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Agent Skill · 研究检索

swing-review(挥杆评论)

swing-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。

swing-review 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为接触敏感数据;许可证信息还不明确,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 43,825。

总安装 955周安装 39GitHub Stars 33下载 306核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

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:swing-review(挥杆评论)
来源仓库:https://github.com/whynowlab/swing-skills
仓库路径:skills/swing-review
安装命令:
npx skills add https://github.com/whynowlab/swing-skills --skill swing-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/whynowlab/swing-skills --skill swing-review

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

挥杆评论技能用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中快速定位候选结果。它支持根据关键词、任务场景或来源线索进行信息整理,适用于评审和反馈收集场景。通过 npx skills add 命令从指定 GitHub 仓库安装,需结合原始 README 了解具体用法。

安装前建议确认权限范围和维护状态,注意是否会触发联网或文件读写操作。swing-review 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

来源仓库内路径:skills/swing-review

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/whynowlab/swing-skills --skill swing-review;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要接触敏感数据,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

swing-review 怎么安装?

在终端执行 npx skills add https://github.com/whynowlab/swing-skills --skill swing-review。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

swing-review 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

swing-review 需要什么权限?

权限级别是接触敏感数据。该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

swing-review 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

swing-review 用的人多吗?

公开数据是累计安装 955 次,近一周安装 39 次,下载 306 次,GitHub 33 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

swing-review 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 需要接触敏感数据,装到常用环境前要先评估影响面。
  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。

不太适合

  • 环境里有大量密钥、凭证,不方便给第三方 Skill 读取的人。
  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
swing-review

挥杆评论 · 本页

95533接触敏感数据未明确通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

Adversarial Review

Structured Devil's Advocate analysis that surfaces hidden flaws, edge cases, and blind spots.

Rules (Absolute)

  1. Default to finding problems. Conduct rigorous analysis across all three vectors. Report every genuine issue found — do not downplay or omit real concerns. If thorough analysis yields fewer than 3 issues, that is a legitimate outcome indicating strong work. Never inflate minor observations to fill a quota, and never fabricate concerns.
  2. Attack the strongest points. Don't waste time on trivial issues. Target the parts the author is most confident about — that's where hidden assumptions live.
  3. Separate severity levels. Not all issues are equal. Clearly distinguish critical from minor.
  4. Propose alternatives. Every criticism must include a concrete alternative or mitigation.
  5. Steel-man first. Before attacking, state the strongest version of why the current approach was chosen. This prevents straw-man critiques.
  6. No ad hominem. Critique the work, not the author. Be sharp but constructive.

Ambiguous Input Handling

If the subject under review is unclear or too broad, ask one clarifying question before proceeding. Do not review a vague target. Examples of ambiguous input that should trigger a clarification question:

  • "Review my project" (which aspect? architecture? security? specific files?)
  • "Is this okay?" with no context (what is "this"?)
  • A topic so broad that a meaningful adversarial review would be unfocused

One question. Get the answer. Then proceed.

Process

Phase 1: Steel-Man

Before any criticism, articulate:

  • Why was this approach chosen? (Best possible justification)
  • What does it optimize for? (Performance? Simplicity? Time-to-market?)
  • Under what conditions is this the right choice?

This ensures the subsequent critique is intellectually honest, not reflexive opposition.

Phase 2: Adversarial Attack (3 Vectors)

Apply three independent attack vectors simultaneously:

Vector A: Logical Soundness

Scope: Does the REASONING hold? Examine premises, conclusions, logical flow.

  • Are there logical contradictions or circular reasoning?
  • Are conclusions actually supported by the stated premises?
  • What unstated assumptions does the reasoning depend on?
  • Is there confirmation bias in the evidence selection? Do NOT examine implementation structure — that's Vector C.
Vector B: Edge Case Assault

Scope: Does it SURVIVE reality? Test against real-world conditions.

  • What happens at boundaries? (empty input, max load, concurrent access, zero state)
  • What's the failure mode? (graceful degradation vs. catastrophic failure)
  • What happens in 6 months? (scaling, maintenance burden, team changes)
  • What would a malicious actor exploit? Test behavior and outcomes, not internal structure.
Vector C: Structural Integrity

Scope: Is the STRUCTURE sound? Examine architecture and design.

