Token导航 LogoToken导航

Agent Skill · 研究检索

competitor-finder-adarsh(竞争对手发现者阿达什)

competitor-finder-adarsh 用于查找、检索和筛选相关信息,适合在 OpenClaw 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-finder-adarsh 是研究检索类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为接触敏感数据;许可证为 MIT-0,第三方扫描有提醒项,按安装量在研究检索类的 73,674 个 Skill 里排第 13,153。

总安装 9,643周安装 410GitHub Stars 公开资料未说明下载 3,378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
MIT-0
版本
1.0.1

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-finder-adarsh(竞争对手发现者阿达什)
来源仓库:https://github.com/adarshvmore/competitor-finder-adarsh
安装命令:
openclaw skills install competitor-finder-adarsh
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 OpenClaw 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。

ClawHubOpenClaw
openclaw skills install competitor-finder-adarsh

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。

通过查询 SerpAPI,然后查询 DataForSEO,最后查询 OpenAI(如果需要)返回名称、网站和原因,为某个品牌找到 3-5 个竞争对手。

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 OpenClaw;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 openclaw skills install competitor-finder-adarsh;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

competitor-finder-adarsh 怎么安装?

在终端执行 openclaw skills install competitor-finder-adarsh。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

competitor-finder-adarsh 支持哪些 AI 工具?

公开资料标注支持 OpenClaw。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-finder-adarsh 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。

competitor-finder-adarsh 是免费的吗?

来源标注的许可证是 MIT-0。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

competitor-finder-adarsh 用的人多吗?

公开数据是累计安装 9,643 次,近一周安装 410 次,下载 3,378 次。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-finder-adarsh 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

  • 专注 OpenClaw 一种宿主,触发方式和使用习惯不用来回切换。
  • 许可证明确为 MIT-0,商用和分发边界好判断。

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。
  • 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 OpenClaw,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-finder-adarsh

竞争对手发现者阿达什 · 本页

9,643—接触敏感数据MIT-0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 Finder Skill

Purpose

Identifies 3-5 competitors for a given brand by searching the web via SerpAPI and, as a last resort, falling back to a minimal OpenAI call. Returns competitor names, websites, and optionally the reason they are considered competitors. This collector feeds into the Marketing Audit Pipeline to populate the Competitor Landscape section of the final report.

Input Schema

// Function signature
collectCompetitors(brandName: string, domain?: string): Promise<CompetitorData>

// brandName: The brand name to find competitors for (e.g. "Gymshark")
// domain: Optional domain for additional context (e.g. "gymshark.com").
// Helps refine competitor search and filter out the brand itself from results.

Output Schema

interface CompetitorData {
 competitors: CompetitorEntry[]; // 3-5 competitor entries
 error?: string; // Present only when collector fails
}

interface CompetitorEntry {
 name: string; // e.g. "Nike"
 website: string; // e.g. "nike.com"
 reason?: string; // e.g. "Direct competitor in activewear market"
}

API Dependencies

Primary: SerpAPI

  • API Name: SerpAPI (Google Search)
  • Endpoint: https://serpapi.com/search.json
  • Auth: SERPAPI_KEY environment variable
  • Cost estimate: ~$0.005 per search
  • Rate limits: Depends on plan; free tier allows 100 searches/month

Secondary: DataForSEO

  • API Name: DataForSEO Competitor Domain API
  • Endpoint: https://api.dataforseo.com/v3/dataforseo_labs/google/competitors_domain/live
  • Auth: DATAFORSEO_LOGIN + DATAFORSEO_PASSWORD environment variables
  • Cost estimate: ~$0.01 per request
  • Rate limits: Depends on plan; free tier allows 100 requests/month

Fallback: OpenAI (minimal call)

  • API Name: OpenAI API
  • Model: gpt-4.1-mini
  • Auth: OPENAI_API_KEY environment variable
  • Cost estimate: ~$0.001 per call (minimal prompt)
  • Usage: Only used when both SerpAPI and DataForSEO fail or return no results

Implementation Pattern

Data Flow

  1. Receive brandName and optional domain from the pipeline
  2. Attempt Method 1: SerpAPI search
  3. If Method 1 fails or returns insufficient results, attempt Method 2: DataForSEO
  4. If both fail, attempt Method 3: OpenAI fallback (minimal prompt)
  5. Deduplicate and filter results (remove the brand itself)
  6. Return 3-5 competitors mapped to CompetitorData

Method 1: SerpAPI Search

// Query: "top competitors of {brandName}"
{
 api_key: process.env.SERPAPI_KEY,
 engine: "google",
 q: `top competitors of ${brandName}`,
 num: 10
}
  • Parse organic results to extract competitor brand names and domains
  • Look for listicle-style results ("Top 10 Gymshark competitors...")
  • Extract domain names from result URLs
  • Filter out non-competitor results (news articles, the brand's own site)

