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 WiseWong6/wise-skills --skill skill-optimizergit clone --depth 1 https://github.com/WiseWong6/wise-skillsWrote 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/wisewong6/wise-skills/skill-optimizer)<a href="https://agentmods.dev/skills/wisewong6/wise-skills/skill-optimizer"><img src="https://agentmods.dev/badge/skills/wisewong6/wise-skills/skill-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/wisewong6/wise-skills/skill-optimizer"><img src="https://agentmods.dev/badge/skills/wisewong6/wise-skills/skill-optimizer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00148 | $0.02937 |
| Opus 5 | $0.00074 | $0.01469 |
| Sonnet 5 | $0.00030 | $0.00587 |
| Haiku 4.5 | $0.00015 | $0.00294 |
Grade A, and why
skill-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 2d 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 — 149 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill Optimizer
目标与边界
把 Skill 当成可执行产品,以一条可复核证据链完成诊断和优化:
权威源码 → 发行载荷 → Agent 安装入口/软链 → Agent 实际加载 → 真实任务
一个规则、资源和版本坐标只有一个主人;开发、发行、安装和运行职责分离。依赖允许存在,但必须说明用途、阶段、体积、引入路径和替代边界。优化不能删除必要的正确性、安全、权限、许可证、来源或用户入口。
每次只处理一个目标 Skill。范围外问题记录证据和建议,不擅自扩大修改面。
选择模式
- 用户说“审计、诊断、评估”时保持只读,只交付问题、影响、证据和建议。
- 用户说“优化、精简、修复”时先给人话方案;用户明确确认后才修改。
- 用户尚未确认时,不写文件、不删除内容、不自动修软链,也不展示大段 diff。
- 目标用户、核心任务或输出合同需要改变时,说明这已是新 Skill,改用
skill-creator,不静默重建。
先读哪份规则
审计软链、发行 manifest、版本、死重或结构时,必须按需读取 生命周期审计判据。它定义证据图、引用入边、候选分类、结构矩阵、阻断级别和死重确认流程。
检查 Codex 或 RedSkill 的 frontmatter/目录政策时,再读取 平台 Schema Profile。平台推荐结构与通用正确性分开报告;RedSkill 的上传白名单、identifier、审核阈值和线上版本仍归 redskill-pack,不迁入本 Skill。
审计测试脚本膨胀、case-by-case、重复 runner 或测试结构时,必须读取 测试体系审计合同。测试语义归目标 Skill 的权威源码仓所有;本 Skill 只校验仓内声明、执行边界与文件覆盖,不靠通用正则替源仓决定哪些测试应该合并。
建立问题合同
先用事实回答:谁维护、谁发布、谁安装、谁执行;目标路径属于源码、发行、安装还是尚未确定;预期行为、实际行为、最小复现和失败信号是什么。
至少分开核对:
- 权威源码及 Git HEAD/dirty;本地 upstream 不是实时远端,需要当前远端事实时才只读查询。
- 发行目录或压缩包、发行 manifest、
source_commit与文件哈希。 - 每个 Agent 的入口原路径、
readlink每一跳和最终realpath。 - 安装副本是否为软链、共享目录或独立副本,内容是否漂移。
- Agent 是否真的加载该 Skill 并完成代表任务;静态解析成功不能冒充真实运行成功。
不通过目录名猜开发仓或发行仓。只有目标本身是显式 source、显式 --source,或发行 manifest 的可信关系能确立权威边。发现无关未提交改动时保留;与目标重叠且无法安全区分时暂停确认。
先运行只读审计
python3 <本 Skill 目录>/scripts/audit_skill.py <目标 Skill 目录> \
--surface <auto|source|release|installed> \
--profile <auto|general|review> \
--schema-profile <auto|codex|redskill> \
--source <权威源码 Skill 目录> \
--release-manifest <发行 manifest 路径> \
--agent-entry codex=<Codex 安装入口> \
--agent-entry agents=<Agents 安装入口> \
--supported-node-majors 22,24 \
--metafile <esbuild metafile 路径> \
--test-system-contract <源仓 test-system.json> \
--format json
除目标目录外,其余参数按证据可用性提供:
--surface auto不确立发行或安装权威,因此不输出可删除候选。--profile auto在release使用review,其他载体使用general。--schema-profile auto保持 Codex 合同;只有显式目标为 RedSkill 时使用redskill。--agent-entry可重复;逐条解析,禁止依赖 shell 空格分词生成或修复软链。--supported-node-majors由调用方按当前官方支持矩阵传入;审计器离线运行,不把内置值冒充实时事实。--test-system-contract可显式指定源仓测试体系声明;相对路径按 Git 根解析,非 Git 目标按目标根解析。未提供时只发现标准仓内位置,不执行仓内测试命令。- 未提供 metafile 时只查找常见文件名。未知 Skill 的 doctor/build/install 不自动执行,不联网、不安装依赖、不自动修改或删除内容。
What ships with it
15 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.
- agents/openai.yaml 921 B
- LICENSE 1.0 KB
- README.md 2.7 KB
- references/lifecycle-audit.md 7.1 KB
- references/platform-schemas.md 2.0 KB
- references/test-system-audit.md 5.9 KB
- scripts/audit_skill.py 601 B runs code
- scripts/skill_audit/__init__.py 196 B runs code
- scripts/skill_audit/core.py 111 KB runs code
- scripts/skill_audit/lifecycle.py 27 KB runs code
- scripts/skill_audit/model.py 1.6 KB runs code
- scripts/skill_audit/reachability.py 18 KB runs code
- scripts/skill_audit/schema.py 4.9 KB runs code
- scripts/skill_audit/structure.py 10.0 KB runs code
- scripts/skill_audit/test_system.py 33 KB runs code
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.
- 2d ago Changed · +8 lines · +15 tokens per session d76a98804ca5
- 8d ago First seen · 141 lines · 133 tokens per session scan A 60276b871092
skill-optimizer is a skill published in the GitHub repository WiseWong6/wise-skills (6 stars, last pushed 3d ago), licensed MIT. It adds 148 tokens to every session and 2,937 once invoked, about $0.0007 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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