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 skills add sharpdeveye/maestro --skill agent-workflowgit 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/agent-workflow)<a href="https://agentmods.dev/skills/sharpdeveye/maestro/agent-workflow"><img src="https://agentmods.dev/badge/skills/sharpdeveye/maestro/agent-workflow/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/sharpdeveye/maestro/agent-workflow"><img src="https://agentmods.dev/badge/skills/sharpdeveye/maestro/agent-workflow.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00041 | $0.01755 |
| Opus 5.5 | $0.00016 | $0.00702 |
| Sonnet 5.5 | $0.00008 | $0.00351 |
| Haiku 4.5 | $0.00004 | $0.00176 |
Grade A, and why
agent-workflow 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 1mo 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 — 226 lines — stays where its author put it; the contents beside it link to each section on GitHub.
MANDATORY — Context Gathering Protocol
Before applying any workflow guidance, gather context:
-
Check for Maestro context in the project root
- First check
.maestro/context.md(v2 layout) - Then check
.maestro.md(v1 layout — backward compatible) - If it exists → read it and use the workflow context within
- If it doesn't exist → tell the user: "No workflow context found. Run /teach-maestro to set up project-specific context for better results."
- First check
-
Check for decision history (optional)
- If
.maestro/decisions.jsonlexists → read the last 5 decisions for session continuity - If it doesn't exist → proceed without it (no error)
- If
-
Minimum viable context (if no
.maestro.md):- What AI model(s) are being used?
- What is the workflow's primary task?
- Are there existing prompts, tools, or agents to work with?
- What are the quality/speed/cost priorities?
-
DO NOT proceed without at least understanding the model, task, and priorities.
Maestro — AI Agent Workflow Mastery
This skill provides the foundational knowledge for designing, building, and maintaining production-grade AI agent workflows. All Maestro commands build on these principles.
Core Principles
- Structure over improvisation — Workflows should be deliberate, not emergent
- Constraints are features — Explicit boundaries prevent failure modes
- Measure, don't assume — Every workflow needs evaluation, not just testing
- Appropriate complexity — Match the solution to the problem, not the ambition
- Graceful degradation — Every component should fail safely
1. Prompt Engineering
DO:
- Use structured prompts with clear sections (role, context, instructions, output format)
- Define output schemas explicitly (JSON schema, markdown template, typed response)
- Use few-shot examples for ambiguous tasks
- Chain-of-thought for multi-step reasoning
- Keep system prompts focused — one clear role per prompt
DON'T:
What ships with it
7 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 1mo ago First seen · 226 lines · 41 tokens per session scan A 160eb61fa856
agent-workflow is a skill published in the GitHub repository sharpdeveye/maestro (591 stars, last pushed 5mo ago), licensed MIT. It adds 41 tokens to every session and 1,755 once invoked, about $0.0002 per session on Opus 5.5. 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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