review-data-pipeline

SKILL开发
开发 未标注权限 许可证不明确
安装这个技能

Review Spark, SQL, and data pipeline changes for correctness, data quality, performance, idempotency, and production risk. Use when asked to review a data job, pipeline PR, Spark change, SQL transformation, or deployment safety.

一句话选型结论

按资料字段整理

review-data-pipeline是开发类的 Agent Skill,安装方式未标注,权限级别未标注,许可证不明确,有可直接复制的安装命令。技能本质是给模型读的说明书,效果取决于模型与客户端;装完先用一个小任务验证,再放进正式流程。

分类开发 安装方式未标注 权限级别未标注权限 许可证许可证不明确
累计安装 未标注 本周 未标注
GitHub 星标 未标注 仓库 未标注
下载量 未标注 技能市场下载数
权限级别 未标注权限 安装前请确认风险范围
许可证 许可证不明确 未说明

安装

未标注
在支持 Skills 的客户端里执行,装完问一句「你有哪些技能」验证
skillhub install user_814dbe54/review-data-pipeline
  1. 1
    确认客户端支持

    确认你的客户端支持 Skills(Claude、Codex、Cursor 等),并升级到较新的版本。

  2. 2
    拿到技能文件

    执行上面的命令安装;也可以把仓库里的 SKILL.md 手动放进客户端的 skills 目录。

  3. 3
    刷新技能列表

    重新打开客户端,或刷新技能列表,让新放进来的技能被加载。

  4. 4
    验证是否生效

    问一句「你有哪些技能」,确认列表里出现它,再拿一个小任务试跑。

技能档案

开发
字段来自技能仓库与公开索引,中文说明由本站标注
作者 / 维护方user_814dbe54仓库 未标注 · 版本 未标注
宿主客户端未标注技能可被这些客户端加载
安装方式未标注安装包 community
权限级别未标注权限安装前请确认风险范围
许可证未说明许可证不明确,商用前请自行确认条款
仓库可用性available技能仓库当前是否可访问
最近核验未标注上游最后核验时间 · 数据更新 1791624244
正文体量5.0 KBSKILL.md 原文大小,越大说明说明书写得越细

典型使用场景

3 类
按分类整理的常见用法,具体以 SKILL.md 为准
写代码与改 bug

按需求补代码、定位并修掉报错。

读仓库与生成改动

让模型读懂项目结构,再给出可落地的改动。

跑测试与排查故障

按现象排查问题,补测试用例并复跑。

选择建议

装之前先看这几条
按条目字段给出的判断,不替你决定用不用

值得选的地方

  • 有可直接复制的安装命令,不用自己翻文档找入口。

先想清楚的地方

  • 没有标注权限级别,实际能做什么要装好后确认。
  • 许可证不明确,商用或二次分发前先到仓库确认条款。
  • 没有做站内核验,内容以作者仓库为准。
  • 技能只是说明书,实际效果取决于模型与客户端,装完先用小任务试一遍。

更适合

  • 需要在开发场景里给模型补一套做法的人。
  • 愿意先读一遍 SKILL.md、确认它会让模型做什么的人。

不太适合

  • 对许可证要求很严、必须明确可商用的人。
  • 只想装完就自动变强、不打算看说明书的人。

同类对比

6 个同类
同分类里挑最接近的几条,字段以各自详情页为准
技能权限级别安装方式累计安装宿主客户端安装命令
review-data-pipeline当前 未标注权限 未标注 未标注 未标注 有
技能审查员 浏览器控制 ClawHub 市场 877.9 万 OpenClaw 有
Vercel React 最佳实践 浏览器控制 GitHub 仓库 849.1 万 Codex、Claude、Cursor 有
GitHub 代码协作 联网访问 ClawHub 市场 763.3 万 OpenClaw 有
网页设计指南 联网访问 GitHub 仓库 668.6 万 Codex、Claude、Cursor 有
本体论 只读 ClawHub 市场 663.7 万 OpenClaw 有

SKILL.md 全文

5.0 KB
模型实际读到的说明书原文,来自技能仓库

Review Data Pipeline

English | 中文

Review data pipeline changes with evidence-first, risk-prioritized findings. Focus on defects that can produce wrong, missing, duplicated, late, or unrecoverable data before discussing maintainability or style.

Required Inputs

Before reviewing, establish a fixed review point and obtain the exact diff:

  • a base and head commit, tag, or branch;
  • a merge base and head commit;
  • a specific commit; or
  • a complete patch supplied by the user.

