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Agent Skill · 研究检索

competitor-analysis(竞争对手分析)

competitor-analysis 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。

competitor-analysis 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,第三方扫描有提醒项,按安装量在研究检索类的 73,675 个 Skill 里排第 59,803。

总安装 376周安装 16GitHub Stars 191下载 132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:competitor-analysis(竞争对手分析)
来源仓库:https://github.com/wcygan/dotfiles
仓库路径:skills/competitor-analysis
安装命令:
npx skills add https://github.com/wcygan/dotfiles --skill competitor-analysis
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/wcygan/dotfiles --skill competitor-analysis

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。

competitor-analysis 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。适用于关键词搜索、任务场景匹配或来源线索筛选等研究检索场景。

通过 npx skills add 命令从 GitHub 仓库安装,具体用法可参考原始 README。安装前需确认权限范围和维护状态,注意是否触发联网、命令执行或文件读写操作。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。

来源仓库内路径:skills/competitor-analysis

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/wcygan/dotfiles --skill competitor-analysis;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

competitor-analysis 怎么安装?

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

competitor-analysis 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。

competitor-analysis 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/wcygan/dotfiles --skill competitor-analysis 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。

competitor-analysis 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。

competitor-analysis 用的人多吗?

公开数据是累计安装 376 次,近一周安装 16 次,下载 132 次,GitHub 191 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。

competitor-analysis 安全吗?

第三方扫描有提醒项,说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

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。

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。
  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 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
competitor-analysis

竞争对手分析 · 本页

376191执行命令未明确有提醒

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

Competitor Analysis

Orchestrate 7 research agents to produce a comprehensive competitor teardown. Phase 1 runs 4 parallel research agents (Product, Marketing, UX, Technical). Phase 2 runs 3 sequential synthesis agents (MVP Spec, GTM Strategy, Competitive Landscape) that build on Phase 1 findings.

Workflow

1. Parse Input

Target: $ARGUMENTS

If the target above is non-empty, use it immediately — do NOT ask the user to confirm or re-provide it. Parse it as follows:

  • URL (starts with http): use as-is for WebFetch, extract company name from domain
  • Company name (no URL): construct likely URLs (https://{name}.com, https://www.{name}.com)

If the target above is empty, ask the user what competitor to analyze and wait for their response.

Store the parsed values:

  • COMPANY_NAME: Human-readable name (e.g., "Linear")
  • PRIMARY_URL: Main product URL (e.g., "https://linear.app")

IMPORTANT: When a target is provided, begin Phase 2 immediately after parsing. Do not pause for user input.

2. Phase 1 — Parallel Research Agents

Spawn 4 agents in parallel using the Task tool. Each agent is general-purpose (needs WebSearch + WebFetch). Run all 4 with run_in_background: true for maximum parallelism.

Read REFERENCE.md first to get the detailed research checklists and output templates for each agent.

Agent 1: Product Overview
subagent_type: general-purpose
run_in_background: true

Prompt:

You are a product research analyst. Research {COMPANY_NAME} ({PRIMARY_URL}) and produce
a comprehensive product overview.

Follow the "Product Overview Agent" template in the reference below. Use WebSearch and
WebFetch to gather information. Cite sources for every claim.

{paste Product Overview section from REFERENCE.md}
Agent 2: Marketing Analysis
subagent_type: general-purpose
run_in_background: true

Prompt:

You are a marketing strategist. Research {COMPANY_NAME}'s marketing and positioning.

Follow the "Marketing Analysis Agent" template in the reference below. Use WebSearch and
WebFetch to analyze their marketing channels, messaging, and content strategy.

{paste Marketing Analysis section from REFERENCE.md}
Agent 3: UX Analysis
subagent_type: general-purpose
run_in_background: true

Prompt:

You are a UX researcher. Analyze the user experience of {COMPANY_NAME} ({PRIMARY_URL}).

Follow the "UX Analysis Agent" template in the reference below. Use WebFetch to walk
through their signup flow, onboarding, and core product experience.

{paste UX Analysis section from REFERENCE.md}
Agent 4: Technical Stack
subagent_type: general-purpose
run_in_background: true

Prompt:

You are a technical researcher. Investigate the technology stack behind {COMPANY_NAME}.

Follow the "Technical Stack Agent" template in the reference below. Use WebSearch and
WebFetch to analyze their tech choices, APIs, architecture signals, and engineering culture.

