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/reflectnpx skills add sharpdeveye/maestro --skill reflectgit clone --depth 1 https://github.com/sharpdeveye/maestroWhat 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 | $0.00019 | $0.00878 |
| Opus 5 | $0.00010 | $0.00439 |
| Sonnet 5 | $0.00004 | $0.00176 |
| Haiku 4.5 | $0.00002 | $0.00088 |
Grade A, and why
reflect 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 2d 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 — 108 lines — stays where its author put it; the contents beside it link to each section on GitHub.
MANDATORY PREPARATION
Invoke /agent-workflow — it contains workflow principles, anti-patterns, and the Context Gathering Protocol. Follow the protocol before proceeding — if no workflow context exists yet, you MUST run /teach-maestro first.
Analyze the Maestro audit trail and decision log to produce a skill-effectiveness scorecard. This tells you which commands work, which fail, and where your workflow needs attention.
Data Sources
Read these files from the project root:
.maestro/audit.jsonl— every command invocation with duration, cost, and outcome.maestro/decisions.jsonl— decisions made with outcomes and next steps
If neither file exists, respond: "No audit data found. Run commands with Maestro to start tracking, then come back."
Analysis Dimensions
1. Usage Frequency
- Which commands run most/least?
- Are any commands never used? (candidates for removal)
2. Completion Rate
- What % of invocations complete successfully?
- Which commands fail most often?
3. Command Flow
- What are the most common command sequences (A → B)?
- Which commands lead to follow-ups vs. abandonment?
- Abandonment rate per command (no follow-up within 30 min)
4. Cost Distribution
- Total estimated cost across all commands
- Cost per command (average)
- Most/least expensive commands
5. Duration Analysis
- Average duration per command
- Outliers (unusually slow invocations)
Output Format
╔══════════════════════════════════════════╗
║ MAESTRO EFFECTIVENESS ║
╠══════════════════════════════════════════╣
║ Commands Run __ (__ unique) ║
║ Completion Rate __% ║
║ Most Used /_____ (__×) ║
║ Most Abandoned /_____ (__% ⚠️) ║
║ Avg Duration __s ║
║ Total Cost ~$__.__ ║
╠══════════════════════════════════════════╣
║ STRONGEST PIPELINES ║
╠══════════════════════════════════════════╣
║ /_____ → /_____ __× ║
║ /_____ → /_____ __× ║
╠══════════════════════════════════════════╣
║ COST PER COMMAND ║
╠══════════════════════════════════════════╣
║ /_____ $__.__/run ████░░ avg ║
║ /_____ $__.__/run █░░░░░ cheap ║
║ /_____ $__.__/run █████░ costly ║
╚══════════════════════════════════════════╝
INSIGHTS:
1. [Data-driven observation with recommended action]
2. [Data-driven observation with recommended action]
3. [Data-driven observation with recommended action]
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.
- 2d ago First seen · 108 lines · 19 tokens per session scan A ed59b6c99782
reflect is a skill published in the GitHub repository sharpdeveye/maestro (415 stars, last pushed 4mo ago), licensed MIT. It adds 19 tokens to every session and 878 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.
Other skills, from other repositories
agent-self-scheduling
Schedule AI agent runs with cron, loops, or external clocks while avoiding unsafe tight autonomous timers.
research
Research and information retrieval capability powered by LangChain.js. Uses GPT-4o to answer questions, summarize information, provide detailed analysis, and generate structured research outputs.
skill-creator
Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.
frontend-slides
Create stunning, animation-rich HTML presentations from scratch or by converting PowerPoint files. Use when the user wants to build a presentation, convert a PPT/PPTX to web, or create slides for a talk/pitch. Helps non-designers discover their aesthetic through visual exploration rather than abstract choices.
agnt-plugin-builder
End-to-end workflow for creating, building, installing, and hot-reloading AGNT plugins entirely from chat. Use this skill whenever the user asks to 'build a plugin', 'create an AGNT plugin', 'add a new tool to AGNT', 'integrate X with AGNT' (where X is an API or service), 'make a plugin for [service]', or wants to…
annie-universal-api-orchestrator
Use AGNT's stored OAuth tokens and API keys to call ANY third-party API directly from the orchestrator, without building a tool or plugin first. Use this skill whenever the user asks you to "do something with my GitHub / Gmail / Drive / Slack / Notion / Stripe / Shopify / Discord / Linear / Jira / Vercel / Netlify /…