#601

Globales Ranking · von 601 Skills

conductor AI Agent Skill

Quellcode ansehen: reinamaccredy/maestro

Installation

npx skills add reinamaccredy/maestro --skill conductor

10

Installationen

maestro

Harness for long-running AI coding agents -- structured memory, cross-feature learning, and plan-approve-execute workflow.

A CLI-first tool that gives AI coding agents persistent state, workflow guardrails, and a plan-first pipeline. Agents interact via maestro <command> --json through Bash. All durable state lives under .maestro/.

Quick Start

maestro init                                    # initialize project
maestro feature-create my-feature               # create feature
maestro memory-write --name findings \
  --file research.md                            # save research
maestro plan-write --file plan.md               # write plan (auto-detects active feature)
maestro plan-approve                            # approve plan
maestro task-sync                               # generate tasks from plan
maestro task-next --json                        # find next runnable task
maestro task-claim --task 01-example \
  --agent-id worker-1                           # claim task for an agent
maestro task-brief --task 01-example --json     # get compiled worker context
maestro task-done --task 01-example \
  --file summary.md                             # mark task complete
maestro feature-complete                        # close the feature

All commands auto-detect the active feature -- no need to pass --feature after feature-create.

Prerequisites

  • bun (runtime and package manager)
  • git (repo state and audit capture)

Optional integrations

The following tools power optional adapters. maestro degrades gracefully when any are absent.

Tool Adapter Purpose
br (beads_rust) sync backend Task tracking and bead persistence
bv (beads viewer) bv-graph Dependency graph insights, next-task recommendations, execution plans
cass (Coding Agent Session Search) cass-search Full-text search across coding agent sessions (Claude Code, Codex, Cursor, Gemini)
agent-mail (MCP Agent Mail) agent-mail-handoff Cross-agent handoff notifications via HTTP API

Building

bun install
bun run build

Produces:

Output Purpose
dist/maestro Standalone binary
dist/cli.js npm CLI entry
hooks/*.mjs 5 Claude Code hook scripts
dist/server.bundle.mjs Plugin server (empty, for hook registration)

Development mode: bun src/surfaces/cli/index.ts <command>.

Architecture

maestro is a CLI-first harness -- the AI agent is the orchestrator, maestro is the filing cabinet with opinions. Agents call maestro <command> --json via Bash. Hooks inject context automatically.

surfaces/  -->  app/     -->  domain/ports/  <--  infra/adapters/
(CLI,           (rules,       (interfaces)       (implementations)
 hooks)          DCP, skills)
src/
  domain/           # Pure domain: ports (interfaces), types, errors
    ports/          # 11 port interfaces (task, feature, plan, memory, doctrine,
                    #   verification, graph, handoff, search, settings, host)
  app/              # Application layer: use cases, DCP, skills, workflow engine
    dcp/            # Dynamic Context Pruning (budget-based memory scoring)
    doctrine/       # Cross-feature learning compiler
    skills/         # Skill loader and registry generator
    tasks/          # Task state machine, graph, verification, spec builder
    workflow/       # Pipeline stages, playbook, execution insights
    ...             # Plus domain orchestration (features, plans, memory, etc.)
  infra/            # Infrastructure: adapters, settings, utilities
    adapters/       # Port implementations (fs-based + toolbox-provided)
    toolbox/        # Plugin registry and loader (br, bv, cass, agent-mail)
    utils/          # Filesystem, git, paths, validation, output
    visual/         # HTML/CSS renderer for visualization
  surfaces/         # External interfaces
    cli/            # 89 CLI commands (citty framework)
    mcp/            # Empty server shell (hooks require plugin recognition)
    hooks/          # 5 Claude Code hooks
  container.ts      # Immutable DI container -- wires ports to adapters
  services.ts       # Service locator (thin shim over container)
skills/             # Bundled SKILL.md workflow guides
hooks/              # Installable Claude Code hooks (build output)

The container (createContainer()) returns an Object.freeze()-d service object. Domain ports have zero dependencies on infrastructure. Optional ports (graph, search, handoff) resolve via toolbox at startup.

Pipeline

discovery --> research --> planning --> approval --> execution --> done

Stages are skippable. Hooks inject pipeline context automatically.

Task Model

6 states: pending --> claimed --> done | blocked | review --> revision

Stale claims expire after a configurable timeout (default 120 min) and auto-reset to pending on task-next.

CLI Commands (89)

All commands accept --json for structured output. All feature-scoped commands auto-detect the active feature. Use maestro <command> --help for full usage.

Content-accepting commands support three input modes: --content "..." (inline), --file path (from file), or --stdin (piped).

Domain Commands Count
Feature create, list, info, active, complete 5
Plan write, read, approve, revoke, comment, comments-clear 6
Task sync, list, next, info, claim, done, block, unblock, accept, reject, brief, spec-read, spec-write, report-read, report-write 15
Memory write, read, list, delete, compile, consolidate, archive, stats, promote, compress, connect, insights 12
Doctrine list, read, write, deprecate, suggest, approve 6
Graph insights, next, plan, discovery, reserve 5
Handoff send, receive, ack, read, list, status 6
Search sessions, related, similar 3
Skill load, list, install, create, remove, sync 6
Stage jump, skip, back 3
Config get, set, agent 3
Toolbox add, create, install, list, remove, test 6
Visual visual, debug-visual 2
Other init, install, ping, status, agents-md, dcp-preview, doctor, history, execution-insights, self-update, update 11

Hooks

Installable Claude Code hooks that integrate maestro into the agent lifecycle:

Hook File Trigger Purpose
SessionStart sessionstart.mjs Session begins Inject pipeline state and recommended skills
PreToolUse:Bash pretooluse.mjs Before Bash tool Remind agent to finish maestro tasks on git commit
PreToolUse:Agent pre-agent.mjs Before agent spawn Inject task spec + DCP-scored memories + doctrine
PostToolUse posttooluse.mjs After tool execution Track tool usage and state changes
PreCompact precompact.mjs Before context compaction Preserve critical maestro state

Install with maestro install.

