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agent-orchestrator

AI Community
Fluxly profile Facts only: domain, agents, trust score, runtime, permissions and network
Domain
AI
Compatible agents
  • Claude Code
  • Cursor
  • Cline
  • Codex
  • Windsurf
  • Gemini CLI
  • +20
Trust score
88 / 100 · community maintained
Author / version / license
@ComposioHQ · no license declared
Token usage
Moderate
Setup complexity
Guided setup
External API key
Required · Anthropic
Operating systems
macOS · Linux · Windows
Runtime requirements
No special requirements
Permissions
  • Read-only
  • Write / modify
  • Shell exec
  • Env read
Network behavior
External requests
Install commands
26 variants

Profile is derived at build time from SKILL.md and install vectors. Subject to drift from author intent.

Heads up: 未限定 allowed-tools，默认拥有全部工具权限。

Output preview agent-orchestrator.preview 
---
name: agent-orchestrator
description: Open-source, pluggable agentic coding orchestrator. Manages durable coding agents (Claude Code…
category: ai
runtime: no special runtime
---

# agent-orchestrator output preview

## PART A: Task fit
- Use case: Open-source, pluggable agentic coding orchestrator. Manages durable coding agents (Claude Code, Codex, OpenCode) through a simple interface — spawn agents, track progress, and let feedback loops like PR reviews and CI failures automatically route to the right agents. Use for fixing bugs, building features, working on GitHub issues, checking status, and managing agent sessions..
- Inputs: target material, constraints, expected output, and acceptance criteria.
- Evidence boundary: follow “Why AO instead of manual coding / How You Think / Intent → Tool Mapping” and do not present inference as author intent.

## PART B: Execution result
- **01** The card summarizes the use case; runtime output centers on “Open-source, pluggable agentic coding orchestrator. Manages durable coding agents (Claude Code, Codex, OpenCode) through a simple interface — spawn agents, track progress, and let feedback loops like PR reviews and CI failures automatically route to the right agents. Use for fixing bugs, building features, working on GitHub issues, checking status, and managing agent sessions.”.
- **02** When the source has headings, the agent prioritizes “Why AO instead of manual coding / How You Think / Intent → Tool Mapping” so the result follows the author’s structure.
- **03** Typical output includes task judgment, concrete steps, required commands or file edits, validation, and follow-up options.
- **04** Risk context follows the fingerprint: read files, write/modify files, run shell commands, read environment variables; may access external network resources; requires Anthropic API keys.

## Running Rules
- read files, write/modify files, run shell commands, read environment variables; may access external network resources; requires Anthropic API keys.
- Validate with a small sample before expanding scope.
- Return the result, validation criteria, and next iteration options.
Interpretation is structured for decision-making; original keeps the upstream SKILL.md unchanged.

Decide Fit First

  • Core job: Open-source, pluggable agentic coding orchestrator. Manages durable coding agents (Claude Code, Codex, OpenCode) through a simpl…
  • Best fit: Use it when the task has reusable inputs, steps, and validation criteria rather than a one-off answer.
  • Avoid forcing it: If the source lacks commands, platform support, or external-service evidence, keep those fields unknown instead of guessing.

Design Intent

  • Structure: The skill is organized around “Why AO instead of manual coding”, “How You Think”, “Intent → Tool Mapping”, “Status / progress”, showing how the author expects the agent to judge fit, collect context, and produce verifiable output.
  • Trigger evidence: Prioritize the author’s wording around when to use it, what context to collect, and what output shape to produce.
  • Evidence boundary: Author text states facts, repository files prove commands and paths, and Fluxly only adds fit, limits, and usage judgment.

How To Use It

  • Inputs: Provide target material, scope, expected result, forbidden changes, and validation method.
  • Invocation: Name agent-orchestrator directly; if the source includes slash commands, start with the command and then add task context.
  • Validation: Start small and check whether the result follows “Why AO instead of manual coding / How You Think / Intent → Tool Mapping” before expanding.

Boundaries And Review

  • Dependencies: Prepare Anthropic API keys before running a full task.
  • Permissions: Declared permissions include read / write / shell-exec / env-read; ask the agent to state file, command, and rollback boundaries before acting.
  • Quality bar: A useful result names the deliverable, evidence, and next action. Generic prose means the task needs tighter context.

Discussion

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