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研究检索执行命令github未标认证来源可访问clear审计提醒

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

474

周安装

19

GitHub Stars

1

下载量

154
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

3

许可证

MIT

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/jschulte/claude-plugins
仓库路径:skills/analyze
安装命令:
npx skills add https://github.com/jschulte/claude-plugins --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。不同来源提供的安装方式可能略有差异;本站展示可直接复制的安装命令,安装前请核对来源页面。

skills.shnpx skills
npx skills add https://github.com/jschulte/claude-plugins --skill analyze

简介

用于查找、检索和筛选相关信息。analyze 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

  • 适合根据关键词或任务场景快速定位候选结果。
  • 可辅助研究类任务的信息收集环节。适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。
  • 建议结合具体需求验证搜索策略的有效性。
  • 需注意来源仓库的维护状态和实际功能边界。

SKILL.md

Initial Analysis (Gear 1 of 6)

Estimated Time: 5 minutes Output: analysis-report.md in the current working directory (always here, regardless of spec_output_location)


Activation Triggers

Activate this skill when the user says any of:

  • "Analyze this codebase"
  • "What tech stack is this using?"
  • "How complete is this application?"
  • "Run initial analysis"
  • "Start reverse engineering process"
  • Starting reverse engineering on a new or existing codebase

Execution Pipeline

This skill has four phases. Execute them in order.

Phase 0: Install Slash Commands

Run the commands in operations/install-commands.md to copy /speckit.* commands into the project.

If the copy command fails, log: "Slash command installation skipped -- commands can be installed later." Continue to Phase 1.

Log to user: "Phase 0 complete -- slash commands installed."

Phase 1: Detection

Check for Batch Session

Follow the procedure in operations/batch-session.md to search for .stackshift-batch-session.json.

If a batch session is found, load its answers, save them to .stackshift-state.json, skip Phase 2, and proceed to Phase 3.

If reading the batch session file fails, log the error and fall back to the normal questionnaire in Phase 2.

Log to user: "Phase 1 complete -- detection type: [type]."

Auto-Detect Application Type

Run these detection checks in priority order. Use the first match:

  1. monorepo-service: Parent directory is services/ or apps/ and ../../package.json exists
  2. nx-app: nx.json exists in current or grandparent directory
  3. turborepo-package: turbo.json exists in current or grandparent directory
  4. lerna-package: lerna.json exists in current or grandparent directory
  5. osiris: Repository name starts with ws-
  6. v9-velocity: widget.vm exists or path contains v9/widgets/
  7. v9-viewmodel: Path contains v9/viewmodel/widgets/
  8. generic: No specific pattern matched (default)

For detection types v9-velocity, v9-viewmodel, and osiris: these are legacy CMS widget types. Run the standard analysis phases (Phase 3) using generic analysis operations. Note in the report that this is a legacy CMS widget. If widget migration framework is selected in Phase 2, the /stackshift.widget-migrate command handles the specialized analysis.

Phase 2: Questionnaire

Present the questions defined in operations/questionnaire.md.

Ask questions conversationally, one at a time or in small related groups. Wait for the user to respond before continuing. Apply the conditional logic rules from that file to determine which questions to skip.

After collecting all answers, write them to .stackshift-state.json.

If .stackshift-state.json already exists, ask the user: "Existing configuration found. Reuse it or start fresh?"

Log to user: "Phase 2 complete -- configuration saved to.stackshift-state.json."

Phase 3: Analysis

Run all five analysis operations. For each operation, use the corresponding reference file. Run operations in parallel where possible.

  1. Detect tech stack -- operations/detect-stack.md Run all language detection commands in parallel. Missing files are normal. Log to user: "Analysis 1/5 complete -- tech stack detected."
  2. Extract core metadata -- Read application name, version, description from manifest files. Read git remote URL. Log to user: "Analysis 2/5 complete -- metadata extracted."
  3. Analyze directory structure -- operations/directory-analysis.md Map architecture patterns and key components. Log to user: "Analysis 3/5 complete -- directory structure analyzed."
  4. Scan documentation -- operations/documentation-scan.md Assess existing documentation quality. Log to user: "Analysis 4/5 complete -- documentation scanned."
  5. Assess completeness -- operations/completeness-assessment.md Estimate completion percentages with evidence. Log to user: "Analysis 5/5 complete -- completeness assessed."

