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 fishzjp/qa-skills --skill regression-testinggit clone --depth 1 https://github.com/fishzjp/qa-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/fishzjp/qa-skills/regression-testing)<a href="https://agentmods.dev/skills/fishzjp/qa-skills/regression-testing"><img src="https://agentmods.dev/badge/skills/fishzjp/qa-skills/regression-testing/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/fishzjp/qa-skills/regression-testing"><img src="https://agentmods.dev/badge/skills/fishzjp/qa-skills/regression-testing.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00094 | $0.02796 |
| Opus 5.5 | $0.00038 | $0.01118 |
| Sonnet 5.5 | $0.00019 | $0.00559 |
| Haiku 4.5 | $0.00009 | $0.00280 |
Grade A, and why
regression-testing 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 1mo 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 — 130 lines — stays where its author put it; the contents beside it link to each section on GitHub.
回归测试(regression-testing)
代码变更 → 自动判断应该回归哪些测试。
- 输入:Code Diff(
git diff <base>...<head>)、Bug 修复说明、需求变更说明、已有用例(markmap +测试用例.schema.yaml)、(可选)需求模型 - 输出(落盘):
{项目}/回归清单_{日期}.md——必须回归(P0)/ 建议回归(P1)/ 可选回归(P2)三级,逐条给依据。档名沿用 P 系记号,但归档规则是影响直接程度,与用例自身优先级是两套口径:直连改动 → 必须回归,同模块间接受影响 → 建议回归;一条 [P2] 用例因 code_refs 直连改动同样进「必须回归」档(流水线分层消费按本档位执行,见../core/pipeline-integration.md) - 前置依赖:持久的"用例 ↔ 代码 ↔ 功能"追溯映射,v0 三层全部来自落盘产物,不依赖会话记忆:
- 代码级锚点:用例附录「代码证据清单(TC ↔ 文件:行)」,由 Schema 的
code_refs结构化 - 功能层:需求模型 + Schema 的
module/risk_ref - per-run 增量:改动文件映射附录(针对本次 diff 生成)
- 代码级锚点:用例附录「代码证据清单(TC ↔ 文件:行)」,由 Schema 的
没有这个数据结构,影响面分析就只是"让模型读一遍 diff 猜"。启动前三档资产盘点(决定走哪条路,防止零资产硬编清单):
- 全套齐(markmap + Schema 追溯映射都在):走工作流完整分析链
- 只有 markmap 无 Schema:先由
test-case-writing对存量 markmap 抽取补建(不需要人工重写),再进工作流- 零用例资产:不凭 diff 编造清单——向用户给降级选项:P0 冒烟建议(逐条标「无追溯依据,覆盖不保真」),或先由
test-case-writing补建最小用例集再回来跑本流程知识库输入(另查,不改变上述分流):项目存在
.qa/知识库时,启动先读其INDEX.md——flaky-tests.md的 active 条目直接作为「历史噪声 vs 真实回归」判定与回归分级的输入(写入与治理由qa-memory承担)。
When to Use
- 代码变更(feature/Bug 修复/重构)合入后,需要选择回归范围
- Bug 修复后需要确定"修复验证 + 关联回归"的用例集
- 需要一份逐条给依据、可裁剪的分级回归清单
When NOT to Use
- 用例文件的增量修改本身(标记/新增/废弃用例)→
test-case-writing增量更新流程 - 长期回归策略(锚点用例、回归节奏)→
test-strategy的 regression_plan - 单条失败的分辨与定性 → 执行类 skill 单条流程(
automated-e2e-testing工作流二 /api-testing§4);一轮失败 ≥3 条先走../core/triage.md分流;Bug 经用户定性裁决后才进bug-analysis(其输入必须是已确认 Bug) - 端到端流水线 →
qa编排
分析链
Changed Files(git diff)
↓
Changed Functions(diff 中被修改的函数/类/组件)
↓
Affected Features(函数 → 所属功能:经 Schema code_refs / module / 需求模型映射)
↓
Affected Test Cases(功能 → 用例:risk_ref / module / 附录改动文件映射)
↓
Regression Scope(三级回归清单 + 依据)
工作流
1. 盘点变更
git diff <base>...<head> --stat # 改动文件清单
git diff <base>...<head> -U3 # 逐文件读改动内容,定位改动函数
变更类型分类(基础四类,与 test-case-writing 阶段一「改动盘点」的枚举一致):🆕 新建 / ✏️ 修改(可按需细分标注:行为修改、🐛 Bug 修复(关联 Bug 单)、🔧 重构(行为应不变))/ 🗑️ 删除 / 💀 死代码。类型不同,回归侧重不同(重构 → 全量行为锚点;Bug 修复 → 修复验证 + 该 Bug 历史关联用例;死代码 → 不产生回归用例)。
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.
- 1mo ago Changed 4b1d750fd5e1
- 1mo ago Changed · -4 tokens per session 689713126830
- 1mo ago Changed · +2 lines dcfbe45c8bab
- 1mo ago Changed · +3 lines 6726a8793d0d
- 1mo ago First seen · 125 lines · 98 tokens per session scan A 5acd8cafaf3a
regression-testing is a skill published in the GitHub repository fishzjp/qa-skills (50 stars, last pushed 3d ago), licensed MIT. It adds 94 tokens to every session and 2,796 once invoked, about $0.0004 per session on Opus 5.5. 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 skills, from other repositories
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Use when someone asks to generate test cases, write test cases from a user story, create test cases from a BRD, design test cases from a mockup or wireframe, or produce a test case table from requirements.
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qa-engineer
QA specialist agent for test planning, execution, and regression analysis.
playwright-expert
Expert in Playwright E2E testing framework, auto-waiting mechanisms, test generation, trace viewer, and CI/CD integration. Use when the user mentions testing, end-to-end tests, QA, automation, end-to-end testing, or test automation, or when the task involves Playwright Framework, Test Organization, Advanced Features…