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 Chris1Wang3/Idea-on-Trial --skill skill-quality-scorergit clone --depth 1 https://github.com/Chris1Wang3/Idea-on-TrialWrote 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/chris1wang3/idea-on-trial/skill-quality-scorer)<a href="https://agentmods.dev/skills/chris1wang3/idea-on-trial/skill-quality-scorer"><img src="https://agentmods.dev/badge/skills/chris1wang3/idea-on-trial/skill-quality-scorer/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/chris1wang3/idea-on-trial/skill-quality-scorer"><img src="https://agentmods.dev/badge/skills/chris1wang3/idea-on-trial/skill-quality-scorer.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 | $0.00124 | $0.01234 |
| Opus 5 | $0.00062 | $0.00617 |
| Sonnet 5 | $0.00025 | $0.00247 |
| Haiku 4.5 | $0.00012 | $0.00123 |
Grade A, and why
skill-quality-scorer 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 11d 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 — 100 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill Quality Scorer · 技能质量评分器
EN TRACE+ (T-R-F-S-I-E) × 30 sub-items · static script first · formula score · JSON + Markdown.
中文 TRACE+ 六维 30 子项 · 先 static_audit · 公式算分 · JSON + Markdown。
When / 何时用： 迭代没方向 · 对标同类 · 发布前自检 · 批量评 skills/
Not / 不用： 从零写 Skill · 替代 skill-eval 行为实验（E 维默认 static_proxy）
Score portfolio-doctor with TRACE+ — where vs skill-reviewer?
给 portfolio-doctor 做 TRACE+ 全维评分。
可直接触发的说法
以下口语输入都应触发本技能：
- 给这个 skill 打分。
- 看看这个技能哪里不符合规范。
- 用 TRACE+ 评一下这个 SKILL.md。
- 这两个技能哪个质量更高？
- 批量扫一下这个 skills 目录。
- SkillHub 分数低，帮我找提分项。
- 和 skill-reviewer 差在哪？
最小可用输入
最低可启动：一个目标 skill 目录，或一个 SKILL.md 文件路径。
推荐输入：目标路径、评分模式（单个/A vs B/批量）、是否有行为评测结果、期望输出格式（JSON / Markdown / 两者）。
可兼容输入：
- 只给目录：自动定位目录下
SKILL.md。 - 只给父目录：按批量模式逐个扫描子目录。
- 只给两条路径：按 A vs B 对比模式。
- 没有 behavior eval：E 维使用
static_proxy，E3 不强行给满。
评测模式
| 模式 | 输出 |
|---|---|
| 单个 | JSON + Markdown（audit-playbook） |
| A vs B | 两份 JSON + 分差表 + 推荐 |
| 批量 | 汇总表 + 各 skill 简评 |
评分前先确认输出格式：JSON、Markdown 或两者。用户未指定时，默认输出 JSON + Markdown。
对比/批量：同一 rubric v2，不得换公式或跳过子项。
工作流
1) 定位 skill 目录（对比/批量则逐个重复 2–6）
2) python scripts/static_audit.py "<skill-dir>" → auto_scores（不可改分）
3) Read scoring-engine-deterministic.md → 30 子项 evidence
4) Read 目标 SKILL.md + 链接的 references/scripts
5) composite = round((T+R+F+S+I+E)×100/60, 1) → 评级 + Verdict
6) 按 audit-playbook 输出（含 F 维触发测试各 ≥3 条）
评级 / Verdict / 30 子项定义 / JSON schema → scoring-engine-deterministic.md
硬约束
- 先脚本后 rubric；
auto_scores只补 evidence 不改分 - 30 子项逐项 evidence；禁止旧公式
(T+R+A+C+E)×2 - E 维默认
static_proxy；有 skill-eval 时切换behavioral_eval - Rigid：子项、公式、Verdict 不可改 · Flexible：evidence 表述、Top 修复排序
验收与失败路径
- 路径不存在：先要求用户提供正确路径，不得凭名称臆测
- 缺少 SKILL.md：判定目标不是标准 skill 包，输出结构问题而非强行评分
- 批量模式：每个 skill 必须使用同一 rubric v2 和同一公式
- 无行为评测：明确标注
effectiveness_mode: static_proxy - 完成标准：JSON 算术校验通过，Markdown 报告含 Top 修复项和触发测试
What ships with it
5 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.
- 11d ago First seen · 100 lines · 124 tokens per session scan A 3ff1f6067ff7
skill-quality-scorer is a skill published in the GitHub repository Chris1Wang3/Idea-on-Trial (4 stars, last pushed 23d ago), licensed MIT. It adds 124 tokens to every session and 1,234 once invoked, about $0.0006 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 2026-08-31.
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