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 patrickserrano/lacquer --skill evaluator-optimizergit clone --depth 1 https://github.com/patrickserrano/lacquerWrote 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/patrickserrano/lacquer/evaluator-optimizer)<a href="https://agentmods.dev/skills/patrickserrano/lacquer/evaluator-optimizer"><img src="https://agentmods.dev/badge/skills/patrickserrano/lacquer/evaluator-optimizer/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/patrickserrano/lacquer/evaluator-optimizer"><img src="https://agentmods.dev/badge/skills/patrickserrano/lacquer/evaluator-optimizer.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 | $0.00102 | $0.01054 |
| Opus 5.5 | $0.00041 | $0.00422 |
| Sonnet 5.5 | $0.00020 | $0.00211 |
| Haiku 4.5 | $0.00010 | $0.00105 |
Grade A, and why
evaluator-optimizer 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 — 94 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Evaluator-Optimizer
Generate → evaluate against explicit criteria → refine with the feedback → repeat until it passes or a round cap is hit. Use it when two things are both true: there's a clear bar (tests, a lint rule, a stated requirement — not "make it better") and refinement demonstrably helps (a model can act on concrete feedback better than it produced the first draft blind). If either is missing — no checkable criteria, or one attempt is already as good as five — skip the loop; it just burns rounds for no gain.
This is a different shape from advisor-checkpoint: that skill is one
strategic consult before you commit to an approach. This is a loop that
converges one artifact against a bar you can actually check.
Prefer an objective check over an opinion
Whenever the task has one, run the real check — a test suite, go vet/swiftlint/a build — rather than asking a model to judge. A test result
is ground truth; a model's opinion about whether code "looks correct" is not.
Reserve a model-as-evaluator for criteria that genuinely can't be
mechanically checked (architecture quality, whether a document actually
answers the stated question, prose clarity).
The loop
- Generate. Produce a candidate against the task.
- Evaluate. Run the objective check, or — if there isn't one — dispatch an evaluation only against explicit criteria you state up front (not "is this good," but "does it satisfy: correctness, no new lint violations, handles the empty-input case"). The evaluator's job is to grade, not to fix — keep the roles separated so the feedback is a clean signal, not a silent rewrite.
- Refine. If it fails, feed the concrete failure (the test output, the lint error, the evaluator's specific complaint) back into the next generation — not "try again," but "this failed because X."
- Repeat, capped at 3-5 rounds. If it hasn't converged by then, stop and surface the failure rather than keep spinning — a persistent failure after several rounds usually means the criteria are wrong, the task is underspecified, or the approach needs to change, not that round 6 will suddenly 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.
- 1mo ago First seen · 94 lines · 102 tokens per session scan A 4b149e0c03c2
evaluator-optimizer is a skill published in the GitHub repository patrickserrano/lacquer (4 stars, last pushed 2d ago), licensed MIT. It adds 102 tokens to every session and 1,054 once invoked, about $0.0004 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 2026-08-31.
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