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 javalenciacai/QASkills --skill multi-agent-orchestrationgit clone --depth 1 https://github.com/javalenciacai/QASkillsWrote 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/javalenciacai/qaskills/multi-agent-orchestration)<a href="https://agentmods.dev/skills/javalenciacai/qaskills/multi-agent-orchestration"><img src="https://agentmods.dev/badge/skills/javalenciacai/qaskills/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/javalenciacai/qaskills/multi-agent-orchestration"><img src="https://agentmods.dev/badge/skills/javalenciacai/qaskills/multi-agent-orchestration.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 | $0.00055 | $0.03498 |
| Opus 5 | $0.00028 | $0.01749 |
| Sonnet 5 | $0.00011 | $0.00700 |
| Haiku 4.5 | $0.00006 | $0.00350 |
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 10d 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 — 580 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Multi-Agent Orchestration
Design and orchestrate sophisticated multi-agent systems where specialized agents collaborate to solve complex problems, combining different expertise and perspectives.
Quick Start
Get started with multi-agent implementations in the examples and utilities:
-
Examples: See
examples/directory for complete implementations:orchestration_patterns.py- Sequential, parallel, hierarchical, and consensus orchestrationframework_implementations.py- Templates for CrewAI, AutoGen, LangGraph, and Swarm
-
Utilities: See
scripts/directory for helper modules:agent_communication.py- Message broker, shared memory, and communication protocolsworkflow_management.py- Workflow execution, optimization, and monitoringbenchmarking.py- Team performance and agent effectiveness metrics
Overview
Multi-agent systems decompose complex problems into specialized sub-tasks, assigning each to an agent with relevant expertise, then coordinating their work toward a unified goal.
When Multi-Agent Systems Shine
- Complex Workflows: Tasks requiring multiple specialized roles
- Domain-Specific Expertise: Finance, legal, HR, engineering need different knowledge
- Parallel Processing: Multiple agents work on different aspects simultaneously
- Collaborative Reasoning: Agents debate, refine, and improve solutions
- Resilience: Failures in one agent don't break the entire system
- Scalability: Easy to add new specialized agents
Architecture Overview
User Request
↓
Orchestrator
├→ Agent 1 (Specialist) → Task 1
├→ Agent 2 (Specialist) → Task 2
├→ Agent 3 (Specialist) → Task 3
↓
Result Aggregator
↓
Final Response
Core Concepts
Agent Definition
An agent is defined by:
- Role: What responsibility does it have? (e.g., "Financial Analyst")
- Goal: What should it accomplish? (e.g., "Analyze financial risks")
- Expertise: What knowledge/tools does it have?
- Tools: What capabilities can it access?
- Context: What information does it need to work effectively?
What ships with it
5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 10d ago First seen · 580 lines · 55 tokens per session scan A 95de66280782
multi-agent-orchestration is a skill published in the GitHub repository javalenciacai/QASkills (5 stars, last pushed 7mo ago), licensed MIT. It adds 55 tokens to every session and 3,498 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.
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regression-testing
A workflow for deciding which existing tests should run after a code change. Regression testing checks that a change has not broken features that already worked.
exploratory-testing
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