Agent Orchestration

What it does

When your agent uses it

• Building autonomous agent loops
• Coordinating multiple agents
• Evaluating CrewAI/AutoGen/Swarm
• Orchestrating complex multi-step scenarios

Example prompts

• “/agent-orchestration”

Requirements

• Python 3
• Node.js
• Compatibility (from SKILL.md): Claude Code 2.1.277+.
• Pre-approved tools (allowed-tools): Read, Glob, Grep, WebFetch, WebSearch

What it can do on your machine

Read from SKILL.md and the folder at commit 02bbf9a. It shows what the files ask for, not the result of running them.

• Tool permissions

  Pre-approves these tools, so the agent can use them without asking each time:

  • Read
  • Glob
  • Grep
  • WebFetch
  • WebSearch

  From allowed-tools in the SKILL.md frontmatter.

• Runs code

  Ships 3 files in scripts/ (Python and TypeScript), which the agent can run.

  Shell commands in SKILL.md call:

  • claude

  From the folder's file list and the shell code blocks in SKILL.md.

• Network

  Links to these hosts (documentation or services it may open):

  • platform.openai.com
  • docs.python.org
  • anthropic.com
  • docs.crewai.com
  • openai.github.io
  • learn.microsoft.com

  From URLs in SKILL.md, links to its own repository left out.

• Credentials

  Names no API keys, tokens, secrets or passwords.

  From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

• Compatibility

  Claude Code 2.1.277+.

  From compatibility in the SKILL.md frontmatter.

Context cost

Agent Orchestration loads about 2.8k tokens when it runs, and up to ~14k if it reads all its reference files. Until then it costs about 75 tokens; SKILL.md has 1,001 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it

~75

When it runs · the whole SKILL.md, loaded when a task matches

~2.8k

With references · SKILL.md plus every file in references/, read only if the agent opens them

~14k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from yonatangross/orchestkit at commit 02bbf9a, republished under its MIT licence (© yonatangross). 1,001 words, ~2,758 tokens.

name

agent-orchestration

description

Agent orchestration patterns for agentic loops, multi-agent coordination, alternative frameworks, and multi-scenario workflows. Use when building autonomous agent loops, coordinating multiple agents, evaluating CrewAI/AutoGen/Swarm, or orchestrating complex multi-step scenarios.

allowed-tools

Read, Glob, Grep, WebFetch, WebSearch

compatibility

Claude Code 2.1.277+.

license

MIT

user-invocable

false

disable-model-invocation

true

effort

high

metadata.owner-agent

workflow-architect

metadata.category

workflow-automation

metadata.version

2.0.0

metadata.author

OrchestKit

metadata.complexity

high

metadata.tags

agents, orchestration, multi-agent, agent-loops, crewai, autogen, swarm, coordination

# ReAct agent loop
async def react_loop(question: str, tools: dict, max_steps: int = 10) -> str:
    history = REACT_PROMPT.format(tools=list(tools.keys()), question=question)
    for step in range(max_steps):
        response = await llm.chat([{"role": "user", "content": history}])
        if "Final Answer:" in response.content:
            return response.content.split("Final Answer:")[-1].strip()
        if "Action:" in response.content:
            action = parse_action(response.content)
            result = await tools[action.name](*action.args)
            history += f"\nObservation: {result}\n"
    return "Max steps reached without answer"

# Supervisor with fan-out/fan-in
async def multi_agent_analysis(content: str) -> dict:
    agents = [("security", security_agent), ("perf", perf_agent)]
    tasks = [agent(content) for _, agent in agents]
    results = await asyncio.gather(*tasks, return_exceptions=True)
    return await synthesize_findings(results)

© yonatangross, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 16 other files (scripts, references) in src/skills/agent-orchestration of yonatangross/orchestkit.

• SKILL.md
• checklists/framework-selection.md
• metadata.json
• references/claude-code-instance-management.md
• references/framework-comparison.md
• references/langgraph-implementation.md
• references/ork-delta.md
• rules/_sections.md
• rules/_template.md
• rules/multi-debate.md
• rules/multi-supervisor.md
• rules/scenario-orchestrator.md
• rules/scenario-routing.md
• scripts/agent-workflow-template.ts
• scripts/crewai-crew.py
• scripts/openai-multi-agent.py
• test-cases.json

Open the folder on GitHubat commit 02bbf9a