Borrowing it
Nothing to install: this file belongs to claude-code-expert/carve-harness. 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/claude-code-expert/carve-harness/main/.claude/skills/eval-goldenset/SKILL.mdgit clone --depth 1 https://github.com/claude-code-expert/carve-harnessWrote 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/claude-code-expert/carve-harness/eval-goldenset)<a href="https://agentmods.dev/skills/claude-code-expert/carve-harness/eval-goldenset"><img src="https://agentmods.dev/badge/skills/claude-code-expert/carve-harness/eval-goldenset/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/claude-code-expert/carve-harness/eval-goldenset"><img src="https://agentmods.dev/badge/skills/claude-code-expert/carve-harness/eval-goldenset.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Agent Snooping · line 203 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
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 | $0.00105 | $0.04685 |
| Opus 5 | $0.00053 | $0.02343 |
| Sonnet 5 | $0.00021 | $0.00937 |
| Haiku 4.5 | $0.00011 | $0.00468 |
Grade A, and why
eval-goldenset 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 — 204 lines — stays where its author put it; the contents beside it link to each section on GitHub.
eval-goldenset — 골든셋 정량평가 (모델 무관 SOP)
권위는 도구가 아니라 이 절차에 있다.
carve-eval워크플로는 이 절차를 코드로 자동 실행할 뿐이다. 태스크당 완성도는checklist-loop(항목 5축 채점)이 담당하고, 이 스킬은 고정 케이스 집합의 품질을 시간축으로 관리한다.첫 골든셋을 만드는 중이면
/eval-init을 써라 — 이 문서의 절차를 프로젝트 분석 + 대화형 인터뷰로 태우는 실행기다(SOP ⟷ 실행기 관계는checklist-loop⟷carve-verify-loop와 같다). 이 문서는 형식·판정 기준의 정본이고, 셋업 이후의 증설(트레이스 마이닝)도 여기 절차를 따른다.
언제 쓰나
- 프롬프트·
CLAUDE.md·에이전트·스킬·규칙을 바꾼 뒤 회귀 확인 — "이 변경이 출력을 나쁘게 만들지 않았나"를 숫자로. - 데모는 되는데 프로덕션은 무너지는지 — pass@k(능력) vs pass^k(일관성) 분리 측정.
- 안 쓰는 경우: 정답 없는 탐색·리서치, 골든셋을 유지할 사람이 없을 때(유지 안 하면 추이가 무의미).
골든셋 형식 (specs/goldenset/*.json)
{
"suite": "cs-agent",
"cases": [
{
"id": "refund-policy",
"version": "1.0",
"prompt": "고객: 어제 산 제품 환불돼요? 규정대로만 답하라.",
"k": 3,
"assert": [
{ "type": "contains", "value": "14일" },
{ "type": "regex", "value": "환불|반품" },
{ "type": "not_contains","value": "무조건" },
{ "type": "llm-rubric", "value": "규정 수치를 정확히 인용하고 규정 외 약속을 하지 않는다" }
]
}
]
}
assert.type3계층:- 텍스트(순수 채점):
contains·not_contains·regex·not_regex - 상태(
.claude/hooks/eval-state.sh가 결정론 채점 — 워크디렉토리의 실제 상태):file_exists(경로) ·file_contains(경로::리터럴) ·cmd_exit0(명령 exit 0) ·git_diff_contains(diff 내 리터럴) ·log_contains(logs/*.jsonl::<jq 불리언 필터>— 워크디렉토리의 하네스 훅 로그로 경로(trajectory)를 결정론 채점. 예:.decision=="block" and .tool=="Write". 외부 target(claude·exec:)에서만 유효 — 세션 응답자는 로그가 리포logs/로 간다) - 정성:
llm-rubric(evaluator 위임 — "평가의 평가" 문제가 있으니 상태 assert로 대체 가능하면 대체)
- 텍스트(순수 채점):
setup(선택): 케이스 실행 전 격리 워크디렉토리에서 실행할 bash 스크립트(환경 구성 — 파일·git 초기화 등). 상태 assert 또는setup이 있으면 respondent는 리포 밖 임시 디렉토리에서 실행된다(골든셋 정답 비노출).k: 반복 실행 횟수(기본 1, 상한 10). k>1이면 pass@k·pass^k가 의미를 가진다.tags(선택):["required", "category:domain_safety"].required케이스는 하나라도 완전 green(100)이 아니면eval-gate가 평균과 무관하게regressed로 막는다(블루프린트 §6.5 관문 ①).category:*는 리포트 분류용. 골든셋에 required가 0건이면carve-validate가 NOTE(--strict면 ERROR) — 안전·결제·인증 케이스에 붙여라. 이 리포는harness-guard5건이 required.version(필수): 케이스 정의의 버전. 케이스를 고치면 반드시 올린다. 버전 없이 점수만 쌓으면 run #3과 run #7이 서로 다른 문제를 푼 점수인데도 같은 축에 그려져 추이가 조용히 무의미해진다. 추이 엔트리에caseVersion으로 함께 기록되고, 직전 run과 다르면[VERSION CHANGED]로 경고한다.- 알 수 없는 assert 타입·잘못된 정규식·상태 채점 불능은 전부 fail-closed(통과로 새지 않음).
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 Changed · +59 lines 9a397202185a
- 9d ago First seen · 145 lines · 105 tokens per session scan A 0c3ca85df791
eval-goldenset is a skill published in the GitHub repository claude-code-expert/carve-harness (13 stars, last pushed yesterday), licensed MIT. It adds 105 tokens to every session and 4,685 once invoked, about $0.0005 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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