{paste Technical Stack section from REFERENCE.md}

3. Collect Phase 1 Results

Wait for all 4 background agents to complete. Read their output files to collect results.

Compile a Phase 1 Summary containing the key findings from each agent. This summary feeds into Phase 2 agents.

4. Phase 2 — Sequential Synthesis Agents

Phase 2 agents run sequentially because each builds on prior results. These are NOT background agents — wait for each to complete before spawning the next.

Read REFERENCE.md for detailed templates.

Agent 5: MVP Specification
subagent_type: general-purpose

Prompt:

You are a product strategist. Based on the competitor research below, define an MVP
specification for a product that competes with {COMPANY_NAME}.

## Phase 1 Research Findings
{paste compiled Phase 1 findings}

Follow the "MVP Specification Agent" template in the reference below.

{paste MVP Specification section from REFERENCE.md}
Agent 6: Go-to-Market Strategy
subagent_type: general-purpose

Prompt:

You are a go-to-market strategist. Based on the competitor research and MVP spec below,
design a go-to-market strategy for competing with {COMPANY_NAME}.

## Phase 1 Research Findings
{paste compiled Phase 1 findings}

## MVP Specification
{paste Agent 5 output}

Follow the "Go-to-Market Strategy Agent" template in the reference below.

{paste GTM Strategy section from REFERENCE.md}
Agent 7: Competitive Landscape
subagent_type: general-purpose

Prompt:

You are a market analyst. Based on all prior research, map the competitive landscape
around {COMPANY_NAME} and identify differentiation opportunities.

## Phase 1 Research Findings
{paste compiled Phase 1 findings}

## MVP Specification
{paste Agent 5 output}

## Go-to-Market Strategy
{paste Agent 6 output}

Follow the "Competitive Landscape Agent" template in the reference below.

{paste Competitive Landscape section from REFERENCE.md}

5. Final Synthesis

Combine all 7 agent outputs into a single report. Present to the user with this structure:

# Competitor Teardown: {COMPANY_NAME}

## Executive Summary
[3-5 bullet points: what they do, how they win, where they're vulnerable]

## Table of Contents
1. Product Overview
2. Marketing Analysis
3. UX Analysis
4. Technical Stack
5. MVP Specification
6. Go-to-Market Strategy
7. Competitive Landscape

---

[Agent 1 output — Product Overview]

---

[Agent 2 output — Marketing Analysis]

---

[Agent 3 output — UX Analysis]

---

[Agent 4 output — Technical Stack]

---

[Agent 5 output — MVP Specification]

---

[Agent 6 output — Go-to-Market Strategy]

---

[Agent 7 output — Competitive Landscape]

---

## Key Takeaways

### Top 3 Opportunities
1. [Biggest gap or underserved segment]
2. [Second opportunity]
3. [Third opportunity]

### Top 3 Risks
1. [Biggest risk in competing]
2. [Second risk]
3. [Third risk]

### Recommended Next Steps
1. [Most important action]
2. [Second action]
3. [Third action]

Example Invocations

/competitor-analysis https://linear.app
/competitor-analysis Notion
/competitor-analysis https://www.figma.com
/competitor-analysis Vercel

Anti-Patterns

  • Don't skip Phase 1 before Phase 2: Synthesis agents need research findings to produce useful output. Never run Phase 2 agents without passing them Phase 1 results.
  • Don't use Explore agents: Sub-agents need WebSearch and WebFetch for external research. Use general-purpose only.
  • Don't collapse agents: Each agent has a distinct research lens. Combining them loses depth.
  • Don't fabricate data: If an agent can't find information (e.g., pricing not public), it should say so explicitly rather than guessing.
  • Don't skip citations: Every factual claim must reference a source URL or page.
  • Don't run Phase 2 in parallel: Agent 6 needs Agent 5's output, Agent 7 needs both.

Notes

  • Total runtime is typically 3-8 minutes depending on the target's web presence.
  • Phase 1 agents run in background for parallelism; Phase 2 agents run sequentially.
  • If a Phase 1 agent fails or returns thin results, note the gap in the final report rather than blocking Phase 2.
  • For private/stealth companies with minimal web presence, agents will produce thinner reports — this is expected.

平台分布

Codex

37.81%
按下载量换算50

Claude

31.41%
按下载量换算41

Cursor

18.24%
按下载量换算24

Gemini CLI

9.42%
按下载量换算12

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

可疑

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/wcygan/dotfiles --skill competitor-analysis 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。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

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