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sharpdeveye

maestro

Workflow fluency for AI coding agents. 1 core skill · 25 commands · 7 domain references · memory layer · audit trail — works across Cursor, Claude Code, Gemini CLI, Copilot, and 6 more.

AI EngineeringGive agent toolsBuild agent workflow (SDK)TypeScript
Open on GitHub
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Created 2026-04-02 · Updated 2026-10-09 · #16444 today
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sharpdeveye

sharpdeveye/maestro is ranked 16,444 with 591 stars.

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Key parameters and specifications
Key parameters and specifications
ParameterValue
LanguageTypeScript
CategoryAI Engineering
SubcategoryGive agent tools, Build agent workflow (SDK)
KeywordsAgent-ready APIs, Memory management, Prompt engineering, Skills
Stars591
Forks74
Contributors1
Created date2026-04-02
Updated date2026-10-09
Stars in 1 day0
Stars in 7 days-1
Rank16,444
Previous rank16,233
Rank change-211
7-day stars rank17,896
Previous 7-day stars rank17,845
7-day stars rank change-51
Total stars rank16,978
Previous total stars rank16,950
Total stars rank change-28
README summary

AI agents are only as good as the workflows they operate in. Without guidance, you get the same predictable mistakes: unstructured prompts, context window overflows, tool sprawl, no error handling, and multi-agent systems for single-agent problems. Local (stdio) Add to your MCP client config (Claude Desktop, Cursor, VS Code, etc. ): Remote (HTTP) Host Maestro as a public MCP endpoint: Clients connect to . Please ensure: All content is original (no copying from other skill projects) SKILL. md files have

valid YAML frontmatter with starting with "Use when.

Frequently asked questions

How many stars and forks are recorded?

sharpdeveye/maestro has 591 stars and 74 forks.

What is the repository rank?

sharpdeveye/maestro is ranked 16,444.

What language and category are recorded?

sharpdeveye/maestro has 591 stars, uses TypeScript and belongs to AI Engineering.

Which repository is closest in rank?

sharpdeveye/maestro is compared with michaelalbada/BuildingApplicationsWithAIAgents, ranked 16,442 with 591 stars.

README

1 core skill · 25 commands · 7 domain references · memory layer · audit trail

Quick Start · Commands · What's New in v2 · Supported Tools · Contributing


What is Maestro?

AI agents are only as good as the workflows they operate in. Without guidance, you get the same predictable mistakes: unstructured prompts, context window overflows, tool sprawl, no error handling, and multi-agent systems for single-agent problems.

Maestro fights that pattern with:

  • A comprehensive agent-workflow skill with 7 domain-specific reference files (view source)
  • 25 commands to diagnose, evaluate, refine, streamline, fortify, capture, reflect, and more
  • Persistent memory — decisions, audit trail, and session history survive across sessions
  • Curated anti-patterns that explicitly tell the AI what NOT to do
  • A context gathering protocol (.maestro.md or .maestro/context.md) that ensures every command has project-specific awareness
  • Every command recommends a next step — no dead ends

Quick Start

npx skills add sharpdeveye/maestro

Then use any command in your AI coding agent:

/diagnose          # Find workflow issues
/streamline        # Remove unnecessary complexity
/fortify           # Add error handling
/refine            # Final quality pass

Most commands accept an optional argument to focus on a specific area:

/diagnose prompts
/fortify payment-workflow
/specialize legal

Combine Commands

/diagnose /calibrate /refine    # Full workflow: audit → standardize → polish
/evaluate /fortify /accelerate  # Review → harden → optimize

The Skill: agent-workflow

A comprehensive workflow design skill with 7 domain references (view skill):

Reference Domain
prompt-engineering Prompt structure, few-shot, CoT, output schemas
context-management Window optimization, memory, state management
tool-orchestration Tool design, chaining, error handling, sandboxing
agent-architecture Topologies, handoffs, multi-agent patterns
feedback-loops Evaluation, self-correction, regression detection
knowledge-systems RAG, chunking, embeddings, source attribution
guardrails-safety Validation, prompt injection, cost ceilings

25 Commands

Analysis — read-only, generate reports

Command Purpose
/diagnose Systematic workflow quality audit with scored dimensions
/evaluate Holistic review of workflow interaction quality
/reflect 🆕 Analyze command history — which skills work, which fail

Fix & Improve — make targeted changes

Command Purpose
/refine Final quality pass on prompts, tools, and configuration
/streamline Remove unnecessary complexity, flatten over-engineering
/calibrate Align workflow components to project conventions
/fortify Add error handling, retries, fallbacks, circuit breakers
/zero-defect Activate maximum precision mode — zero mistakes allowed

Enhancement — add capabilities

Command Purpose
/amplify Boost capabilities with better tools and context
/compose Design multi-agent orchestration and delegation
/enrich Add knowledge sources, RAG, and grounding
/accelerate Optimize for speed, reduce latency and cost
/chain Build effective tool chains and pipelines
/guard Add safety constraints and security boundaries
/iterate Set up feedback loops and evaluation cycles
/temper Reduce over-engineering, simplify overbuilt workflows
/turbocharge Push past conventional limits — advanced techniques

Utility

Command Purpose
/extract-pattern Extract reusable patterns from working workflows
/adapt-workflow Adapt workflows for different providers/contexts
/onboard-agent Set up new agent configurations from scratch
/specialize Make workflows domain-specific (legal, medical, etc.)
/teach-maestro One-time context gathering, saves to .maestro.md
/capture 🆕 Save a session summary — persist what happened
/recap 🆕 Quick summary of the last session

