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/team-perf-opt

Unified team skill for performance optimization. Coordinator orchestrates pipeline, workers are team-worker agents. Supports single/fan-out/independent parallel modes. Triggers on "team perf-opt".

From plugin
maestro-flow
57524 skills29 agents18 commands3 MCP

Claude Code

$ npx -y skills add catlog22/maestro-flow --skill team-perf-opt --agent claude-code

SKILL.md

team-perf-opt.SKILL.md
name: team-perf-opt
disable-model-invocation: true
description: Unified team skill for performance optimization. Coordinator orchestrates pipeline, workers are team-worker agents. Supports single/fan-out/independent parallel modes. Triggers on "team perf-opt".
allowed-tools: Agent, TaskCreate, TaskList, TaskGet, TaskUpdate, TeamCreate, TeamDelete, SendMessage, AskUserQuestion, Read, Write, Edit, Bash, Glob, Grep, mcp__maestro__team_msg
session-mode: run
contract:
  contract_version: 2.1
  consumes: []
  produces:
    - {path: outputs/branches, kind: performance-branches-collection, schema: performance-branches-collection/1.0, role: attachment, required: false}
    - {path: outputs/pipelines, kind: performance-pipelines-collection, schema: performance-pipelines-collection/1.0, role: attachment, required: false}
  gates: {entry: [], exit: []}

<required_reading> @~/.maestro/workflows/run-mode-lite.md </required_reading>

Team Performance Optimization

Profile application performance, identify bottlenecks, design optimization strategies, implement changes, benchmark improvements, and review code quality.

Architecture

Skill(skill="team-perf-opt", args="<task-description>")
                    |
         SKILL.md (this file) = Router
                    |
     +--------------+--------------+
     |                             |
  no --role flag              --role <name>
     |                             |
  Coordinator                  Worker
  roles/coordinator/role.md    roles/<name>/role.md
     |
     +-- analyze -> dispatch -> spawn workers -> STOP
                                    |
                    +-------+-------+-------+-------+-------+
                    v       v       v       v       v
                 [profiler] [strategist] [optimizer] [benchmarker] [reviewer]
                 (team-worker agents)

Pipeline (Single mode):
  PROFILE-001 -> STRATEGY-001 -> IMPL-001 -> BENCH-001 + REVIEW-001 (fix cycle)

Pipeline (Fan-out mode):
  PROFILE-001 -> STRATEGY-001 -> [IMPL-B01..N](parallel) -> BENCH+REVIEW per branch

Pipeline (Independent mode):
  [Pipeline A: PROFILE-A->STRATEGY-A->IMPL-A->BENCH-A+REVIEW-A]
  [Pipeline B: PROFILE-B->STRATEGY-B->IMPL-B->BENCH-B+REVIEW-B] (parallel)

Role Registry

| Role | Path | Prefix | Inner Loop | |------|------|--------|------------| | coordinator | [roles/coordinator/role.md](roles/coordinator/role.md) | — | — | | profiler | [roles/profiler/role.md](roles/profiler/role.md) | PROFILE-* | false | | strategist | [roles/strategist/role.md](roles/strategist/role.md) | STRATEGY-* | false | | optimizer | [roles/optimizer/role.md](roles/optimizer/role.md) | IMPL-*, FIX-* | true | | benchmarker | [roles/benchmarker/role.md](roles/benchmarker/role.md) | BENCH-* | false | | reviewer | [roles/reviewer/role.md](roles/reviewer/role.md) | REVIEW-*, QUALITY-* | false |

Pre-load (coordinator, before dispatch)

1. **Codebase docs**: If `.workflow/codebase/ARCHITECTURE.md` exists, read for module boundaries 2. **Specs (coding)**: `maestro load --type spec --category coding` — load coding constraints as shared context 3. **Wiki knowledge**: `maestro search "performance optimization profiling" --json` — top 5 entries as prior context 4. All optional — proceed without if unavailable

Role Router

Parse `$ARGUMENTS`:

  • Has `--role <name>` → Read `roles/<name>/role.md`, execute Phase 2-4
  • No `--role` → `@roles/coordinator/role.md`, execute entry router

Shared Constants

  • **Session prefix**: `PERF-OPT`
  • **Session path**: `{run_dir}/work/team/`
  • **Team name**: `perf-opt`
  • **CLI tools**: `maestro delegate --mode analysis` (read-only), `maestro delegate --mode write` (modifications)
  • **Message bus**: `mcp__maestro__team_msg(session_id=<run-id>, ...)`

Worker Spawn Template

Coordinator spawns workers using this template:

Agent({
  subagent_type: "team-worker",
  description: "Spawn <role> worker",
  team_name: "perf-opt",
  name: "<role>",
  run_in_background: true,
  prompt: `## Role Assignment
role: <role>
role_spec: <skill_root>/roles/<role>/role.md
session: {run_dir}/work/team
session_id: <run-id>
team_name: perf-opt
requirement: <task-description>
inner_loop: <true|false>

## Progress Milestones
session_id: <run-id>
Report progress via team_msg at natural phase boundaries (context loaded -> core work done -> verification).
Report blockers immediately via team_msg type="blocker".
Report completion via team_msg type="task_complete" after final SendMessage.

Read role_spec file (@<skill_root>/roles/<role>/role.md) to load Phase 2-4 domain instructions.
Execute built-in Phase 1 (task discovery) -> role Phase 2-4 -> built-in Phase 5 (report).`
})

**Inner Loop roles** (optimizer): Set `inner_loop` dynamically — `true` for single mode, `false` for fan-out/independent (parallel branches). **Single-task roles** (profiler, strategist, benchmarker, reviewer): Set `inner_loop: false`.

User Commands

| Command | Action | |---------|--------| | `check` / `status` | Output execution status graph (branch-grouped), no advancement | | `resume` / `continue` | Check worker states, advance next step | | `revise <TASK-ID> [feedback]` | Create revision task + cascade downstream (scoped to branch) | | `feedback <text>` | Analyze feedback impact, create targeted revision chain | | `recheck` | Re-run quality check | | `improve [dimension]` | Auto-improve weakest dimension |

Session Directory

{run_dir}/work/team/
+-- team-session.json                    # Session metadata + status + parallel_mode
+-- {run_dir}/outputs/              # Run deliverables (via maestro run)
|   +-- baseline-metrics.json       # Profiler: before-optimization metrics
|   +-- bottleneck-report.md        # Profiler: ranked bottleneck findings
|   +-- optimization-plan.md        # Strategist: prioritized optimization plan
|   +-- benchmark-results.json      # Benchmarker: after-optimization metrics
|   +-- review-report.md            # Reviewer: c
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@catlog22
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Intent-driven workflow orchestration for multi-agent AI development — adaptive lifecycle engine, self-reinforcing knowledge graph, and visual dashboard for Claude Code, Gemini, Codex & more

57524 skills29 agents3 MCP
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Questions about team-perf-opt.

Which coding agents can use team-perf-opt?
team-perf-opt installs in Claude Code, Codex, Cursor, Gemini CLI, GitHub Copilot, OpenCode and Windsurf with the skills CLI, using each agent's flag shown on this page. Flowy's auto-invocation, which fires a skill when your prompt matches, runs in Claude Code only.