 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 | $0.00054 | $0.00292 |
| Opus 5 | $0.00027 | $0.00146 |
| Sonnet 5 | $0.00011 | $0.00058 |
| Haiku 4.5 | $0.00005 | $0.00029 |
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 4d 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.
This is a copy
100% identical to rag-governance — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
What it actually says
RAG Governance
Principles
- Access before ranking — filter by tenant/role before vector search
- Citations required — every answer cites chunk IDs
- 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_ragstrategy in venkat-ai-platform delegates to Enterprise RAG API- Env:
ENTERPRISE_RAG_API_URL
LoopForge tuning (ODAEU)
top_k,hybrid_alpha,rerank_thresholdversioned in RAG config tree- Lessons stored when eval fails
Reference
enterprise_rag_platform— Qdrant adapter, OTLP exportloop-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
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.
- 4d ago First seen · 42 lines · 54 tokens per session scan A 4cd65b7adc6f
rag-governance is a skill published in the GitHub repository vpeetla-ai/react-agent-pattern (2 stars, last pushed yesterday), licensed MIT. It adds 54 tokens to every session and 292 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to rag-governance, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
rag-governance
Implement access-aware RAG in Enterprise RAG or VAP: hybrid retrieval, rerank, citations, AegisAI HITL for sensitive chunks. Use when tuning retrieval, adding Qdrant adapter, or wiring enterpriseragplatform.
tdd-agent-loops
Test-driven development for agent systems: red-green-refactor on graphs, mocked LLM fixtures, pytest-asyncio, trace assertions. Use when adding agent nodes, fixing loop bugs, or building pattern repos.
governed-ai-stack
Maps tasks to the vpeetla-ai 6-layer reference stack (VAP, AegisAI, Enterprise RAG, AegisLoop, Content Factory, LoopForge). Use when choosing which repo to change, designing integrations, or explaining architecture.
loop-engineering
Implement ODAEU harness loops, RAG evolve tuning, and procedural memory in LoopForge or similar systems. Use when building self-improving agents, eval gates, MCP tool bridges, or RAG version trees.
cuml-machine-learning
Use for GPU-accelerated machine learning on tabular data using NVIDIA cuML. Triggers when tasks involve classification, regression, clustering, dimensionality reduction, or model training on datasets.
rag-knowledge
RAG domain knowledge — architecture, component routing, rules, and reference tables. Use when working on any file under rag/.