If the review point is ambiguous, ask the user to choose one. Do not review a moving working tree without recording the current commit and diff. Inspect the changed files plus the minimum surrounding code, schema, tests, and configuration needed to understand their behavior.

If no diff or fixed review point is available, stop and request it. Do not infer changes from filenames, a task description, or an issue alone.

Review Priority

Always review and report in this order:

  1. Data correctness and incident risk.
  2. Grain, keys, join cardinality, and duplicates.
  3. Time, time zones, and partitions.
  4. Idempotency and reruns.
  5. Performance and resource risk.
  6. Observability and the minimum validation plan.

Use references/review-checklist.md to guide the review. Apply only checks relevant to the diff; the checklist is not evidence that a defect exists.

Evidence Rules

  • Every finding must be supported by the diff and relevant repository context.
  • Cite the narrowest useful file path and line range, function, query block, or configuration key.
  • Explain the concrete execution path from changed code to impact.
  • Distinguish observed behavior from assumptions. Resolve assumptions by inspecting code or asking a focused question.
  • Do not invent runtime statistics, table properties, key uniqueness, scheduler behavior, or business semantics.
  • If evidence is insufficient, record an open question or validation gap instead of a finding.
  • Do not report pre-existing defects unless the change newly exposes or worsens them; label such context explicitly.

Severity

  • Critical: The change can cause materially wrong data, destructive or unrecoverable writes, severe data loss, or a production failure with broad impact. It must be fixed before merge.
  • Major: The change contains an important correctness, reliability, scalability, or operability defect that should be fixed before merge.
  • Minor: The change has a bounded improvement opportunity that does not currently threaten correctness or safe operation.

Do not inflate severity. Style preferences are not findings unless they create a demonstrated maintenance or operational risk.

Output Format

Start with ## Findings. Sort findings by Critical, then Major, then Minor, while preserving the review priority within each severity.

Use this exact structure for every finding:

### [Critical|Major|Minor] <concise title>
- Location: `<path>:<line-range>` — `<symbol or query block>`
- Impact: <specific user, data, operational, or resource consequence>
- Evidence: <what the diff and surrounding code demonstrate>
- Recommendation: <smallest practical correction>
- Confidence: High | Medium | Low

Do not omit any field. A low-confidence concern without enough evidence should normally be an open question, not a finding.

If there are no findings, write No findings. Do not claim the change is risk-free.

After findings, always include:

  1. ## Open Questions — unresolved facts that could change the review, or None.
  2. ## Patch Summary — three to five concise bullets describing the reviewed change.
  3. ## Validation Plan — the smallest executable checks needed before merge.
  4. ## Residual Risks — relevant risks not eliminated by the available evidence.
  5. ## Recommendation — Merge, Merge after fixes, or Do not merge.

See references/examples.md for complete examples.

Related Skills

  • Live row-count, duplicate, null-key, or join-amplification incidents: investigate-data-quality.
  • YARN application failed, killed, or stalled at runtime: debug-spark-yarn.
  • Historical partition repair or recomputation planning: plan-safe-backfill.
  • Production change request covering schema, rollout, and rollback: prepare-production-data-change.

Safety

Treat review as read-only unless the user separately asks for changes. Never run a data write, deployment, backfill, destructive command, or expensive unbounded query as part of review. Prefer static inspection and existing test results. Before proposing a validation query, bound it by partition or a small fixture and identify any possible cost or data exposure. Redact credentials, connection strings, personal data, and proprietary values from quoted evidence.

常见问题

5 问
答案由本站依据条目字段整理
review-data-pipeline怎么安装?

在支持 Skills 的客户端里执行:skillhub install user_814dbe54/review-data-pipeline。安装方式为未标注。装完刷新技能列表或重开客户端。

review-data-pipeline支持哪些客户端?

资料没有标注宿主客户端,但只要客户端支持 Skills(Claude、Codex、Cursor 等),一般都能加载。

review-data-pipeline权限高吗,安全吗?

权限级别为未标注权限。安装前先看它会读取或写入哪些内容。

review-data-pipeline收费吗?

本站不代收费用,也没有标注具体价格。是否收费取决于技能本身和所用的模型或客户端,正式使用前按作者说明确认。

review-data-pipeline用什么许可证?

许可证标注为 未说明(许可证不明确)。商用或二次分发前请到仓库确认条款原文。

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技能内容以作者仓库为准;分类、权限级别与许可证状态的中文说明为中文整理,最近核验 未标注。