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maestro

Classify user intents and orchestrate Session/Run chains with verified lifecycle transitions.

76|5|Updated Jul 7, 2026
Search Tags:#automation#workflow#planning#orchestration#session#ai-agent#run
One-click install
npx skills add https://github.com/catlog22/pi-maestro-flow --skill maestro-catlog22
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: maestro
Source: https://github.com/catlog22/pi-maestro-flow/tree/main/.pi/skills/maestro
Command: npx skills add https://github.com/catlog22/pi-maestro-flow --skill maestro-catlog22

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Maestro turns a user intent into a canonical Session/Run workflow, so an agent can classify the task, create the right chain, and execute it through a controlled lifecycle instead of improvising ad hoc steps.

Core Features & Use Cases

  • Intent classification: Distinguishes narrow edits, broad rewrites, continuations, and amendments before execution.
  • Session/Run orchestration: Creates and advances the shared canonical session, then drives the run loop with read-only status checks and verified transitions.
  • Controlled execution: Supports confirmation gates, dry runs, amendments, and safe continuation of existing sessions.
  • Use case: A developer wants a code change handled through a structured plan, approval step, and execution loop with auditable progress and rollback-friendly coordination.

Quick Start

Use the maestro skill to classify this intent and start the appropriate session chain for the requested change.

Frequently Asked Questions about maestro

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I orchestrate complex AI agent workflows with structured session management?▼

Workflow orchestration for AI agents uses structured session management to convert user intents into canonical run chains. This approach classifies tasks, creates controlled execution loops, and applies verified lifecycle transitions with safe fallbacks instead of ad hoc steps.

What is the best way to manage long-running coding sessions and audit goal amendments?▼

Managing long-running coding sessions requires a controlled orchestration loop that supports audited goal amendments. The system creates a canonical session before execution, enabling safe continuation, progress tracking, and rollback-friendly coordination for ongoing workflows.

How do I add confirmation gates and dry runs to an automated code change workflow?▼

Adding confirmation gates and dry runs to an automated code change workflow requires controlled execution orchestration. The process classifies the edit intent, creates a structured run chain, and applies verified lifecycle transitions before advancing any execution steps.

When do I need to classify a task intent before starting an AI agent run chain?▼

Task intent classification is needed before starting an AI agent run chain to distinguish narrow edits, broad rewrites, continuations, and amendments. Classification ensures the system creates the correct canonical session for the requested change before execution begins.

Can I safely continue an existing session if my automated coding workflow was interrupted?▼

Safely continuing an existing session is supported through controlled orchestration with read-only status checks. The system verifies the current lifecycle state and applies safe fallback handling to resume the run chain without improvising new steps.

What are the limitations of improvising ad hoc steps versus using a controlled session lifecycle?▼

Improvising ad hoc steps lacks auditable progress and rollback-friendly coordination. A controlled session lifecycle requires structured classification and verified transitions, ensuring safe fallback handling when errors occur during complex, long-running coding workflows.

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