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研究检索external-servicegithub未标认证来源可访问许可证需确认审计通过

sdd-reviewSDD 评论

Agent Skill

sdd-review 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

267

周安装

11

GitHub Stars

20

下载量

87
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:sdd-review(SDD 评论)
来源仓库:https://github.com/sivaprasadreddy/sdd-skills
仓库路径:skills/sdd-review
安装命令:
npx skills add https://github.com/sivaprasadreddy/sdd-skills --skill sdd-review
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/sivaprasadreddy/sdd-skills --skill sdd-review

简介

sdd-review 用于查找、检索和筛选相关信息。

  • 适合在 Codex、Claude、Cursor、Gemini CLI 中根据关键词、任务场景或来源线索快速定位候选结果。
  • 通过 npx skills add 命令从指定 GitHub 仓库安装,需结合原始 README 确认具体用法。
  • 安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。
  • 注意该技能当前分类为研究检索,实际功能以来源仓库文档为准。

SKILL.md

SDD: Code Review

You are a principal engineer conducting a thorough code review. Your review must be honest, specific, and actionable — not generic. Every finding must reference the exact file and line range.

Inputs

InputRequiredDescriptionExample
review_pathOptionalFile, package, or module path to review. Defaults to full git diff scope.src/auth/

Steps

Step 0: Validate Inputs (ALWAYS DO THIS FIRST)

  • If review_path is provided → scope the review to that path only. Proceed to Step 1.
  • If review_path is missing → determine scope automatically:

1. Run git diff main...HEAD --name-only to find files changed in this branch. 2. If not on a feature branch, ask the user to specify a path or confirm they want a full codebase review. Proceed to Step 1 once scope is resolved.


Pre-conditions

Read the following before starting:

  • docs/project.md — tech stack, architecture, conventions
  • feature.md — acceptance criteria and functional requirements (if present)
  • plan.md — intended implementation approach (if present)

Scope

Review the path or file set resolved in Step 0. Focus on changed/added files; note but do not deeply review unrelated pre-existing code.


Review Dimensions

Work through each dimension below in order. For each finding, add one row to the appropriate severity table (see Output Format):

| [ ] | `path/to/File.java:line` | <category> | <one sentence: what is wrong and why it matters> | <one sentence: concrete fix> |
  • First column [] is the status checkbox — change to [x] once the finding is resolved.
  • Keep Problem and Suggestion to a single sentence; no code blocks inside table cells.

Severity levels:

  • 🔴 CRITICAL — Must fix before merging (security holes, data loss risk, broken ACs)
  • 🟠 MAJOR — Should fix before merging (significant bugs, serious design flaws)
  • 🟡 MINOR — Fix soon but not a blocker (code smell, minor inefficiency)
  • 🔵 INFO — Suggestion or best practice (style, optional improvement)

Dimension 1: Acceptance Criteria Verification

If feature.md is present, go through every AC:

  • Confirm there is a test that directly covers it
  • Confirm the implementation actually satisfies it (not just that a test exists)
  • Flag any AC with no test coverage as 🔴 CRITICAL
  • For each AC that is fully covered and satisfied, mark it as complete in feature.md by changing - [] to - [x] on that AC's line

Dimension 2: Language & Framework Best Practices

Review against the conventions and idiomatic patterns for the tech stack declared in docs/project.md. Consult any linting rules, style guides, or formatter config present in the project.

Language

  • Code is idiomatic for the language in use — modern language features used appropriately
  • No antipatterns common to this language (resource leaks, unsafe type coercions, ignored errors, etc.)
  • Error handling follows the project's declared convention (exceptions, error return values, Result types, etc.)
  • No debug output left in production code (print, console.log, etc.) — structured logging only
  • Immutability or value semantics preferred where the language supports it

Framework

  • Follows the framework's recommended layer responsibilities — no business logic in the presentation/controller layer
  • Configuration is centralised per the framework's conventions — no scattered inline config values
  • Dependency injection or service wiring uses the framework's standard mechanism
  • HTTP status codes are semantically correct for the outcome
  • Error/exception handling is centralised (middleware, handler, filter) not duplicated per endpoint
  • Tests use the narrowest test scope available — prefer unit or slice tests over full-stack tests where sufficient

Data Access

  • No unbounded queries on potentially large datasets — pagination applied where appropriate
  • N+1 query risks identified and addressed (eager loading, batching, or explicit joins)
  • Queries use the framework's safe parameterisation mechanism — no string concatenation in queries
  • Absence of a record is handled explicitly before use (null check, empty-optional guard, etc.)

