Skip to content
Get packs

Multi-Agent Optimization Toolkit

Guides profiling and tuning of multi-agent systems for speed, cost, and reliability

Source Download skill
Repository
sickn33/agentic-awesome-skills
Stars
49,023
Licence
MIT
Files
1 file

What it does

This skill gives an AI agent a structured playbook for improving systems that run several AI agents working together. Once installed, the agent follows steps like setting baseline metrics, profiling bottlenecks, changing orchestration incrementally, and validating results with repeatable tests and rollbacks. It also gives guidance on cost-aware model choice and on compressing context, but it is a design guide rather than a tool that changes anything by itself.

Great for

  • Engineers running multi-agent AI pipelines who want a disciplined way to find bottlenecks
  • Teams trying to cut LLM token or model costs across several agents without losing quality
  • Architects planning parallel agent workflows who need concurrency and failure-isolation guidance

Skip it if

  • Tuning a single agent's prompt, which the skill itself says it should not be used for
  • Projects with no metrics or evaluation data, since the guidance depends on measured baselines (guessing would make the advice unreliable)
  • Anyone expecting it to spawn agents, deploy changes, or run profiling automatically; the skill explicitly does not authorize these actions

Try asking

  • “Profile our multi-agent customer support pipeline and find where latency is coming from”
  • “Suggest a cost-aware model selection plan for our five-agent research workflow given a monthly budget”
  • “Design a rollout and rollback plan for changing how our orchestrator distributes work between agents”
  • “Review our agent coordination code for concurrency limits, cancellation handling, and failure isolation”
Step-by-step guide: how to use Multi-Agent Optimization ToolkitInstall, first run, examples, tips and FAQs

Install in 3 steps

  1. 1

    Download the skill

    The ZIP contains the 1 original files, the MIT licence and an attribution note.

    agent-orchestration-multi-agent-optimize.zip
  2. 2

    Add it to your agent

    Claude Code (all projects)

    mkdir -p ~/.claude/skills && unzip agent-orchestration-multi-agent-optimize.zip -d ~/.claude/skills/

    Claude Code / Agent SDK (this project only)

    mkdir -p .claude/skills && unzip agent-orchestration-multi-agent-optimize.zip -d .claude/skills/

    Claude apps (web/desktop): Settings → Capabilities → Skills → Upload skill, and choose the ZIP.

  3. 3

    Ask for the task

    No special command needed — the agent reads the skill's description and loads it when your request matches. Try one of the prompts above.

Get better results

  • Provide real metrics (latency, token counts, error rates) and a target goal; the skill relies on measured baselines rather than assumptions
  • Treat the Python snippets as illustrative sketches only; the named agent classes and tracker helpers must be implemented in your own application
  • Ask for changes to be rolled out gradually with regression tests, since the skill emphasises incremental changes and validation

What's inside

  • SKILL.md

Redistributed unmodified from sickn33/agentic-awesome-skills under the MIT licence. The assessment was drafted with AI from the skill's files on 2026-10-29. Skills can instruct your agent to run code — read SKILL.md and any scripts before installing.