Skills

21 bundled workflow skills, all using maestro: colon-prefixed naming:

  • maestro:design -- deep discovery and specification (16-step process with reference files)
  • maestro:implement -- task execution with TDD, parallel, and team modes
  • maestro:review -- track-aware code review with automated checks
  • maestro:brainstorming -- creative exploration before implementation
  • maestro:parallel-exploration -- parallel read-only exploration
  • maestro:dispatching -- parallel agent dispatch
  • maestro:debugging -- systematic debugging methodology
  • maestro:tdd -- test-driven development guidance
  • maestro:verification -- verification before completion
  • maestro:agents-md -- AGENTS.md quality discipline and generation
  • maestro:docker -- Docker container workflows
  • maestro:prompt-leverage -- prompt engineering for AI agents
  • maestro:new-feature -- create feature/bug tracks with spec and plan
  • maestro:note -- capture decisions and context to persistent notepad
  • maestro:plan-review-loop -- iterative adversarial plan review
  • maestro:revert -- git-aware undo of track implementation
  • maestro:next-move -- strategic analysis for highest-leverage next step
  • maestro:simplify -- review changed code for reuse, quality, and efficiency
  • maestro:visual -- generate visual explanations of systems and data

Load with maestro skill <name>, list with maestro skill-list.

Skills with reference/ subdirectories support progressive disclosure:

maestro skill maestro:design --ref steps/step-01-init.md

Old skill names (e.g., writing-plans) are aliased with deprecation warnings.

Data Layout

.maestro/
  config.json                           # Project configuration
  doctrine/                             # Doctrine items (cross-feature operating rules)
    <name>.json                         # Structured rule with effectiveness metrics
  memory/                               # Global project-scoped memory files
  features/
    <feature>/
      feature.json                      # Feature metadata and state
      plan.md                           # Implementation plan
      comments.json                     # Plan review comments
      memory/                           # Feature-scoped memory files
      tasks/
        <task>/
          task.json                     # Task state, claims, summaries
          spec.md                       # Compiled task specification
          report.md                     # Task completion report
          doctrine-trace.json           # Doctrine injection trace

License

MIT

Installationen

Installationen 10
Globales Ranking #601 von 601

Sicherheitsprüfung

Keine Sicherheitsdaten für diesen Skill verfügbar.

EU EU-Hosted Inference API

Power your AI Agents with the best open-source models.

Drop-in OpenAI-compatible API. No data leaves Europe.

Explore Inference API

GLM

GLM 5

$1.00 / $3.20

per M tokens

Kimi

Kimi K2.5

$0.60 / $2.80

per M tokens

MiniMax

MiniMax M2.5

$0.30 / $1.20

per M tokens

Qwen

Qwen3.5 122B

$0.40 / $3.00

per M tokens

So verwenden Sie diesen Skill

1

Install conductor by running npx skills add reinamaccredy/maestro --skill conductor in your project directory. Führen Sie den obigen Installationsbefehl in Ihrem Projektverzeichnis aus. Die Skill-Datei wird von GitHub heruntergeladen und in Ihrem Projekt platziert.

2

Keine Konfiguration erforderlich. Ihr KI-Agent (Claude Code, Cursor, Windsurf usw.) erkennt installierte Skills automatisch und nutzt sie als Kontext bei der Code-Generierung.

3

Der Skill verbessert das Verständnis Ihres Agenten für conductor, und hilft ihm, etablierte Muster zu befolgen, häufige Fehler zu vermeiden und produktionsreifen Code zu erzeugen.

Was Sie erhalten

Skills sind Klartext-Anweisungsdateien — kein ausführbarer Code. Sie kodieren Expertenwissen über Frameworks, Sprachen oder Tools, das Ihr KI-Agent liest, um seine Ausgabe zu verbessern. Das bedeutet null Laufzeit-Overhead, keine Abhängigkeitskonflikte und volle Transparenz: Sie können jede Anweisung vor der Installation lesen und prüfen.

Kompatibilität

Dieser Skill funktioniert mit jedem KI-Coding-Agenten, der das skills.sh-Format unterstützt, einschließlich Claude Code (Anthropic), Cursor, Windsurf, Cline, Aider und anderen Tools, die projektbezogene Kontextdateien lesen. Skills sind auf Transportebene framework-agnostisch — der Inhalt bestimmt, für welche Sprache oder welches Framework er gilt.

Data sourced from the skills.sh registry and GitHub. Install counts and security audits are updated regularly.

EU Made in Europe

Chat with 100+ AI Models in one App.

Use Claude, ChatGPT, Gemini alongside with EU-Hosted Models like Deepseek, GLM-5, Kimi K2.5 and many more.

Start for free View pricing