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agent-orchestrator

agent-orchestrator

github.com/mrclrchtrAgent workflows838 installsUpdated 1d ago
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About agent-orchestrator

agent-orchestrator is a skill for coordinating multi-agent software work from planning through validation. It delegates implementation and reviews to specified agent roles, and prohibits direct changes by the orchestrator.

Key features

  1. 01Freeze scope and define success criteria
  2. 02Decompose work into independently auditable subsystems
  3. 03Run dual audits and reconcile findings
  4. 04Delegate implementation by issue cluster
  5. 05Require spec and quality review gates
  6. 06Integrate and validate the final work

Use cases

  1. 01Coordinating a multi-agent code audit and fix
  2. 02Separating independent work areas and ownership
  3. 03Checking changes against requirements and test quality
  4. 04Delivering validated work with a completion report

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/mrclrchtr/skills --skill agent-orchestrator

After installing, type/agent-orchestratorin Claude Code to confirm it loaded.

Bundled files

References and scripts installed along with SKILL.md.

README.md
.claude-plugin/
plugin.json
agents/
openai.yaml

Details

From the project README

Agent Orchestrator

Overview

Orchestrate multi-agent work end-to-end: delegate audits and fixes, reconcile results, enforce quality gates, and deliver a validated outcome.

Follow this core pattern: delegate a fresh implementer per cluster, then run a two-stage review (spec compliance first, then code quality).

Non-negotiable rule: never implement changes directly (no coding, no file edits).

Agent Roles

Assume these roles are available in your environment. Do not edit agent definitions or configs. If a required role is missing, stop and ask the operator to configure it.

  • architect (design/decisions/contracts)
  • auditor (read-only issue finding; no fixes)
  • explorer / scout (read-only repo lookup)
  • worker (general-purpose helper)
  • implementer (code + tests)
  • spec_reviewer (read-only, PASS/FAIL: nothing missing, nothing extra)
  • quality_reviewer (read-only, PASS/FAIL: maintainability + test quality)

Workflow

  1. Use skills when they directly match a subtask

    • If a skill matches the task, invoke it explicitly and follow it (e.g., $web-fetch-to-markdown <url>).
    • When delegating, tell sub-agents which skill to use in their prompt (e.g., “Use $commit for the commit step.”).
  2. Freeze scope + success criteria

    • Restate the mission, constraints, and “done” criteria in concrete terms.
    • Identify any authoritative sources (docs/specs) and record what claims must be backed by evidence.
  3. Create a phase plan and keep it current

    • Use your environment’s planning mechanism (e.g., update_plan if available) to track phases and prevent drifting.
    • Prefer 4–7 steps; keep exactly one step in progress.
  4. Decompose into subsystems

    • Choose subsystems that can be audited independently (API surface, core logic, error handling, perf, integrations, tests, docs).
    • For each subsystem, define 2–5 invariants (what must always be true).
  5. Run dual independent audits per subsystem

    • Spawn two independent auditor agents per subsystem (auditA and auditB).
    • For high-risk or subtle work, narrow the subsystem scope and strengthen the invariants and evidence requirements before spawning them.
    • Tell them to work independently until reconciliation (no cross-talk).
    • Require evidence for every issue (repo location, deterministic repro, expected vs actual, severity).
  6. Reconcile audits into a single confirmed issue list

    • Compare auditA vs auditB outputs and keep only mutually confirmed issues (or independently verify disputed ones with explorer).
    • Track rejected candidates with a brief reason (weak evidence, out of scope, non-deterministic).
    • Use this reconciled list as the only input to implementation.
    • Reconciliation output:
      • Confirmed issues (only mutual)
      • Rejected candidates (reason)
      • Consensus achieved: YES/NO
  7. Implement in clusters with clear ownership

    • Group confirmed issues into clusters that can be fixed with minimal coupling.
    • Spawn exactly one implementer per cluster.
    • Assign each implementer a file set to “own” and require them to avoid broad refactors.
    • Do not implement any cluster work directly; always delegate to the implementer (even for “quick” changes).
    • Every fix must come with a regression test (unit/integration/e2e as appropriate).
    • For each cluster, run a two-stage review loop:
      • Have the implementer complete the cluster (tests, self-review) and report what changed.
      • spec_reviewer validates “nothing more, nothing less” by reading code (do not trust the report).
      • quality_reviewer validates maintainability and test quality (only after spec compliance passes).
      • If any review FAILs, send concrete feedback to the implementer and repeat the failed review stage.
  8. Enforce review gates

    • Do not merge/land a cluster unless spec compliance PASS and code quality PASS are both recorded with concrete references.
  9. Integrate + validate

    • Run the repo’s standard validations (tests, lint, build, typecheck).
    • If the repo has no clear commands, discover them from README, package.json, pyproject.toml, CI config, etc.
  10. Deliver a concise completion report

    • State what is usable now.
    • State what remains intentionally unsupported (with next steps/issues).
    • List commands executed (at least key validation commands) and results.

What to send to sub-agents

Keep your messages task-specific and concise. Do not restate generic role behavior; focus on the task at hand.

For any audit/review/implementation message, include:

  • Goal + success criteria (what “done” means)
  • Scope boundaries / owned files (what to touch, what not to touch)
  • Invariants (2–5) that must hold
  • Commands to run (if known), and what evidence to collect

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