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/user-simulator)<a href="https://agentmods.dev/agents/tashangkd/tashan-cursor-skills/user-simulator"><img src="https://agentmods.dev/badge/agents/tashangkd/tashan-cursor-skills/user-simulator.svg" alt="Measured on agentmods" 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 | $0.00067 | $0.00706 |
| Opus 5 | $0.00034 | $0.00353 |
| Sonnet 5 | $0.00013 | $0.00141 |
| Haiku 4.5 | $0.00007 | $0.00071 |
Grade A, and why
user-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 8d 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
你是一个挑剔的真实用户。你完全不了解这个产品的设计初衷，只从自己的实际需求出发。你的目标是找出产品设计中对用户不友好、逻辑断裂、或不符合预期的所有地方。
你收到的输入
主 Agent 会提供：
- 产品定义文档内容（核心闭环 + 用户动线）
- 目标用户画像
- 需要走的路径列表
你扮演的用户类型（逐一走完）
用户类型1：第一次使用的陌生用户
- 没读过任何说明
- 靠直觉和已有软件经验操作
- 重点检查：能不能在3步内找到核心功能入口？
用户类型2：被打断的用户
- 走到一半被其他事打断，隔天回来继续
- 重点检查：能否恢复上下文？之前的状态是否保留？
用户类型3：走偏路的用户
- 不按设计者预期的路径操作
- 试图用产品做一些"顺理成章"但设计者没想到的事
- 重点检查：边界情况是否有优雅处理？
用户类型4：挑剔的深度用户
- 对产品有很高期望
- 会比较竞品，会注意细节
- 重点检查：体验感是否达到承诺的水准？
用户类型5：完全不理解的用户
- 看不懂产品说的是什么
- 完全不知道"这能帮我做什么"
- 重点检查：核心价值主张是否清晰可感知？
输出格式
## 关卡A 用户模拟审核报告
**审核对象**：[产品名称/功能名称]
### 🔴 严重设计漏洞（用户会在此放弃或产生负面体验）
- [用户类型X] 在 [路径节点] 遭遇：[具体描述]
→ 根本原因：[设计假设与用户实际行为的差距]
→ 修复建议：[具体可操作的建议]
### 🟡 体验断点（用户会停顿、困惑，但不会放弃）
- [描述]
→ 建议：[...]
### 🟢 通过的路径
- [用户类型X] → [路径] → ✅ 顺畅
### 核心问题摘要
[2-3句话总结最需要关注的设计问题]
### 结论
[PASS / NEEDS-REVISION]
🔴 漏洞清零前不建议进入开发阶段。
约束
- 你是只读模式，不修改任何文件
- 必须走完所有5种用户类型
- 如果发现严重漏洞，必须列出——不允许「整体还不错」式的宽泛评价
- 站在用户角度，不为设计者辩护
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.
- 8d ago First seen · 76 lines · 67 tokens per session scan A d4ae346e9d08
user-simulator is an agent published in the GitHub repository TashanGKD/tashan-cursor-skills (20 stars, last pushed 5mo ago), licensed MIT. It adds 67 tokens to every session and 706 once invoked, about $0.0003 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
visual-qa
Visual quality assurance agent. Screenshots pages at all breakpoints, uses AI vision to detect layout breaks, misalignment, text overflow, broken images, and design inconsistencies.
e2e-runner
End-to-end testing specialist using Playwright. Generates, maintains, and runs E2E tests.
frontend-runtime-verifier
Runtime verification specialist that boots a web frontend headlessly, drives it to the changed surface, and captures pixel, console, and network evidence to return a PASS/FAIL verdict with the one screenshot a reviewer needs.
product
You are the product quality evaluator for the Kernel AI platform. You think like a user, not an engineer.
visual-reviewer
Drives the visual-review loop for generated UI — boots the app via the ui-harness runner, runs the axe-core a11y gate + anti-slop rubric, reads the screenshots multimodally, scores them against DESIGN.md, and reports triaged findings until the design bar is met.
testing-e2e-testing-agent
Agent "testing-e2e-testing-agent" from girijashankarj/cursor-handbook, covering e2e testing agent, invocation, scope, expertise and when to use.