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/multi-agent-system-pattern/governed-ai-stacknpx skills add vpeetla-ai/multi-agent-system-pattern --skill governed-ai-stackgit clone --depth 1 https://github.com/vpeetla-ai/multi-agent-system-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/multi-agent-system-pattern/governed-ai-stack)<a href="https://agentmods.dev/skills/vpeetla-ai/multi-agent-system-pattern/governed-ai-stack"><img src="https://agentmods.dev/badge/skills/vpeetla-ai/multi-agent-system-pattern/governed-ai-stack.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 5.1 | $0.00059 | $0.00361 |
| Opus 5 | $0.00030 | $0.00180 |
| Sonnet 5 | $0.00012 | $0.00072 |
| Haiku 4.5 | $0.00006 | $0.00036 |
Grade A, and why
governed-ai-stack 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 5d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- governed-ai-stack — 100% identical, 0 lines differ
What it actually says
Governed AI Stack
Read CONTEXT.md for terms.
Layer routing
| If the task is about… | Work in… |
|---|---|
| Multi-agent orchestration, RAG strategies, notify | venkat-ai-platform |
| Policy, HITL, audit, tool authorization | aegisai-enterprise-agent-platform |
| Retrieval, citations, access control | enterprise_rag_platform |
| Missions, traces, eval gates, FinOps | aegisloop-agentops-workbench |
| Content pipeline, publish, cron | ai-content-factory |
| Self-improving loops, repo fix, RAG tuning | loop-engine-agent-platform |
| ADRs, case studies, portfolio copy | ai-architecture-portfolio |
| Public site, ecosystem wiring | venkat-ai-portfolio |
| Single pattern reference (ReAct, etc.) | *-agent-pattern repos |
Integration rules
- VAP delegates side effects; AegisAI authorizes them
- Enterprise RAG feeds VAP strategies; does not replace gateway
- LoopForge improves configs/prompts; does not replace orchestration
Essay
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.
- 5d ago First seen · 36 lines · 59 tokens per session scan A dc2a3c45a521
governed-ai-stack is a skill published in the GitHub repository vpeetla-ai/multi-agent-system-pattern (2 stars, last pushed today), licensed MIT. It adds 59 tokens to every session and 361 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 2026-08-31.
Other skills, from other repositories
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.
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.
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.
agent-builder
Build production-ready LLM agents with LangGraph, tool use, memory, streaming, and error handling. Use when designing or implementing an AI agent, multi-agent system, or agentic workflow.