Token导航 LogoToken导航TokenDH.com
研究检索执行命令github未标认证来源可访问许可证需确认审计通过

analyze分析

Agent Skill

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。

总安装

1,357

周安装

56

GitHub Stars

265

下载量

444
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: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

简介

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。

SKILL.md

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.mddocs/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.

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

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。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

继续浏览同类 Skills