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Install in OpenClaw
/install agent-compete-scope
Description
경쟁사 포지셔닝 분석 및 화이트스페이스 도출 에이전트
README (SKILL.md)
🔍 CompeteScope Agent
내 제품과 경쟁사들을 비교 분석하여 시장의 빈틈(Whitespace)과 차별화 전략을 제안합니다.
Features
- 경쟁사 프로필: 웹 검색을 통한 경쟁사 상세 프로필 자동 생성
- 비교 매트릭스: 기능, 가격, 타겟 등 다차원 비교표 생성
- 전략 도출: AI가 분석한 시장 기회 및 추천 전략 제공
Usage
ACP Job Payload:
{
"my_product": "AI 마케팅 툴",
"competitors": ["Competitor A", "Competitor B"]
}
Usage Guidance
This skill implements competitor analysis via web search and LLM calls, which fits its description, but the package metadata omits important details. Before installing or running: 1) Do not supply API keys until you confirm which provider you'll use (default is GROQ via GROQ_API_KEY; Tavily is used for web search via TAVILY_API_KEY). 2) Expect outbound network calls to api.tavily.com, generativelanguage.googleapis.com (if Gemini/Google selected), api.groq.com, and Anthropic endpoints; review those endpoints and be comfortable with sending scraped content and competitor names there. 3) The repository contains package.json and package-lock — run npm install/build in an isolated environment (or review code) before executing. 4) The SKILL.md and registry metadata don't list required env vars; ask the publisher to update metadata to list required secrets and an install guide. 5) If you must test, run with mock mode (TAVILY_API_KEY unset) and without real secrets, and inspect network calls/logs. If you are not comfortable exposing API keys or data to third-party LLM/search services, do not install/use this skill.
Capability Analysis
Type: OpenClaw Skill
Name: agent-compete-scope
Version: 1.0.2
The CompeteScope bundle is a legitimate competitor analysis tool that uses the Tavily API for web searching and various LLM providers (Groq, Anthropic, Gemini) for data synthesis. The code logic in src/analyzer.ts and src/fetcher.ts is well-structured, follows the stated purpose in SKILL.md, and contains no evidence of data exfiltration, malicious execution, or prompt injection. All external network calls are directed to official API endpoints (api.tavily.com, api.groq.com, etc.), and dependencies in package.json are standard industry libraries.
Capability Assessment
Purpose & Capability
The skill's stated purpose (competitor positioning and whitespace analysis) is consistent with the code (web searching + LLM analysis). However the registry metadata declares no required environment variables or binaries while the code and README require API keys (TAVILY_API_KEY, GROQ_API_KEY, and optionally ANTHROPIC_API_KEY or GOOGLE_API_KEY). This mismatch is incoherent and could lead users to install/run the skill without realizing it will use external services and secrets.
Instruction Scope
SKILL.md shows only the job payload and feature description and does not mention loading .env or external APIs, yet the runtime code (fetcher + LLM adapters) reads .env and sends data to external endpoints (api.tavily.com, generativelanguage.googleapis.com, api.groq.com, Anthropic SDK). Sending scraped content and competitor names to external LLM/search APIs is within the stated purpose but the instructions omit these data flows and do not warn about secrets or outbound network calls.
Install Mechanism
There is no install spec in the registry, but the package.json/package-lock are included and declare npm dependencies (@anthropic-ai/sdk, openai, dotenv, etc.). The dependency sources are standard npm packages (no obscure download URLs), so installation risk is typical for an npm project — but the absence of an install spec in registry metadata is an inconsistency that should be clarified (the project requires npm install/build to run).
Credentials
The code expects multiple API keys (GROQ_API_KEY by default via LLM_FACTORY, plus optional ANTHROPIC_API_KEY or GOOGLE_API_KEY and TAVILY_API_KEY for web search). The registry declares no required env vars or primary credential. While these keys are logically related to the skill's function, the omission from metadata makes the credential requests non-transparent. Also multiple provider keys increase the secret surface; confirm you only provide the keys you intend this skill to use.
Persistence & Privilege
The skill does not request always:true, does not modify other skills or system-wide configs, and has no install spec that writes unexpected files. It will load a local .env and may create network traffic, but it does not ask for elevated system privileges. Autonomous invocation is allowed (platform default) but not combined with 'always: true'.
How to Use
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install agent-compete-scope - After installation, invoke the skill by name or use
/agent-compete-scope - Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.2
- Updated package.json only; no changes to features or documentation.
- No functional changes in this version.
v1.0.1
- Updated package.json with version 1.0.1.
- No changes to functionality or documentation content.
v1.0.0
Initial release of CompeteScope Agent.
- 경쟁사 프로필 자동 생성 (웹 검색 기반)
- 제품별 기능, 가격, 타겟 등 비교 매트릭스 생성
- 시장의 빈틈(Whitespace)과 차별화 전략 제안
- 사용 예시 및 ACP Job Payload 가이드 추가
Metadata
Frequently Asked Questions
What is Agent Compete Scope?
경쟁사 포지셔닝 분석 및 화이트스페이스 도출 에이전트. It is an AI Agent Skill for Claude Code / OpenClaw, with 299 downloads so far.
How do I install Agent Compete Scope?
Run "/install agent-compete-scope" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is Agent Compete Scope free?
Yes, Agent Compete Scope is completely free (open-source). You can download, install and use it at no cost.
Which platforms does Agent Compete Scope support?
Agent Compete Scope is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).
Who created Agent Compete Scope?
It is built and maintained by jinu4you (@jinu4you); the current version is v1.0.2.
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