  • Does each component have a single, clear responsibility?
  • Where are the coupling points and dependency chains?
  • What is the weakest structural link?
  • Which component, if changed, would cause the most cascading failures? Examine architecture, not logical reasoning.

Phase 3: Severity Classification

Classify every finding:

SeveritySymbolMeaningAction Required
Critical🔴Will cause production issues, security vulnerabilities, or data lossMust fix before merge/deploy
Major🟠Significant risk, performance issue, or maintainability problemShould fix, blocking for merge
Minor🟡Code smell, style issue, or small optimization opportunityConsider fixing, non-blocking
Note💡Observation, alternative approach, or future considerationInformational only

Phase 4: Counter-Proposal

For each Critical and Major finding, provide:

  1. What's wrong (1-2 sentences)
  2. Why it matters (concrete impact)
  3. Suggested fix (code snippet or approach)
  4. Trade-off of the fix (nothing is free — what does the fix cost?)

Output Format

## Adversarial Review: [Subject]

### Steel-Man
> [Why this approach makes sense — strongest justification]

### Findings

#### 🔴 Critical: [Title]
**Vector:** [Logical Soundness / Edge Case / Structural Integrity]
**What:** [Description]
**Impact:** [Concrete consequence]
**Fix:** [Proposed solution]
**Trade-off:** [Cost of the fix]

#### 🟠 Major: [Title]
...

#### 🟡 Minor: [Title]
...

#### 💡 Note: [Title]
...

### Summary
| Severity | Count |
|----------|-------|
| 🔴 Critical | N |
| 🟠 Major | N |
| 🟡 Minor | N |
| 💡 Note | N |

### Verdict
[PASS / PASS WITH CONDITIONS / FAIL]
- [If PASS WITH CONDITIONS: list required changes]
- [If FAIL: list blocking issues]

### Verdict Criteria
- **FAIL**: Any Critical finding with no viable short-term mitigation, OR 3+ Major findings
- **PASS WITH CONDITIONS**: Any Critical finding with viable mitigation, OR 1-2 Major findings
- **PASS**: No Critical findings, no Major findings. Minor and Notes only.
These thresholds ensure consistent verdicts across invocations.

### Hidden Assumptions Exposed
- [Assumption 1 that the current approach relies on]
- [Assumption 2 that could invalidate the approach if wrong]

Quality Calibration

BAD Adversarial Review (Don't Do This)

## Adversarial Review: User Auth Module

### Steel-Man
> It works.

### Findings

#### 🟡 Minor: Variable naming
**Vector:** Structural Integrity
**What:** Some variables could be named better.
**Impact:** Readability.
**Fix:** Rename them.
**Trade-off:** Time.

#### 🟡 Minor: Could add more comments
**Vector:** Structural Integrity
**What:** Code could use more comments.
**Impact:** Future developers might be confused.
**Fix:** Add comments.
**Trade-off:** None.

#### 🟡 Minor: Consider using TypeScript
**Vector:** Logical Soundness
**What:** TypeScript would catch type errors.
**Impact:** Fewer runtime bugs.
**Fix:** Migrate to TypeScript.
**Trade-off:** Migration effort.

### Verdict: PASS

Why this is bad:

  • Steel-man is lazy — no genuine engagement with design intent
  • All findings are shallow nitpicks, not substantive concerns
  • No Critical or Major issues even considered — no real stress-testing happened
  • Vectors are misapplied ("variable naming" is not Structural Integrity)
  • Fixes are vague ("rename them", "add comments") with no specifics
  • "Consider using TypeScript" is a preference, not a flaw found through analysis

GOOD Adversarial Review (Do This)

## Adversarial Review: User Auth Module

### Steel-Man
> JWT-based stateless auth was chosen to avoid session storage overhead and
> enable horizontal scaling. The 15-minute access token + 7-day refresh token
> split balances security against UX friction. Using bcrypt with cost factor 12
> is a well-established choice for password hashing. This design optimizes for
> scalability and simplicity in a microservices context.

