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/evaluatenpx skills add sharpdeveye/maestro --skill evaluategit 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 | $0.00023 | $0.00651 |
| Opus 5 | $0.00012 | $0.00326 |
| Sonnet 5 | $0.00005 | $0.00130 |
| Haiku 4.5 | $0.00002 | $0.00065 |
Grade A, and why
evaluate 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 yesterday.
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 — 92 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. Consult the feedback-loops reference in the agent-workflow skill for evaluation patterns, golden test sets, and regression detection.
Evaluate the workflow's actual interaction quality by testing it against scenarios that represent real usage.
Evaluation Dimensions
1. Task Completion
- Does the workflow actually accomplish what it's supposed to?
- Does it handle the complete task or only the happy path?
- Are edge cases addressed or silently dropped?
2. Output Quality
- Is the output accurate, complete, and well-formatted?
- Does it match the defined output schema (if any)?
- Would a domain expert approve the output?
3. Error Behavior
- What happens when input is malformed?
- What happens when a tool fails?
- What happens when the model is uncertain?
- Is the error message useful or generic?
4. User Experience
- Is the interaction natural and intuitive?
- Are confirmations requested for destructive operations?
- Is the response time acceptable?
- Does the workflow communicate its limitations?
5. Consistency
- Does the same input produce consistent output quality?
- Are there random failures that aren't reproducible?
- Does quality degrade over long conversations?
Scenario Testing
Create and run test scenarios:
| Scenario | Input | Expected | Actual | Grade |
|---|---|---|---|---|
| Happy path | Normal input | Correct output | ? | A-F |
| Edge case | Unusual input | Graceful handling | ? | A-F |
| Error case | Bad input | Helpful error | ? | A-F |
| Stress case | Large/complex input | Reasonable handling | ? | A-F |
| Adversarial | Tricky/malicious input | Safe response | ? | A-F |
Evaluation Report
Produce a structured report with:
- Overall quality grade (A-F)
- Per-dimension scores with evidence
- Specific scenario results
- Priority improvements with recommended Maestro commands
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.
- yesterday First seen · 92 lines · 23 tokens per session scan A 7f3cb4e8eee3
evaluate is a skill published in the GitHub repository sharpdeveye/maestro (415 stars, last pushed 4mo ago), licensed MIT. It adds 23 tokens to every session and 651 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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