Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/sharpdeveye/maestro/teach-maestronpx skills add sharpdeveye/maestro --skill teach-maestrogit clone --depth 1 https://github.com/sharpdeveye/maestroWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/sharpdeveye/maestro/teach-maestro)<a href="https://agentmods.dev/skills/sharpdeveye/maestro/teach-maestro"><img src="https://agentmods.dev/badge/skills/sharpdeveye/maestro/teach-maestro.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00029 | $0.00712 |
| Opus 5 | $0.00015 | $0.00356 |
| Sonnet 5 | $0.00006 | $0.00142 |
| Haiku 4.5 | $0.00003 | $0.00071 |
Grade A, and why
teach-maestro scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 103 lines — stays where its author put it; the contents beside it link to each section on GitHub.
BOOTSTRAP — First Command to Run
This is the entry point for Maestro. It creates the .maestro.md context file that all other Maestro commands depend on. No other preparation is needed — this IS the preparation.
You are conducting a structured interview to understand this project's AI workflow setup. Be conversational but thorough.
Interview Questions
Ask these questions one section at a time. Wait for answers before proceeding.
Section 1 — Models & Providers
- What AI model(s) are you using? (e.g., GPT-4, Claude, Gemini, local models)
- Are you using APIs directly or through a framework? (e.g., LangChain, LlamaIndex, custom)
- What are your context window sizes?
Section 2 — Workflow Architecture
- Describe your current workflow at a high level (what goes in, what comes out)
- Do you have multiple agents/steps, or is it a single-agent system?
- What tools/functions are available to your agent(s)?
Section 3 — Quality & Evaluation
- How do you currently evaluate output quality?
- Do you have test cases or golden examples?
- What are the most common failure modes?
Section 4 — Constraints
- What are your cost constraints? (budget per request, per day)
- What are your latency requirements? (real-time, batch, async)
- Are there compliance requirements? (HIPAA, GDPR, SOC2, etc.)
Section 5 — Priorities
- Rank these from most to least important: Quality, Speed, Cost, Safety
- What's the single biggest workflow problem you want to solve?
Output Format
After gathering all answers, generate a .maestro.md file:
# Maestro Workflow Context
Generated: [date]
## Models & Providers
[answers from section 1]
## Workflow Architecture
[answers from section 2]
## Quality & Evaluation
[answers from section 3]
## Constraints
[answers from section 4]
## Priorities
[answers from section 5, with ranked priorities]
Save this file to the project root as .maestro.md.
Context Completeness
| Section | Status | Impact if Missing |
|---|---|---|
| Models & Providers | ? | Commands can't tailor advice to your stack |
| Workflow Architecture | ? | Commands can't assess complexity |
| Quality & Evaluation | ? | /iterate and /evaluate less effective |
| Constraints | ? | /guard and /accelerate can't set limits |
| Priorities | ? | All commands default to generic guidance |
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 4d ago First seen · 103 lines · 29 tokens per session scan A 690bcd398db9
teach-maestro is a skill published in the GitHub repository sharpdeveye/maestro (415 stars, last pushed 4mo ago), licensed MIT. It adds 29 tokens to every session and 712 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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