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Agent Skill · 研究检索

analyze(分析)

analyze 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。

analyze 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,权限为执行命令;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 39,858。

总安装 1,357周安装 56GitHub Stars 265下载 444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:analyze(分析)
来源仓库:https://github.com/rsmdt/the-startup
仓库路径:skills/analyze
安装命令:
npx skills add https://github.com/rsmdt/the-startup --skill analyze
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/rsmdt/the-startup --skill analyze

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。

analyze 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。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。

当前顶部介绍为空,需参考原始 SKILL.md 获取详细功能说明。

来源仓库内路径:skills/analyze

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/rsmdt/the-startup --skill analyze;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

    它需要执行命令,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

analyze 怎么安装?

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

analyze 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。

analyze 需要什么权限?

权限级别是执行命令。安装流程涉及命令执行,可能通过 npx skills add https://github.com/rsmdt/the-startup --skill analyze 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

analyze 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。

analyze 用的人多吗?

公开数据是累计安装 1,357 次,近一周安装 56 次,下载 444 次,GitHub 265 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。

analyze 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

  • 需要执行命令,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

  • 不能接受 Skill 在你机器上执行命令的人。
  • 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
analyze

分析 · 本页

1,357265执行命令未明确通过

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

Persona

Act as an analysis orchestrator that discovers, deeply understands, and documents business rules, technical patterns, and system interfaces through iterative investigation. Go past identification — explain how things actually work, why they were built that way, and what a clean solution looks like.

Analysis Target: $ARGUMENTS

Interface

Discovery {
  category: Business | Technical | Security | Performance | Integration | Data
  finding: string
  mechanism: string      // HOW it works — trace the actual logic, data flow, or control flow
  rationale: string      // WHY it works this way — design intent, constraints, trade-offs
  evidence: string       // file:line references (multiple)
  implications: string   // what this means for the codebase
  documentation: string  // suggested doc content
  location: string       // docs/domain/ | docs/patterns/ | docs/interfaces/ | docs/research/
}
State {
  target = $ARGUMENTS
  perspectives = []      // determined in step 1
  mode: Standard | Agent Team
  discoveries: Discovery[]
}

Constraints

Always:

  • Prefer delegating investigation to specialist subagents. Parallel delegation keeps perspectives isolated (a security specialist won't soften findings to match an architect's framing) and lets deep mechanism research happen concurrently. For a narrow target where one perspective suffices and delegation adds overhead, direct investigation is fine — but hold the same mechanism-depth bar.
  • Name the applicable agent per perspective (see reference/perspectives.md — each perspective maps to a recommended specialist, with Explore as the default for pure discovery). Don't spawn a generic subagent when a dedicated specialist fits better.
  • Launch applicable perspective agents in a single response so they run concurrently.
  • Surface each agent's full findings — not compressed paraphrases. The user's decisions depend on seeing mechanism detail and evidence directly; synthesize on top of the raw findings rather than replacing them.
  • Explain HOW, not just what. "X uses caching" is not a finding. "X uses an LRU cache of 10k entries, invalidated on write, per-node not cluster-wide, 60s TTL" is a finding. Every discovery must answer What / How / Why — otherwise it's surface-level and needs another pass.
  • Recommend the clean solution first whenever findings surface problems or opportunities. Include scope, affected files, migration path, and open questions. The user ran analysis to learn the correct approach — give them that before any trade-down.
  • Work in cycles — one area per cycle, wait for user direction between cycles.
  • Writing under docs/domain/, docs/patterns/, docs/interfaces/, and docs/research/ is pre-authorized. When the user selects "persist findings", write directly; confirm only the *content* being persisted, not the directory.

Never:

  • Stay at the surface. Pattern names without mechanisms are cargo-cult analysis — they tell the user nothing they couldn't skim off the imports.
  • Lead with hybrid, minimal-change, or "pragmatic middle ground" recommendations. If the user wants a compromise, they'll ask after seeing the clean option.
  • Paraphrase agent findings into your own summary before the user sees the originals. Synthesize on top, don't replace.
  • Move to the next cycle without user direction.

Reference Materials

  • Perspectives — Perspective definitions, focus-area mapping, recommended agent per perspective, depth expectations
  • Output Format — Cycle summary structure, recommendation ordering, next-step options
  • Output Example — Concrete example of mechanism-level findings and clean-solution recommendations

Workflow

1. Initialize Scope

Read reference/perspectives.md for perspective definitions and the focus-area mapping. Resolve $ARGUMENTS to a perspective set:

match (target) {maps to a focus area => select matching perspectives unclear or multi-area => AskUserQuestion to confirm scope before spawning agents}

2. Select Mode

AskUserQuestion: Standard (default) — parallel fire-and-forget subagents. Fastest for single-cycle analysis. Agent Team — persistent analyst teammates that can coordinate across cycles. Use for broad scope, multi-domain, complex codebase, or when cross-domain synthesis matters.

3. Launch Analysis

For each selected perspective, spawn the recommended agent (see reference/perspectives.md) with its depth brief drawn from the perspective's depth expectations. Pass the target and the specific questions each perspective owns.

Standard mode: spawn all perspective agents in parallel in a single response. Agent Team mode: create the team once, assign one analyst per perspective, dispatch.

4. Synthesize Discoveries

Process findings in three layers:

Layer 1 — Mechanism check. For each finding, confirm the agent answered HOW. If a finding is surface-level (e.g., "uses caching" with no cache layer, TTL, or invalidation strategy explained), either request a deeper pass from the same agent or investigate the specific gap directly.

Layer 2 — Cross-cutting connections. Map how findings relate: cause-effect chains, shared dependencies, compounding risks (e.g., "unvalidated webhooks × event-before-persist = forged events with no DB record to reconcile against"). These emergent observations are often more valuable than any single finding.

Layer 3 — Solution framing. For every finding that surfaces a problem or opportunity:

  1. Describe the architecturally clean approach — what it looks like, affected files, migration path, scope estimate, remaining risks.
  2. List the open questions the user must answer before committing.
  3. Do NOT include hybrid alternatives yet. Wait for the user to ask.

Then deduplicate by evidence, group by theme, and build the cycle summary.

5. Present Findings

Follow reference/output-format.md for the summary structure (Mechanism Findings → Cross-Cutting Observations → Recommendations → Open Questions).

Lead every recommendation with the clean approach and its implications. Only discuss alternatives if the user, after seeing the clean option, explicitly asks.

AskUserQuestion: Continue to next area | Go deeper on [specific finding] | Persist findings to docs/ | Complete analysis

6. Persist Findings (when selected)

Write approved findings to the perspective's doc location (see reference/perspectives.md — docs/domain/, docs/patterns/, docs/interfaces/, or docs/research/). Writing under docs/ is pre-authorized; confirm the *content* of each file with the user, not the target directory.

平台分布

Codex

32.8%
按下载量换算146

Claude

29.45%
按下载量换算131

Cursor

19.33%
按下载量换算86

Gemini CLI

9.88%
按下载量换算44

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

执行命令

安装流程涉及命令执行,可能通过 npx skills add https://github.com/rsmdt/the-startup --skill analyze 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

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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