maestro-analyze

Analyze codebases into CSV tasks and decision artifacts via multi-wave agents.

511|63|Updated Mar 17, 2026
One-click install
npx skills add https://github.com/catlog22/Maestro-Flow --skill maestro-analyze-catlog22
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: maestro-analyze
Source: https://github.com/catlog22/Maestro-Flow/tree/main/.codex/skills/maestro-analyze
Command: npx skills add https://github.com/catlog22/Maestro-Flow --skill maestro-analyze-catlog22

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Maestro Analyze automates multi-dimensional technical analysis that would otherwise require coordinating many human reviewers and manual note-taking. It converts exploration, evidence gathering, and multi-axis scoring into a reproducible CSV-driven pipeline so teams can get objective, auditable Go/No-Go recommendations and decision context quickly.

Core Features & Use Cases

  • Wave-based multi-agent orchestration: runs exploration agents, six-dimension scoring agents, and a single synthesis agent in a diamond topology to produce a consolidated verdict.
  • CSV-first state and append-only discovery board: uses tasks.csv and discoveries.ndjson as the single source of truth for provenance, retries, and merging agent outputs.
  • Quick and full modes: supports a quick synthesis-only shortcut or a full exploration → score → synthesize flow with concurrency controls and graceful degradation.
  • Use case: evaluate a proposed Phase 3 feature by extracting code anchors, scoring feasibility/impact/risk/complexity/alignment/maintainability, and generating analysis.md, context.md, and conclusions.json for planners and implementers.

Quick Start

Run the maestro-analyze command with a subject like "microservices vs monolith" (optionally -y to auto-run) to produce an analysis.md, context.md, and the CSV session artifacts in .workflow/.csv-wave.

Frequently Asked Questions about maestro-analyze

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

FAQPage Schema
How do I automate multi-agent codebase analysis for technical decisions?

Multi-agent codebase analysis uses a wave-based orchestration pipeline to explore code, score across six dimensions, and synthesize Go/No-Go recommendations. It converts manual review into a reproducible CSV-driven workflow for objective technical trade-off evaluation.

What is CSV-based orchestration for multi-agent analysis?

CSV-based orchestration uses a tasks.csv file as the single source of truth for agent state and provenance. An append-only discoveries.ndjson board captures findings, enabling retries and merging agent outputs across concurrent waves.

How do I score codebase features for feasibility and risk?

Scoring codebase features involves evaluating them across six dimensions: feasibility, impact, risk, complexity, alignment, and maintainability. Dedicated scoring agents process extracted code anchors to generate objective multi-axis assessments.

Can I run a quick synthesis-only analysis without full code exploration?

Yes, a quick-mode shortcut supports synthesis-only analysis to bypass full exploration. This allows rapid generation of conclusions without running the complete exploration and scoring waves, while full mode offers concurrency controls and graceful degradation.

What outputs does multi-dimensional codebase analysis generate?

Multi-dimensional analysis generates an analysis.md for documentation, context.md for planners, and conclusions.json for structured verdicts. It also produces session artifacts like tasks.csv and discoveries.ndjson in the workflow directory.

When should I use multi-wave orchestration instead of manual codebase review?

Use multi-wave orchestration when you need reproducible, auditable technical analysis for phase planning. It replaces coordinating multiple human reviewers and manual note-taking with a diamond topology of exploration, scoring, and synthesis agents.