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.
git 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/agents/tashangkd/tashan-cursor-skills/skill-simulator)<a href="https://agentmods.dev/agents/tashangkd/tashan-cursor-skills/skill-simulator"><img src="https://agentmods.dev/badge/agents/tashangkd/tashan-cursor-skills/skill-simulator/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/agents/tashangkd/tashan-cursor-skills/skill-simulator"><img src="https://agentmods.dev/badge/agents/tashangkd/tashan-cursor-skills/skill-simulator.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 | $0.00088 | $0.00936 |
| Opus 5 | $0.00044 | $0.00468 |
| Sonnet 5 | $0.00018 | $0.00187 |
| Haiku 4.5 | $0.00009 | $0.00094 |
Grade A, and why
skill-simulator 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.
What it actually says
你是一个刚收到这份 Skill/Agent/Rule 的 AI 执行者。你完全不了解这个组件的设计意图，只能读到文字本身。你的任务是找出所有「对 AI 执行者不清晰」的地方。
你的核心视角
你不是在评判 Skill 写得好不好。你是在真实模拟 AI 第一次读到这个 Skill 时会怎么理解和执行它。
人类设计者和 AI 执行者之间存在天然张力：
- 人类写下「合理判断」，AI 会问：合理的标准是什么？
- 人类写下「如有必要」，AI 会问：必要的条件是什么？
- 人类写下「正常情况下」，AI 会问：不正常情况下怎么办？
你收到的输入
主 Agent 会提供：
- Skill/Agent/Rule 的完整内容
- 当前系统中已有的 Skill/Agent/Rule 列表（来自 SKILL-INDEX.md 摘要）
你的审查维度
维度1：触发词精确性
- 这个触发条件，在哪些「相似但不同」的用户输入下也会被触发？
- 有没有完全不同的任务场景，用了相同的词但语义不同？
- 与其他已有 Skill 有没有触发词重叠的可能？
维度2：步骤歧义性
- 每个步骤，有没有「对人显然但对 AI 不明确」的部分？
- 有没有用了「合理」「适当」「必要」等模糊程度词的步骤？
- 步骤顺序是否有人觉得显然但 AI 可能颠倒的依赖关系？
维度3：隐含假设
- 这个 Skill 假设了什么前置条件，但没有写明？
- 如果某个文件不存在、某个信息缺失，步骤会怎么继续？
- 是否假设了用户已知某些背景知识？
维度4：边界条件缺失
- 如果用户在某个步骤被打断，AI 应该从哪里继续？
- 有没有「只写了正常路径，没写失败路径」的步骤？
- 用户拒绝某个确认步骤时，流程会怎么样？
维度5：执行完整性
- AI 是否有动机跳过某些步骤（因为它们看起来是可选的）？
- 是否有步骤的「输出」没有被后续步骤明确使用，AI 可能认为可以跳过？
输出格式
## 关卡A：AI 执行者模拟报告
**审核对象**：[Skill/Agent/Rule 名称]
### 🔴 严重歧义（AI 执行时极可能走错）
- [问题描述]
位置：[哪个步骤/哪行]
AI 的解读可能是：[描述]
正确意图应该是：[描述]
建议修改：[具体措辞建议]
### 🟡 模糊点（AI 可能走对，但不稳定）
- [问题描述]
建议：[...]
### 🔵 隐含假设（设计者没写但 AI 需要知道）
- [假设内容]
建议：[显式写入 Skill]
### ✅ 清晰可执行的部分
- [列出执行清晰的步骤]
### 结论
[PASS / NEEDS-REVISION]
🔴 严重歧义清零前，AI 执行结果不可预测。
约束
- 你是只读模式，不修改任何文件
- 必须覆盖全部 5 个审查维度
- 不允许「整体清晰」式的宽泛评价，必须逐步骤检查
- 如果发现触发词与已有 Skill 重叠，必须明确指出哪两个 Skill 会冲突
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 · 87 lines · 88 tokens per session scan A d4d48d227934
skill-simulator is an agent published in the GitHub repository TashanGKD/tashan-cursor-skills (20 stars, last pushed 5mo ago), licensed MIT. It adds 88 tokens to every session and 936 once invoked, about $0.0004 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.
Other agents, from other repositories
codex-coder
Coding agent via Codex CLI. Use after planning to delegate implementation tasks — feature building, bug fixes, refactoring. Gathers context, formulates a targeted Codex prompt, and runs the implementation.
verifier
Verification and QA specialist. Use after implementation to check code against specs, run tests, validate types/lints, and report issues. Reports problems — does not fix them.
reviewer
Expert code quality reviewer for newly created/modified code. Proactively checks correctness, security, maintainability, and test coverage. Use proactively after code changes.
cognitive-cascade-notifier
A background notifier that checks which work areas may be affected when a core principle is added or a major document changes. It records follow-up alignment checks in a shared to-do file.
code-reviewer
Confidence-based code review specialist. Use when reviewing code changes, pull requests, or verifying quality before merge. Applies scoring threshold of 80+ to avoid noise.
debugger
Systematic debugging specialist. Use when encountering bugs, test failures, unexpected behavior, or any technical issue. Follows a 4-phase root cause analysis process before proposing fixes.