Borrowing it
Nothing to install: this file belongs to dzhng/duet-agent. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/dzhng/duet-agent/main/.agents/skills/eval-skills/SKILL.mdgit clone --depth 1 https://github.com/dzhng/duet-agentWrote 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/dzhng/duet-agent/eval-skills)<a href="https://agentmods.dev/skills/dzhng/duet-agent/eval-skills"><img src="https://agentmods.dev/badge/skills/dzhng/duet-agent/eval-skills/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/dzhng/duet-agent/eval-skills"><img src="https://agentmods.dev/badge/skills/dzhng/duet-agent/eval-skills.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 | $0.00114 | $0.01810 |
| Opus 5 | $0.00057 | $0.00905 |
| Sonnet 5 | $0.00023 | $0.00362 |
| Haiku 4.5 | $0.00011 | $0.00181 |
Grade A, and why
eval-skills 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 9d 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.
This is a copy
100% identical to eval-skills — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 129 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Eval Skills
Treat a skill like a function under test. Feed it example inputs in a clean room, check the artifacts against what good looks like, and let the failures drive the edits. The eval is only honest if the run is blind: the agent executing the skill must carry none of this conversation's context and must never see the expected output. Leak either and you are teaching to the test.
Inputs you need — refuse without them
Confirm all three before spawning anything. If any is missing or unresolvable, stop and tell the user exactly which one and what a good version looks like. Do not invent cases, guess intent, or eval against a fuzzy wish.
- Target skill — must resolve to a real
SKILL.md. If you can't find it, list the skills you can see and ask which one they mean. - At least one golden case — a concrete input the skill will actually receive: a screenshot, a prompt, a file, a scene. "Improve write-spec" with no input attached is not a case.
- The bar per case — the outcome a good artifact achieves and the smells that would make it bad, not an exhaustive parts list. The skill's judgment is what's under test, so do not pre-enumerate every requirement — that turns the eval into a conformance check and stops testing whether the skill decides well. "Sliced so each piece is independently buildable and verifiable, at the granularity a competent practitioner would pick — a lazy mega-slice and pointless over-splitting are both failures" is a bar a judge can hold the work to; "slices it well" is too thin to grade and a fixed list of expected slices is too prescriptive. State the bar and the smells; let the judge apply them. The exception is a conformance-style skill that genuinely wants an exact task hit exactly — then the explicit criteria are the bar; match the bar's shape to the skill's nature, and if you can't tell which it is, ask. If the user gives only a fuzzy wish with no bar, draw the bar out of them and echo it back before spending agents.
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.
- 9d ago First seen · 129 lines · 114 tokens per session scan A 2e9bf33371fc
eval-skills is a skill published in the GitHub repository dzhng/duet-agent (42 stars, last pushed 6d ago), licensed Apache-2.0. It adds 114 tokens to every session and 1,810 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to eval-skills, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
evaluator-write-qa-parallel
Internal Auto-Harness evaluator skill for parallel sprint QA and QA report writing. Use only inside the Evaluator subagent during evaluatorqaparallel.
evaluator-write-qa
Internal Auto-Harness evaluator skill for sprint QA and QA report writing. Use only inside the Evaluator subagent during qa mode.
evaluator-write-final-parallel
Internal Auto-Harness evaluator skill for parallel final QA report aggregation. Use only inside the Evaluator subagent during evaluatorfinalparallel.
evaluator-write-retest-parallel
Internal Auto-Harness evaluator skill for parallel sprint retest and retest report writing. Use only inside the Evaluator subagent during evaluatorretestparallel.
evaluator-write-retest
Internal Auto-Harness evaluator skill for sprint retest and retest report writing. Use only inside the Evaluator subagent during retest mode.
generator-apply-fixes
Internal Auto-Harness generator skill for QA fix cycles. Use only inside the Generator subagent when it is addressing named defects from QA or retest.