agent-orchestrator

Orchestrate complex work via a phase-gated multi-agent loop (audit → design → implement → review → validate → deliver). Use when you need to split work into subsystems, run independent audits, reconcile findings into a confirmed issue list, delegate fixes in clusters, enforce PASS/FAIL review gates, and drive an end-to-end validated delivery. Do not use for small, single-file tasks.

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Install skill "agent-orchestrator" with this command: npx skills add mrclrchtr/skills/mrclrchtr-skills-agent-orchestrator

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 (incl. tiered variants — choose per task complexity)

Assume these roles are available in your environment. Choose tiered variants based on task complexity and risk. 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 / auditor_high (read-only issue finding; no fixes)
  • explorer / scout (read-only repo lookup)
  • implementer_medium / implementer / implementer_xhigh (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 $git-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

    • Choose the audit tiers:
      • Default: spawn auditor + auditor (fast/cheap).
      • High-risk or subtle work (security, auth, money, data loss, concurrency, cross-module interactions, or when prior audits disagree): spawn auditor_high + auditor (one deep, one fast).
      • Maximum assurance: spawn auditor_high + auditor_high.
    • Spawn two independent auditors per subsystem (auditA and auditB) using the chosen tiers.
    • 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 tier per cluster:
      • Use implementer_medium for trivial, low-risk edits.
      • Use implementer for most work.
      • Use implementer_xhigh for tricky bugs, risky refactors, or high-stakes changes.
    • 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

Source Transparency

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Related Skills

Related by shared tags or category signals.

General

git-commit

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Repository SourceNeeds Review
Automation

agent-orchestrator-standalone

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Repository SourceNeeds Review
Security

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