What's New in v2

Memory Layer

Maestro now remembers what happened across sessions:

.maestro/
├── context.md        ← project context (replaces .maestro.md)
├── decisions.jsonl    ← append-only decision log
├── audit.jsonl        ← every command invocation with cost + duration
└── sessions/
    └── 2026-04-26_fix_auth.md  ← session summaries
  • Backward compatible — .maestro.md users change nothing
  • Opt-in — .maestro/ is created only when you run /capture or use the extension
  • Git-friendly — session data is gitignored by default, context file is versioned

Audit Trail

Every command invocation is logged with duration, token usage, and estimated cost:

{"command":"fortify","duration_ms":8200,"cost_estimate_usd":0.019,"exit_status":"completed"}

Cost Estimation

Approximate cost tracking for Claude, GPT-4, Gemini, and more. Accuracy: ±20% — useful for trends, not invoicing.

/reflect — Effectiveness Scorecard

Analyze your command history to see which skills work, which fail, and where to improve:

╔══════════════════════════════════════════╗
║          MAESTRO EFFECTIVENESS           ║
╠══════════════════════════════════════════╣
║ Commands Run         23 (7 unique)       ║
║ Completion Rate      87%                 ║
║ Most Used            /fortify (6×)       ║
║ Total Cost           ~$0.47              ║
╚══════════════════════════════════════════╝

Anti-Patterns ("Workflow Slop")

The skill includes explicit guidance on what to avoid:

  • Don't dump entire codebases/databases into context
  • Don't use multi-agent systems for single-agent problems
  • Don't skip error handling (happy path only = production failure)
  • Don't retry the same prompt hoping for different results
  • Don't deploy without cost controls
  • Don't use vague tool descriptions that confuse the model
  • Don't ship without evaluation ("it seems to work" ≠ tested)

Supported Tools

Tool Directory
Cursor .cursor/skills/
Claude Code .claude/skills/
Gemini CLI .gemini/skills/
Codex CLI .codex/skills/
VS Code Copilot / Antigravity .agents/skills/
Kiro .kiro/skills/
Trae .trae/skills/
Trae China .trae-cn/skills/
OpenCode .opencode/skills/
Pi .pi/skills/

MCP Server

Use Maestro as a live MCP server instead of static skill files. Any MCP-compatible client can connect — no file copying required.

Local (stdio)

Add to your MCP client config (Claude Desktop, Cursor, VS Code, etc.):

{
  "mcpServers": {
    "maestro": {
      "command": "npx",
      "args": ["-y", "maestro-workflow-mcp"]
    }
  }
}

Remote (HTTP)

Host Maestro as a public MCP endpoint:

npx maestro-workflow-mcp --http --port 3001

Clients connect to http://your-server:3001/mcp.

What the MCP Server Exposes

Type Count Description
Prompts 25 One per command — select from the prompt picker
Tools 10 list_commands, run_command, read_context, init, wave_start, wave_advance, wave_status, write_decision, read_decisions, read_audit
Resources 8 Core skill + 7 domain references

Manual Installation

If npx skills add doesn't work for your setup, copy the appropriate provider directory to your project root:

# Example for Claude Code
cp -r .claude/skills/ your-project/.claude/skills/

# Example for Cursor
cp -r .cursor/skills/ your-project/.cursor/skills/

Project Structure

maestro/
├── source/skills/           # 25 source skill definitions
│   ├── agent-workflow/      # Core skill + 7 reference files
│   │   └── reference/       # Domain-specific guidance
│   ├── diagnose/            # Analysis commands
│   ├── evaluate/
│   ├── reflect/             # 🆕 Effectiveness analysis
│   ├── refine/              # Fix & improve commands
│   ├── streamline/
│   ├── calibrate/
│   ├── fortify/
│   ├── zero-defect/
│   ├── amplify/             # Enhancement commands
│   ├── compose/
│   ├── enrich/
│   ├── accelerate/
│   ├── chain/
│   ├── guard/
│   ├── iterate/
│   ├── temper/
│   ├── turbocharge/
│   ├── extract-pattern/     # Utility commands
│   ├── adapt-workflow/
│   ├── onboard-agent/
│   ├── specialize/
│   ├── teach-maestro/
│   ├── capture/             # 🆕 Session persistence
│   └── recap/               # 🆕 Session recovery
├── packages/core/           # Shared utilities
│   └── src/
│       ├── context-utils.ts  # Section parser + matcher
│       ├── token-estimator.ts # Token estimation
│       ├── decisions.ts      # 🆕 Decision log
│       ├── audit.ts          # 🆕 Audit trail
│       └── cost-estimator.ts # 🆕 Cost estimation
├── maestro-extension/       # VS Code extension
├── mcp-server/              # MCP server package
│   ├── src/
│   │   ├── index.ts         # Entry point (stdio + HTTP)
│   │   ├── http.ts          # HTTP transport wrapper
│   │   ├── tools.ts         # 10 MCP tools
│   │   ├── prompts.ts       # 25 MCP prompts
│   │   └── resources.ts     # 8 MCP resources
│   └── package.json
├── scripts/
│   ├── build.js             # Multi-provider build pipeline
│   ├── bundle-skills.js     # MCP skill bundler
│   └── validate.js          # Skill validation checks
└── package.json

Contributing

Contributions welcome! Please ensure:

  • All content is original (no copying from other skill projects)
  • SKILL.md files have valid YAML frontmatter with description starting with "Use when..."
  • All code fences have a language specifier
  • Run npm run check to validate before submitting

License

MIT — see LICENSE.

Created by sharpdeveye

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