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 issue-trackergit 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/issue-tracker)<a href="https://agentmods.dev/skills/tashangkd/tashan-cursor-skills/issue-tracker"><img src="https://agentmods.dev/badge/skills/tashangkd/tashan-cursor-skills/issue-tracker/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/issue-tracker"><img src="https://agentmods.dev/badge/skills/tashangkd/tashan-cursor-skills/issue-tracker.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 | $0.00090 | $0.04078 |
| Opus 5 | $0.00045 | $0.02039 |
| Sonnet 5 | $0.00018 | $0.00816 |
| Haiku 4.5 | $0.00009 | $0.00408 |
Grade A, and why
issue-tracker 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.
How it starts
The opening of the file, as written. The whole thing — 292 lines — stays where its author put it; the contents beside it link to each section on GitHub.
问题追踪 Skill（Issue Tracker）
用户反馈的问题往往同时包含「产品设计层面的问题」和「技术实现层面的问题」。把它们混在一起处理，要么产品经理不知道，要么开发只修了代码但设计漏洞还在。
本 Skill 的作用：拆解 → 分类 → 记录到正确的地方 → 保证不被遗忘。
知识导航表（执行前必须理解的概念根）
| 层级 | 文档 | 需要理解的概念 |
|---|---|---|
| D0 认知根（必读） | _内部总控/认知结构/L1_系统性文档/产品理论维度/AI时代产品问题全景框架.md |
§一 产品问题全景框架（产品设计类vs技术实现类的分类依据）；§十 各角色职责边界 |
| D3 规范参考 | — | 本 Skill 主要是分类路由，无特定规范参考 |
| D4 运行时数据 | 项目群/[项目]/产品经理/产品问题追踪台.md + 项目群/[项目]/技术架构师/技术问题追踪台.md |
写入目标（追踪台文件路径，与各角色领地一致） |
核心概念速查： ① 产品设计问题 = 「应该做什么」的疑问，需PM决策，写入产品问题追踪台 ② 技术实现问题 = 「怎么做」的疑问，AI可直接修复，写入技术问题追踪台 ③ 同一问题可能同时有两种属性（例：某功能体验不好=产品设计问题+可能的技术实现缺陷）
与相关 Skill 的区别
| 本 Skill（问题追踪） | 产品反馈处理 | 规范迭代审核 | |
|---|---|---|---|
| 触发 | 用户报告了现有功能的问题（不对、用不了、报错） | 用户提出改进想法（想要新功能） | 审核 AI 对比实现与规范的差距 |
| 核心动作 | 拆解根因 + 分类记录 | 评估是否值得做 | 更新开发规范 |
| 输出 | 产品问题追踪台 + 技术问题追踪台 | 产品定义更新 + 开发计划 | 开发规范.md |
激活后立即执行
Step 1 理解用户描述的具体问题现象（不要急于判断根因）
→ 问清楚：「你看到了什么？」「期望看到的是什么？」「怎么操作触发的？」
→ 如果现象描述不清晰，先追问，再继续
Step 2 拆解根因：判断这个问题属于哪一类（可多选）
□ 产品设计类：入口不对、流程断裂、用户感知歧义、功能缺失、交互误导
□ 技术实现类：代码 Bug、接口错误、数据写入失败、性能问题、边界情况未处理
Step 3 分类记录（见下方记录格式）
→ 产品设计类 → 写入「产品问题追踪台」
→ 技术实现类 → 写入「技术问题追踪台」
→ 两类都有 → 两个文件都写
Step 4 输出拆解结果给用户，说明：
「这个问题有两个层面：[产品层面]... [技术层面]...，已分别记录，下次处理对应模块时会自动看到。」
若问题属于「技术-产品冲突（技术无法实现某需求）」类型，且产品定义已被修改：
→ 提醒用户：「产品需求范围已变更，请在修改后说「做审核」重新触发关卡A，确保新版产品定义通过审核」
Step 5 判断是否需要立即处理
⚠️ 优先轴是「AI 确定/不确定」，P0/P1/P2 是辅助参考：
P0 + AI 确定修法（唯一技术方案）→ 立即处理，不等下次
P0 + AI 不确定（涉及产品设计 / 多方案权衡）→ 立即向用户呈现，等待决策
P1/P2 + AI 确定 → 记录进追踪台，可在当前轮次顺手修复
P1/P2 + AI 不确定 → 仅记录，不打断当前任务
若项目是 AI Agent 平台（如 tashan-openbrain），记录时标注架构层：
L1/L2/L3/L4/L5/FE（帮助 bug-fix-loop-coordinator 按层排序）
Step 6 【Bug 修复后必须执行】检查是否需要写入踩坑记录
每次修复完一个 Bug，在关闭追踪台条目之前，先问：
□ 这个 Bug 是一个可重复踩的模式吗？（下次遇到类似场景还会犯同样的错？）
□ 现有踩坑速查里有没有覆盖这类问题？
→ 是可重复模式 且 踩坑速查没有覆盖 → 立即追加到对应的踩坑速查文件：
前端问题 → `.cursor/skills/role-前端开发/knowledge/前端踩坑速查.md`
后端问题 → `.cursor/skills/role-后端开发/knowledge/后端踩坑速查.md`
部署问题 → `.cursor/skills/role-DevOps/knowledge/部署踩坑速查.md`
→ 已有覆盖 / 是一次性错误 → 不需要写，关闭追踪台条目即可
⚠️ **禁止「稍后统一补」**：踩坑记录必须在当次修复完成后立即写入，
不允许以任何理由推迟。推迟等于不写。
格式（追加一行到踩坑速查表）：
| FE-XX | [症状] | [根因] | [修复方案] |
What ships with it
6 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.
- 8d ago First seen · 292 lines · 90 tokens per session scan A a6fd3a5b6341
issue-tracker is a skill published in the GitHub repository TashanGKD/tashan-cursor-skills (20 stars, last pushed 5mo ago), licensed MIT. It adds 90 tokens to every session and 4,078 once invoked, about $0.0005 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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