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 skills add roedyrustam/vibes-plug --skill multi-agent-orchestrationgit clone --depth 1 https://github.com/roedyrustam/vibes-plugWrote 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/roedyrustam/vibes-plug/multi-agent-orchestration)<a href="https://agentmods.dev/skills/roedyrustam/vibes-plug/multi-agent-orchestration"><img src="https://agentmods.dev/badge/skills/roedyrustam/vibes-plug/multi-agent-orchestration/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/skills/roedyrustam/vibes-plug/multi-agent-orchestration"><img src="https://agentmods.dev/badge/skills/roedyrustam/vibes-plug/multi-agent-orchestration.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 findings, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 157 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
- medium Excessive Agency · line 245 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00071 | $0.07143 |
| Opus 5.5 | $0.00028 | $0.02857 |
| Sonnet 5.5 | $0.00014 | $0.01429 |
| Haiku 4.5 | $0.00007 | $0.00714 |
Grade A, and why
multi-agent-orchestration 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.
How it starts
The opening of the file, as written. The whole thing — 597 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Multi-Agent Orchestration Expert (2026 Edition)
English
Orchestration & Integration
- Swarm Directorship & Planning:
brainstorming,zero-to-prod-orchestrator,speculative-multi-draft-synthesizer - Foundation Models & Providers:
ai-llm-integration-expert,frontier-ai-models-expert,gemini-agent-booster,vercel-ai-sdk-expert - Tool Execution & MCP Protocols:
mcp-server-architect,parallel-tool-orchestrator,doku-mcp-server - Agent Languages & Frameworks:
pydantic-ai-expert,structured-output-contracts,python-programming-expert - Memory, State & Telemetry:
session-memory-manager,agentic-memory-architect,data-telemetry-expert - Security & Quality Gates:
anti-slop,mcp-sampling-critic,autonomous-red-teamer,zero-trust-secret-vault
Description
Expert guide for designing, building, and deploying production-grade multi-agent AI systems. Covers core agentic design patterns (Prompt Chaining, Routing, Parallelization, Orchestrator-Workers, Evaluator-Optimizer), stateful graph engines (LangGraph, OpenAI Agents SDK, Google ADK, Mastra.ai), shared episodic/semantic memory, tool execution sandboxes, and human-in-the-loop (HITL) guardrails.
Swarm Synergy: This skill acts as a master orchestrator when combined with mcp-server-architect (for external tool integration) and ai-llm-integration-expert (for foundation model setup). Together, they form a complete, end-to-end AI Engineering Swarm.
Trigger Conditions
- Building autonomous AI agents that execute complex, multi-step tasks across several domains.
- Designing systems where multiple specialized AI agents collaborate, deliberate, and cross-validate.
- Implementing stateful, graph-based agent workflows with LangGraph, OpenAI Agents SDK, or Google ADK.
- Implementing Anthropic agentic design patterns: Evaluator-Optimizer loops, Orchestrator-Workers, or Routing.
- Integrating human-in-the-loop (HITL) pause checkpoints for high-risk actions (code execution, database migrations, financial transactions).
- Evaluating and selecting agent architectures across Python, TypeScript, and multi-platform swarms.
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 Changed · +206 lines · +71 tokens per session ed78483e401f
- 5d ago Changed · +2 lines · -67 tokens per session 19717603685a
- 8d ago Changed b0911eac9b43
- 17d ago Changed 17dda8e67e8c
- 22d ago Changed c69621f7e4e0
- 23d ago Changed 0896fafdbced
- 25d ago Changed d832186f7e90
- 27d ago Changed · +10 lines 8401efcbc1a0
- 1mo ago Changed · +124 lines 8b518b5ef630
- 1mo ago First seen · 255 lines · 67 tokens per session scan A 499b81597967
multi-agent-orchestration is a skill published in the GitHub repository roedyrustam/vibes-plug (113 stars, last pushed yesterday), licensed MIT. It adds 71 tokens to every session and 7,143 once invoked, about $0.0003 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 2026-09-03.
Other skills, from other repositories
ai-engineering-toolkit
Overview of six AI engineering workflows: prompt evaluation (8-dimension scoring), context budget planning, RAG pipeline design, agent security audit (65-point checklist), eval harness building, and product sense coaching.
ai-prompt-regression-testing
Use when comparing prompt changes against a fixed evaluation set: record actual model settings, required checks, missing results, judge limitations and case-level regression evidence.
ai-engineering-toolkit
6 production-ready AI engineering workflows: prompt evaluation (8-dimension scoring), context budget planning, RAG pipeline design, agent security audit (65-point checklist), eval harness building, and product sense coaching.
ai-prompt-regression-testing
Prompt engineering regression test matrix register: baseline outputs, semantic drift thresholds, judge evaluations, and golden dataset.
prompt-modeler
Generate structured strategic prompts with diagnostic and multiple options. Triggers on: /modelar-prompt.
ai-engineering-toolkit
6 production-ready AI engineering workflows: prompt evaluation (8-dimension scoring), context budget planning, RAG pipeline design, agent security audit (65-point checklist), eval harness building, and product sense coaching.