Before proceeding to Phase 4, verify that all five operations produced results. If any operation produced no results, re-run it once. If it still fails, record "Unable to assess" for that section and continue.

Phase 4: Report Generation

Generate analysis-report.md using the template in operations/generate-report.md.

Write the report to the current working directory as analysis-report.md.

Read the StackShift version from .claude-plugin/plugin.json for the report header.

Log to user: "Phase 4 complete -- analysis-report.md generated."

Present a summary of findings to the user and ask if they are ready to proceed to Gear 2 (Reverse Engineer).


Path Descriptions

Greenfield (Build New App from Business Logic)

Use when building a new application based on existing business logic, migrating to a different tech stack, or wanting platform-agnostic specifications.

Result: Specifications focus on WHAT (business requirements only), tech-stack agnostic.

Example: "Extract the business logic from this Rails app so we can rebuild it in Next.js"

Brownfield (Manage Existing with Spec Kit)

Use when managing an existing codebase with specs, planning upgrades or refactoring, or needing specs that match current implementation.

Result: Specifications include WHAT and HOW (business logic + technical implementation), tech-stack prescriptive.

Example: "Add GitHub Spec Kit to this Next.js app so we can manage it with specs going forward"


Shell Command Error Handling

Apply these rules to every shell command across all operation files:

  1. Commands that return no output: Treat as "not found" and note the absence in the report. Do not treat as an error.
  2. Commands that fail with non-zero exit: Log the failure and continue with remaining commands. Never abort analysis due to a single command failure.
  3. Commands that produce excessive output: Use head -50 for directory listings and head -20 for grep results. Note truncation in the report if it occurs.
  4. Commands on files with spaces in names: Use -print0 / -0 flags with find/xargs pipelines where practical.
  5. Always exclude: node_modules, vendor,.git, build, dist, target, pycache,.next,.nuxt, coverage directories.

State Cleanup

If analysis fails or is interrupted before report generation, delete .stackshift-state.json to prevent stale configuration from affecting re-runs.

If the user cancels mid-analysis, inform them: "Analysis was interrupted. Run the analyze skill again to restart."


Cruise Control Behavior

When Cruise Control transmission is selected, the entire pipeline runs non-interactively after Phase 2. The progress log messages at each phase boundary serve as checkpoints. If any phase fails during Cruise Control, stop execution, report the failure, and ask the user how to proceed.


Handling Re-analysis

If analysis-report.md already exists when this skill activates:

  1. Ask the user: "An analysis report already exists. Update it, or skip to Gear 2?"
  2. If updating: re-run the full analysis pipeline and overwrite the existing report.
  3. If skipping: confirm the existing report is still accurate and proceed to Gear 2.

If the user already knows their tech stack and just wants to configure the pipeline, skip Phase 3 analysis operations and proceed directly to Phase 4 report generation using user-provided information.


After Analysis

Once analysis-report.md is generated and reviewed, proceed to Gear 2 (Reverse Engineer) using the reverse-engineer skill.

The analysis report is Gear 1's output. Gear 2 reads it as input to generate comprehensive documentation.

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

04

需要参考平台分布和安装热度时

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

补充不同宿主或平台的使用分布数据

能力 5

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

Claude Code

28.62%
按下载量换算44

OpenCode

21.82%
按下载量换算34

Codex

15.96%
按下载量换算25

github-copilot

12.2%
按下载量换算19

Antigravity

6.68%
按下载量换算10

Gemini CLI

2.97%
按下载量换算5

安全审计

Gen Agent Trust Hub

通过

Socket

可疑

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/jschulte/claude-plugins --skill analyze;npx skills add jschulte/claude-plugins --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。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。

来源信息

继续浏览同类 Skills