AAS Core is a local control plane for coding agents that lets them search a large catalogue of skills, choose a stack, validate it, and create a reproducible plan. It is used to assemble and review agent workflows through its CLI, local MCP server, catalogue, plugins, and Workbench. The catalogue add-ons provide the skills, plugins, bundles, and workflows that AAS Core helps agents select and validate.
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 sickn33/agentic-awesome-skills --skill agent-orchestration-multi-agent-optimizegit clone --depth 1 https://github.com/sickn33/agentic-awesome-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/sickn33/agentic-awesome-skills/agent-orchestration-multi-agent-optimize)<a href="https://agentmods.dev/skills/sickn33/agentic-awesome-skills/agent-orchestration-multi-agent-optimize"><img src="https://agentmods.dev/badge/skills/sickn33/agentic-awesome-skills/agent-orchestration-multi-agent-optimize/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/sickn33/agentic-awesome-skills/agent-orchestration-multi-agent-optimize"><img src="https://agentmods.dev/badge/skills/sickn33/agentic-awesome-skills/agent-orchestration-multi-agent-optimize.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- NVIDIA SkillSpector pass
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 | $0.00038 | $0.01447 |
| Opus 5.5 | $0.00015 | $0.00579 |
| Sonnet 5.5 | $0.00008 | $0.00289 |
| Haiku 4.5 | $0.00004 | $0.00145 |
Grade A, and why
agent-orchestration-multi-agent-optimize 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 11d 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
8 near-identical copies found in the catalogue:
- agent-orchestration-multi-agent-optimize — 100% identical, 36 lines differ
- agent-orchestration-multi-agent-optimize — 98% identical, 38 lines differ
- agent-orchestration-multi-agent-optimize — 98% identical, 38 lines differ
- agent-orchestration-multi-agent-optimize — 98% identical, 38 lines differ
- agent-orchestration-multi-agent-optimize — 98% identical, 38 lines differ
- agent-orchestration-multi-agent-optimize — 98% identical, 38 lines differ
- agent-orchestration-multi-agent-optimize — 98% identical, 38 lines differ
- agent-orchestration-multi-agent-optimize — 98% identical, 36 lines differ
How it starts
The opening of the file, as written. The whole thing — 238 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Multi-Agent Optimization Toolkit
Use this skill when
- Improving multi-agent coordination, throughput, or latency
- Profiling agent workflows to identify bottlenecks
- Designing orchestration strategies for complex workflows
- Optimizing cost, context usage, or tool efficiency
Do not use this skill when
- You only need to tune a single agent prompt
- There are no measurable metrics or evaluation data
- The task is unrelated to multi-agent orchestration
Instructions
- Establish baseline metrics and target performance goals.
- Profile agent workloads and identify coordination bottlenecks.
- Apply orchestration changes and cost controls incrementally.
- Validate improvements with repeatable tests and rollbacks.
Execution boundaries
This guide does not authorize spawning agents, remote execution, deployment or external messages. Where parallel work is authorized, assign disjoint write ownership, cap concurrency and deadlines, isolate failures, inspect every result and verify the aggregate. A running future cannot generally be stopped by cancelling its handle; design cooperative cancellation and record incomplete work. Context compression must retain constraints, evidence provenance and unresolved tasks; validate losses against a full-context baseline.
Safety
- Avoid deploying orchestration changes without regression testing.
- Roll out changes gradually to prevent system-wide regressions.
Role: AI-Powered Multi-Agent Performance Engineering Specialist
Context
This is an orchestration design guide, not a bundled framework. Profile the existing system and compare scoped changes against a measured baseline. Named agent classes and tracker helpers in design examples must be implemented in the application.
Core Capabilities
- Intelligent multi-agent coordination
- Performance profiling and bottleneck identification
- Adaptive optimization strategies
- Cross-domain performance optimization
- Cost and efficiency tracking
Arguments Handling
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.
- 11d ago Changed · -10 lines cf9ecd271888
- 1mo ago First seen · 248 lines · 38 tokens per session scan A f63acb56ac0b
agent-orchestration-multi-agent-optimize is a skill published in the GitHub repository sickn33/agentic-awesome-skills (48,503 stars, last pushed yesterday), licensed MIT. It adds 38 tokens to every session and 1,447 once invoked, about $0.0002 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 2026-08-30.
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