Borrowing it
Nothing to install: this file belongs to KonghaYao/peri. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/KonghaYao/peri/main/.claude/skills/auto-issue-fixer/SKILL.mdgit clone --depth 1 https://github.com/KonghaYao/periWrote 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/konghayao/peri/auto-issue-fixer)<a href="https://agentmods.dev/skills/konghayao/peri/auto-issue-fixer"><img src="https://agentmods.dev/badge/skills/konghayao/peri/auto-issue-fixer.svg" alt="Measured on agentmods" 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 | $0.00106 | $0.03397 |
| Opus 5 | $0.00053 | $0.01699 |
| Sonnet 5 | $0.00021 | $0.00679 |
| Haiku 4.5 | $0.00011 | $0.00340 |
Grade A, and why
auto-issue-fixer 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 — 348 lines — stays where its author put it; the contents beside it link to each section on GitHub.
auto-issue-fixer: Issue 全生命周期管理
单入口技能，覆盖 issue 的创建 → 修复 → 验证 → 归档全流程。你根据用户输入自动判断阶段并执行对应操作。
阶段分发
收到用户输入后，先判断阶段，再执行：
| 用户输入特征 | 阶段 | 动作 |
|---|---|---|
| 描述 bug/异常/性能/重构需求，无明确 issue 路径 | 创建 | 访谈 → 生成 issue 文档 |
给出 spec/issues/xxx.md 路径或 issue 标题 + "修"/"fix" |
修复 | 读 issue → 改代码 → 更新文档 |
| "验证"/"verify"/"好了"/"还是不行" + issue 引用 | 验证 | 定位 issue → 问反馈 → 更新状态 |
| "归档"/"archive" + 可选 issue 引用 | 归档 | 扫描终态 issue → 移动 → 提炼认知 |
优先级：如果用户输入同时匹配多个阶段（如 "修一下 xxx issue 然后验证"），按 创建→修复→验证→归档 的顺序逐一执行，每阶段完成后自动进入下一阶段。
阶段一：创建（Create）
核心原则
你是记录员，不是诊断员。产出是症状文档，不包含根因分析。
| 允许 | 禁止 |
|---|---|
| 读用户提到的文件来理解术语 | 追踪数据流跨多个文件找 bug 源头 |
| 记录用户观察到的现象 | 写"根因总结"或"问题本质" |
| 搜索函数名确认其存在 | 搜索函数的所有调用点来验证假设 |
| 阅读 ≤ 5 个文件 | 阅读第 6 个文件（越界了） |
1.0 历史 Issue 检查
收到用户描述后，在提问之前用 Grep 在 spec/issues/ 中搜索相关关键词。找到匹配时告知用户，让其选择更新已有 issue 还是新建。
如果是更新已有 issue：追加新现象到「症状详情」，更新状态变更记录（Fixed→Reopen, etc.），不覆盖原有内容。
1.1 理解初始描述
分析用户输入，提取提到的文件/模块/函数/错误信息。只读用户提到的文件来理解术语。
第一轮提问：扫描所有模糊点和缺失信息，将 2-4 个独立问题打包为一次 AskUserQuestion 调用（AskUserQuestion 支持 1-4 问，尽量用满）。各问题间相互独立、不依赖前一问的答案。优先覆盖以下维度：
- Bug：复现频率、触发步骤、期望 vs 实际行为、环境
- 重构/技术债：当前代码问题、期望改进方向
- 性能：规模/负载条件、具体数据
- 安全：风险描述、影响范围
⚠️ 必须批量提问：每次
AskUserQuestion至少问 2 个问题。一次只问一个问题会让用户反复等待，体验极差。如果当前只有 1 个模糊点，想想还有哪些维度没覆盖——总可以凑出第二个问题。不要问"能不能详细说说？"这种开放式问题。
1.2 补充轮次
- 用户回复后，如果第一轮覆盖了所有关键信息 → 直接进入 1.4 自动定性
- 如果还有漏掉的维度或新暴露的模糊点 → 再打包 2-4 个问题为一轮
- 如果用户提到新文件，去读那个文件理解术语
- 不问代码能直接回答的问题（文件有多少行等）
- 总轮次 ≤ 2：最多两轮提问，之后直接基于已有信息生成 issue
1.4 自动定性与评级
自动判定，不询问用户：
| 类型 | 条件 |
|---|---|
| Bug | 异常行为、错误信息、崩溃、与预期不符 |
| 安全 | 数据泄露、注入、权限绕过等 |
| 性能 | 耗时、吞吐量、内存、卡顿 |
| 重构 | 结构性改进目标（拆分、解耦、迁移） |
| 技术债 | 职责混乱、重复代码、文件过大 |
| 文档 | 缺失/过时文档 |
| 优先级 | 条件 |
|---|---|
| 高 | 数据丢失/崩溃/功能不可用；安全漏洞可被利用 |
| 中 | 部分场景影响但有 workaround；增加修改成本 |
| 低 | 代码质量改进；非阻塞优化 |
1.5 生成文档
按 references/issue-template.md 格式生成 issue，保存到 spec/issues/YYYY-MM-DD-<slug>.md。
slug 从标题生成，kebab-case，描述现象。spec/issues/ 不存在时先创建目录。
What ships with it
1 file 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 · 348 lines · 106 tokens per session scan A 5ce3ddc78b77
auto-issue-fixer is a skill published in the GitHub repository KonghaYao/peri (163 stars, last pushed today), licensed Apache-2.0. It adds 106 tokens to every session and 3,397 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 2026-08-30.
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