### Findings

#### 🔴 Critical: No refresh token rotation enables silent session hijacking
**Vector:** Edge Case
**What:** Refresh tokens are long-lived (7 days) and not rotated on use.
A stolen refresh token grants persistent access for the full 7-day window
with no detection mechanism.
**Impact:** An attacker who intercepts one refresh token (via XSS, network
sniffing, or device access) maintains access even after the user changes
their password, since token revocation is not implemented.
**Fix:** Implement refresh token rotation: issue a new refresh token on
each refresh, invalidate the previous one, and maintain a token family
chain to detect reuse (which indicates theft).
**Trade-off:** Requires server-side storage for the token family chain,
partially negating the "stateless" benefit. Adds ~50ms per refresh request.

#### 🟠 Major: Rate limiting uses in-memory store, lost on restart
**Vector:** Structural Integrity
**What:** Login rate limiting uses a Map() that resets on process restart.
**Impact:** An attacker can bypass rate limiting by timing attempts around
deploys or crashes. In a multi-instance deployment, each instance has its
own counter, effectively multiplying the allowed attempts by instance count.
**Fix:** Move rate limit state to Redis with TTL-based expiry.
**Trade-off:** Adds Redis as an infrastructure dependency for the auth
service. ~2ms latency per rate limit check.

### Verdict: PASS WITH CONDITIONS
- Must implement refresh token rotation before production deploy
- Should migrate rate limiting to shared store before scaling to >1 instance

Why this is good:

  • Steel-man genuinely engages with the design rationale and trade-offs
  • Findings target real security risks, not style preferences
  • Each finding has specific, concrete impact (not "readability" or "confusion")
  • Fixes include implementation direction AND quantified trade-offs
  • Vectors are correctly applied and don't overlap
  • Verdict follows directly from the severity thresholds

Specialized Modes

Code Review Mode

When reviewing code (files, PRs, diffs):

  • Read all changed files with the Read tool
  • Check for OWASP Top 10 vulnerabilities
  • Verify error handling completeness
  • Assess test coverage of edge cases
  • Review naming, structure, and abstraction levels

Architecture Decision Mode

When reviewing architecture/design decisions:

  • Evaluate scalability assumptions
  • Test with 10x and 100x current load mentally
  • Check for single points of failure
  • Assess vendor lock-in risks
  • Consider team capability alignment

PR Review Mode

When reviewing pull requests:

  • Focus on behavioral changes, not style
  • Check for breaking changes to public APIs
  • Verify backward compatibility
  • Assess rollback strategy
  • Check migration paths for data changes

When to Use

  • Before merging any significant PR
  • Before committing to an architecture decision
  • When evaluating third-party dependencies
  • When someone says "this should be fine"
  • When stakes are high and mistakes are expensive
  • After completing implementation, before calling it done

When NOT to Use

  • Trivial changes (typos, formatting)
  • When exploration is needed first (use swing-research)
  • When generating alternatives (use swing-options)
  • When you need neutral, exhaustive analysis without a verdict
  • Personal preferences or subjective design choices

Gotchas

  1. Steel-man first, always. Skipping it produces straw-man critiques the author will dismiss. If you can't articulate why the current approach was chosen, you don't understand it well enough to critique it.
  2. Don't pad with style nitpicks. "Rename this variable" and "add comments" are not adversarial findings. Attack the architecture, the logic, the edge cases — not formatting.
  3. Verdict must follow the threshold rules. Don't upgrade PASS to PASS WITH CONDITIONS because you "feel" something is off. No Major finding = PASS. Period.
  4. Every criticism needs a fix AND a trade-off. "This is bad" without a concrete alternative is venting, not reviewing. And every fix has a cost — state it.
  5. Read the code before reviewing. Never critique based on file names or descriptions alone. Use Read/Grep to examine actual implementation.

Integration Notes

  • With swing-clarify: Run swing-clarify first on ambiguous requests before invoking this skill. Clarified scope produces better results.
  • With swing-options: After adversarial review reveals problems, use swing-options to generate alternative approaches
  • With swing-research: Use research to verify claims made during review (e.g., "is this really a security risk?"). For a full-rigor workflow: swing-research → swing-review
  • With orchestrator strategy team: Complements the strategy team's Devil's Advocate agent with structured methodology

平台分布

Codex

36.63%
按下载量换算112

Claude

27.53%
按下载量换算84

Cursor

19.79%
按下载量换算61

Gemini CLI

8.76%
按下载量换算27

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

接触敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

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。目前只有一个公开来源,建议同时核对源仓库。

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