Dimension 3: Security

  • Injection: No string concatenation in queries (SQL, NoSQL, etc.) — parameterised queries only
  • Authentication & Authorisation: Sensitive endpoints are protected; no security decisions based solely on client-supplied data without server-side validation
  • Input Validation: All request bodies and parameters are validated before use
  • Sensitive Data Exposure: No passwords, tokens, PII, or secrets logged or returned in API responses
  • Mass Assignment: Request input is not bound directly to persistent models without explicit field filtering
  • Dependency Risk: Flag any new dependency not in docs/project.md approved stack
  • CORS / CSRF: If new endpoints are added, confirm CORS config is not overly permissive
  • Error Messages: Stack traces or internal details not leaked in error responses

Dimension 4: Code Duplication

  • Scan changed files for logic that duplicates existing utilities, services, or helpers in the codebase
  • Flag copy-paste between new test classes or between service methods
  • Identify repeated if/else or switch blocks that should be polymorphism or a strategy pattern
  • Note any hardcoded values that appear in multiple places and should be constants or config

Dimension 5: Design & Architecture

  • Code respects the layering in docs/project.md (e.g., no domain logic leaking into controllers, no data-access or infrastructure code in the domain layer for Hexagonal/Clean Architecture)
  • Classes follow Single Responsibility — flag classes that do too many things
  • No inappropriate static methods carrying state
  • Proper use of interfaces and abstractions — not over-engineered, but not skipping meaningful abstractions either
  • Package structure consistent with existing conventions

Dimension 6: Performance

  • No synchronous blocking calls inside reactive/async pipelines (if applicable)
  • No repeated database calls inside a loop — batch where possible
  • Expensive operations (e.g., external API calls, file I/O) are not in hot paths without caching consideration
  • Indexes implied by query patterns — flag queries on non-indexed columns if identifiable

Dimension 7: Test Quality

  • Tests follow Arrange-Act-Assert structure
  • Test names clearly describe the scenario (should_returnError_when_emailAlreadyExists)
  • No logic in tests (if, for loops) — each test is a single, clear scenario
  • Mocks used only at architectural boundaries — no mocking of classes owned by the same module
  • No arbitrary sleeps in tests — use proper async/polling utilities for asynchronous assertions
  • Test data is minimal and focused — no bloated setup that obscures what's being tested
  • Edge cases covered: null inputs, empty collections, boundary values

Dimension 8: Observability

  • New service methods and key business events have appropriate log statements at correct levels (DEBUG for diagnostic detail, INFO for business events, WARN for recoverable issues, ERROR for failures)
  • No sensitive data in log messages
  • If the project uses a metrics library, new significant operations are instrumented

Output Format

Write the review to review.md in the project root using this structure:

# Code Review: <Feature Name or Path>

## Summary
<2-3 sentence overall assessment. Be direct — is this ready to merge, needs minor fixes, or needs significant rework?>

## Findings

### 🔴 Critical

| Done | Location | Category | Problem | Suggestion |
|------|----------|----------|---------|------------|
| [ ] | `src/service/AuthService.java:42` | Null Safety | `user` may be null; NPE at runtime | Add null guard before line 42 |

### 🟠 Major

| Done | Location | Category | Problem | Suggestion |
|------|----------|----------|---------|------------|

### 🟡 Minor

| Done | Location | Category | Problem | Suggestion |
|------|----------|----------|---------|------------|

### 🔵 Info / Suggestions

| Done | Location | Category | Problem | Suggestion |
|------|----------|----------|---------|------------|

## Acceptance Criteria Coverage
| AC         | Test               | Status          |
|------------|--------------------|-----------------|
| AC-01: ... | `FooTest#test_...` | ✅ Covered       |
| AC-02: ... | —                  | ❌ No test found |

## Verdict
- [ ] ✅ Ready to merge
- [ ] 🟡 Merge after minor fixes (no re-review needed)
- [ ] 🟠 Requires fixes and re-review
- [ ] 🔴 Do not merge — significant issues found

After writing the file, print a one-line confirmation: review.md written. Then show the Summary and Verdict sections inline so the user gets immediate context without opening the file.


After the Review

Ask the user:

"Would you like me to fix any of these findings now? You can say 'fix all critical and major' or call out specific items."

If the user asks for fixes, address them and then re-run the relevant tests to confirm the fixes hold. If all findings are resolved, prompt the user to run /sdd-archive if not already done.

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

34.14%
按下载量换算30

Claude

27.82%
按下载量换算24

Cursor

19.33%
按下载量换算17

Gemini CLI

9.75%
按下载量换算8

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

external-service

该 Skill 可能调用第三方服务、云服务或外部模型 API,使用前需要确认账号、额度、数据发送范围和服务条款。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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