Method 2: DataForSEO Competitor Domain

[{
 target: domain, // e.g. "gymshark.com"
 language_code: "en",
 location_code: 2840, // United States
 limit: 5
}]
  • Returns domains that compete for the same keywords
  • More accurate than SERP search but requires the domain parameter

Method 3: OpenAI Fallback (Minimal)

// ONLY used when Methods 1 and 2 both fail
// This is a MINIMAL prompt -- keep token usage as low as possible
const response = await openai.chat.completions.create({
 model: 'gpt-4.1-mini',
 max_tokens: 200,
 temperature: 0.3,
 messages: [
 {
 role: 'system',
 content: 'You are a marketing analyst. Return only a JSON array of competitor objects.'
 },
 {
 role: 'user',
 content: `List 5 direct competitors of "${brandName}"${domain ? ` (${domain})` : ''}. Return JSON: [{"name":"...","website":"...","reason":"..."}]`
 }
 ]
});
  • Parse the JSON response
  • This call costs ~$0.001 and should only happen when SERP/DataForSEO APIs are unavailable
  • Log a warning when this fallback is used so it can be monitored

Result Filtering

  • Remove entries where the name or website matches the input brand
  • Deduplicate by website domain (normalize: strip www, trailing slashes)
  • Ensure each entry has both name and website populated
  • Limit to 5 results maximum; aim for at least 3

Error Handling

  • Entire function wrapped in try/catch
  • On failure of all three methods, return EMPTY_COMPETITOR_DATA with error field set:
 return { ...EMPTY_COMPETITOR_DATA, error: 'Competitor data unavailable: <reason>' };
  • Never throw -- always return a valid CompetitorData object
  • Log errors with Winston logger including brandName and method that failed:
 logger.error('Competitor collector failed', { brandName, domain, method, err });
  • Log warnings when falling back to secondary/tertiary methods:
 logger.warn('Competitor finder: SerpAPI failed, falling back to DataForSEO', { brandName });
 logger.warn('Competitor finder: DataForSEO failed, falling back to OpenAI', { brandName });
  • Common failure scenarios:

- SerpAPI key invalid or quota exhausted - DataForSEO credentials invalid or out of credits - OpenAI API key invalid - No competitors found for niche or unknown brand - Network timeout on any API

Example Usage

import { collectCompetitors } from '../collectors/competitorCollector';

// Successful collection (via SerpAPI)
const data = await collectCompetitors('Gymshark', 'gymshark.com');
// Returns:
// {
// competitors: [
// { name: "Nike", website: "nike.com", reason: "Global leader in athletic apparel" },
// { name: "Lululemon", website: "lululemon.com", reason: "Premium activewear competitor" },
// { name: "Under Armour", website: "underarmour.com", reason: "Direct competitor in gym wear" },
// { name: "Alphalete", website: "alphalete.com", reason: "DTC fitness apparel brand" },
// { name: "Fabletics", website: "fabletics.com", reason: "Subscription-based activewear" },
// ],
// }

// Partial result (only OpenAI fallback worked)
const partial = await collectCompetitors('ObscureBrand');
// Returns:
// {
// competitors: [
// { name: "CompetitorA", website: "competitora.com", reason: "Similar product category" },
// { name: "CompetitorB", website: "competitorb.com", reason: "Same target market" },
// { name: "CompetitorC", website: "competitorc.com" },
// ],
// }

// Failed collection (graceful degradation)
const failedData = await collectCompetitors('UnknownBrand');
// Returns:
// {
// competitors: [],
// error: "Competitor data unavailable: All methods failed"
// }

Notes

  • This collector uses a three-tier fallback strategy to maximize data availability. SerpAPI is preferred because it provides real SERP data. DataForSEO provides keyword-overlap-based competitors. OpenAI is a last resort.
  • The OpenAI fallback is the ONLY place outside of reportGenerator.ts where an AI model call is permitted. It must be minimal (max 200 tokens) and should be logged as a warning for cost monitoring.
  • When the input type is 'instagram' (no domain available), skip Method 2 (DataForSEO requires a domain) and rely on Methods 1 and 3.
  • The EMPTY_COMPETITOR_DATA constant is defined in src/types/audit.types.ts and should be imported for fallback returns.
  • Competitor data is inherently subjective. The report generator (GPT-4.1-mini) will contextualize the raw competitor list into strategic analysis.
  • This collector must never block the pipeline. Even a complete failure returns valid typed data with an error flag.

平台分布

OpenClaw

72.8%
按下载量换算2,459

安全审计

VirusTotal

通过

ClawScan

可疑

Static analysis

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。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。

继续浏览同类 Skills