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/refinenpx skills add sharpdeveye/maestro --skill refinegit 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 | $0.00028 | $0.00660 |
| Opus 5 | $0.00014 | $0.00330 |
| Sonnet 5 | $0.00006 | $0.00132 |
| Haiku 4.5 | $0.00003 | $0.00066 |
Grade A, and why
refine 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 3d 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 — 86 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.
This is the final quality pass. The workflow works — now make it excellent.
Refinement Checklist
Prompts
- Every prompt follows the 4-zone pattern (role, context, instructions, output)
- Output schemas are explicit and validated
- Negative instructions clarify what NOT to do
- No contradictory instructions
- Few-shot examples included for ambiguous tasks
- Chain-of-thought used for multi-step reasoning tasks
Tool Descriptions
- Every tool has a multi-line description: what, when to use, when NOT to use, returns
- Input parameters have descriptions and types
- Error responses are documented
- At least one example input/output in the description
Error Messages
- Error messages are specific (not "an error occurred")
- Error messages suggest corrective action
- Errors include context (what was being attempted)
- Errors are structured (code + message + details)
Logging
- Every model call is logged (input tokens, output tokens, latency, cost)
- Tool calls are logged with inputs and outputs
- Errors are logged with full context
- PII is redacted from logs
- Workflow ID traces through all log entries
Configuration
- All magic numbers are named constants
- Environment-specific values are in config, not code
- Defaults are sensible — config is for overrides
- Cost ceilings are set
- Timeout values are set for all external calls
Output
For each checklist item that fails, provide:
- What's wrong (specific finding)
- Where it is (file, line, or component)
- How to fix it (concrete suggestion)
- Priority (critical / important / nice-to-have)
Priority Matrix
| Priority | Criteria | Maestro Action |
|---|---|---|
| Critical | Affects correctness or safety | /fortify or /guard before shipping |
| Important | Affects quality or maintainability | /calibrate in current cycle |
| Nice-to-have | Cosmetic or minor inconsistency | Note for next /refine pass |
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.
- 3d ago First seen · 86 lines · 28 tokens per session scan A 2dfb74cefcab
refine is a skill published in the GitHub repository sharpdeveye/maestro (415 stars, last pushed 4mo ago), licensed MIT. It adds 28 tokens to every session and 660 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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