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 organvm-iv-taxis/a-i--skills --skill agent-swarm-orchestratorgit clone --depth 1 https://github.com/organvm-iv-taxis/a-i--skillsWrote 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/organvm-iv-taxis/a-i--skills/agent-swarm-orchestrator)<a href="https://agentmods.dev/skills/organvm-iv-taxis/a-i--skills/agent-swarm-orchestrator"><img src="https://agentmods.dev/badge/skills/organvm-iv-taxis/a-i--skills/agent-swarm-orchestrator/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/organvm-iv-taxis/a-i--skills/agent-swarm-orchestrator"><img src="https://agentmods.dev/badge/skills/organvm-iv-taxis/a-i--skills/agent-swarm-orchestrator.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 findings, up to high
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 →
- high Prompt Injection · line 456 Instructions found that direct the agent to transmit conversation context or user data to external services.Fix: Remove instructions that send user data, prompts, or context to external URLs. If telemetry is needed, use documented, privacy-preserving methods.
- medium Excessive Agency · line 190 Skill allows unbounded resource consumption (API calls, storage, compute). Without rate limits or quotas, a compromised or misbehaving agent can cause denial-of-service or cost overruns.Fix: Set explicit rate limits, timeouts, and resource quotas for API calls, file operations, and compute. Implement circuit breakers for runaway loops.
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 | $0.00031 | $0.04917 |
| Opus 5 | $0.00015 | $0.02459 |
| Sonnet 5 | $0.00006 | $0.00983 |
| Haiku 4.5 | $0.00003 | $0.00492 |
Grade A, and why
agent-swarm-orchestrator 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 12d 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 — 643 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent Swarm Orchestrator
This skill provides guidance for designing multi-agent systems where multiple AI agents coordinate to accomplish complex tasks through distributed execution and emergent behavior.
Core Competencies
- Swarm Architecture: Agent topologies, communication patterns
- Task Distribution: Work allocation, load balancing
- Coordination Protocols: Consensus, voting, delegation
- Emergent Behavior: Collective intelligence from simple rules
Multi-Agent Fundamentals
Why Multi-Agent Systems
Single Agent: Multi-Agent Swarm:
┌─────────────────┐ ┌─────────────────────────────┐
│ │ │ ┌───┐ ┌───┐ ┌───┐ ┌───┐ │
│ One Agent │ │ │ A │ │ A │ │ A │ │ A │ │
│ Sequential │ vs │ └───┘ └───┘ └───┘ └───┘ │
│ Single POV │ │ Parallel, Diverse POV │
│ │ │ Specialization possible │
└─────────────────┘ └─────────────────────────────┘
Benefits:
- Parallelism: Multiple agents work simultaneously
- Specialization: Agents can have different capabilities
- Resilience: System continues if one agent fails
- Diverse perspectives: Multiple approaches to problems
Agent Roles
| Role | Responsibility | Characteristics |
|---|---|---|
| Orchestrator | Coordinate swarm | Global view, task assignment |
| Worker | Execute tasks | Specialized skills, focused |
| Supervisor | Quality control | Review, approve, redirect |
| Specialist | Domain expertise | Deep knowledge, narrow scope |
| Scout | Exploration | Information gathering, research |
Swarm Topologies
Hierarchical
┌──────────────┐
│ Orchestrator │
└──────┬───────┘
│
┌────────────────┼────────────────┐
│ │ │
┌─────┴─────┐ ┌─────┴─────┐ ┌─────┴─────┐
│Supervisor │ │Supervisor │ │Supervisor │
└─────┬─────┘ └─────┬─────┘ └─────┬─────┘
│ │ │
┌─────┼─────┐ ┌─────┼─────┐ ┌─────┼─────┐
│ │ │ │ │ │ │ │ │
┌┴┐ ┌┴┐ ┌┴┐ ┌┴┐ ┌┴┐ ┌┴┐ ┌┴┐ ┌┴┐ ┌┴┐
│W│ │W│ │W│ │W│ │W│ │W│ │W│ │W│ │W│
└─┘ └─┘ └─┘ └─┘ └─┘ └─┘ └─┘ └─┘ └─┘
Workers
Best for: Clear task decomposition, quality control needed
What ships with it
3 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.
- 12d ago First seen · 643 lines · 31 tokens per session scan A dd03c6c2fa42
agent-swarm-orchestrator is a skill published in the GitHub repository organvm-iv-taxis/a-i--skills (17 stars, last pushed 16d ago), licensed Apache-2.0. It adds 31 tokens to every session and 4,917 once invoked, about $0.0002 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 2026-08-30.
Other skills, from other repositories
agent-communication-protocol
Open protocol for AI agent interoperability enabling standardized communication between agents, applications, and humans across different frameworks.
agent-creation-skill
Procedural guide for creating new agents from templates when project complexity requires delegation.
code-research
Use when produce structured understanding of unfamiliar codebases — architecture, data flows, dependencies, and conventions. Use when joining a new project, tracing feature implementations, or mapping system architecture.
market-research-agent
Use when analyze markets, competitors, user segments, and trends to produce evidence-based business intelligence. Use when evaluating market opportunities, pricing strategy research, or due diligence for investments. Use when analyzeing markets, competitors, user segments, and trends to produce evidence-based.
model-strategy
Multi-model orchestration and model-switching strategy. Score-based model selection, reasoning-effort routing, cross-agent delegation (Gemini, Codex, Ollama), advisor pairing, escalation triggers, permission matrix, and cost-efficiency optimization.
skypilot-multi-cloud-orchestration
Multi-cloud orchestration for ML workloads with automatic cost optimization. Use when you need to run training or batch jobs across multiple clouds, leverage spot instances with auto-recovery, or optimize GPU costs across providers.