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 TashanGKD/tashan-cursor-skills --skill skill-domain-self-optimizergit clone --depth 1 https://github.com/TashanGKD/tashan-cursor-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/tashangkd/tashan-cursor-skills/skill-domain-self-optimizer)<a href="https://agentmods.dev/skills/tashangkd/tashan-cursor-skills/skill-domain-self-optimizer"><img src="https://agentmods.dev/badge/skills/tashangkd/tashan-cursor-skills/skill-domain-self-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/tashangkd/tashan-cursor-skills/skill-domain-self-optimizer"><img src="https://agentmods.dev/badge/skills/tashangkd/tashan-cursor-skills/skill-domain-self-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 | $0.00140 | $0.02315 |
| Opus 5 | $0.00070 | $0.01157 |
| Sonnet 5 | $0.00028 | $0.00463 |
| Haiku 4.5 | $0.00014 | $0.00231 |
Grade A, and why
skill-domain-self-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 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.
How it starts
The opening of the file, as written. The whole thing — 198 lines — stays where its author put it; the contents beside it link to each section on GitHub.
域自我优化(skill-domain-self-optimizer)
关系类型:depends-on(依赖域健康报告 + 沙盘文件 + DOMAIN-REGISTRY) 强绑定 Rule:R2 NO_FABRICATION / R6 ARTIFACT_FIRST / R13 RULE_BEATS_STYLE
核心设计原则
Gap → 行动 原则:每个 Gap 必须对应一个具体可执行的行动建议(不能只说「缺什么」,必须说「用什么Skill填补、或新建什么Skill」)。
证据优先 原则:建议必须基于沙盘中已记录的具体 Gap,不能凭推断扩展到沙盘未覆盖的问题。
激活后立即执行
Step 1 确认处理范围
询问(如果用户未指定):
「请指定要优化的域:
(1) 产品开发 (2) 认知结构 (3) 公司运营 (4) 内容宣传 (5) Skill体系 (6) 全部」
若是由 skill-closure-verifier-meta 调用,直接使用传入的域名称。
Step 2 读取输入数据(R3 READ_FIRST)
并行读取:
- _内部总控/skill-system-design/domain-health-[域名]-*.md(域健康报告,取最新)
- _内部总控/skill-system-design/sandboxes/[域名]/*.md(该域所有沙盘)
- _内部总控/skill-system-design/DOMAIN-REGISTRY.md(节点图)
- _内部总控/skill-system-design/NODE-IO-CONTRACTS.md(I/O契约)
若健康报告不存在:
→ 先调用 skill-domain-health-check 生成健康报告,再继续
若沙盘数量 < 3:
→ 在报告中注明「沙盘样本不足,建议扩充后重新运行」
→ 但仍基于现有数据给出建议,不阻断
Step 3 Gap 分类与模式识别
汇总所有 Gap 来源(健康报告 + 沙盘 Gap 发现表格):
按类型分类:
- 【类型A】缺节点:对应Skill文件不存在
- 【类型B】缺触发条件:节点间的触发边缺失或不明确
- 【类型C】I/O不匹配:节点的输出与下游节点的输入要求不对应
- 【类型D】文档不自洽:域内核心文档之间内容矛盾
- 【类型E】边界盲区:入口节点未覆盖的触发词/场景
识别模式:
→ 同一节点被多个沙盘标注 Gap?→ 该节点是高优先修复点
→ 同一边(触发条件)被多个沙盘标注断裂?→ 该链路是系统性缺陷
Step 4 生成行动建议
对每类 Gap,生成对应的行动方案:
【类型A处理:缺节点】
→ 直接建议:「新建 Skill [skill-name],对应节点 [节点ID]」
→ 输出 Skill 产品定义草稿(2-3句话描述功能、触发词、I/O)
→ 标注:「建议通过 skill-designer(Level [N])正式设计」
【类型B处理:缺触发条件】
→ 定位缺失的触发边(哪个节点完成后应触发哪个下游节点)
→ 建议:「修改 [上游Skill].SKILL.md:在 [步骤N] 末尾增加触发下游的明确步骤」
→ 给出具体的修改建议文字(如:「在 Step X 末尾增加:触发条件满足时,建议用户执行 [下游节点]」)
【类型C处理:I/O不匹配】
→ 定位不匹配点:上游输出的格式/路径 ≠ 下游需要的格式/路径
→ 建议:「修改 [Skill].SKILL.md:明确输出格式/路径」或「修改 NODE-IO-CONTRACTS 更正描述」
【类型D处理:文档不自洽】
→ 建议触发 doc-consolidator 或 cognitive-consistency-check(域相关)
【类型E处理:边界盲区】
→ 建议:「更新 [Skill].SKILL.md 的 description 触发词部分,增加 [场景描述]」
Step 5 优先级排序
按以下规则排序行动建议:
P0(立即执行):
- 缺节点且是关键传播路径上的节点
- 多个沙盘均标注的相同 Gap
P1(近期执行):
- 缺触发条件(影响闭环完整性)
- I/O不匹配(影响数据流正确性)
P2(计划执行):
- 边界盲区覆盖(扩展而非修复)
- 文档自洽问题(可在下次复盘时处理)
Step 6 输出域优化建议报告(R6:必须写文件)
写入:_内部总控/skill-system-design/domain-optimizer-[域名]-YYYYMMDD.md
格式(见下方「报告格式」)
同时在对话中输出摘要(已识别 N 个Gap,生成 M 条行动建议)
Step 7 路由执行建议
P0 行动建议:
→ 询问用户:「是否现在立即处理 P0 项?」
→ 若同意:路由到对应 Skill(skill-designer / skill-rule-修改规范)
P1/P2 行动建议:
→ 建议追加到 PENDING-SKILLS.md(如果是新建Skill)
→ 或告知用户「下次运行 skill-rule-修改规范 时处理」
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 · 198 lines · 140 tokens per session scan A b049543e83cc
skill-domain-self-optimizer is a skill published in the GitHub repository TashanGKD/tashan-cursor-skills (20 stars, last pushed 5mo ago), licensed MIT. It adds 140 tokens to every session and 2,315 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 2026-09-03.
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