apify-competitor-intelligence
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ChineseCompetitor Intelligence
竞品情报分析
Analyze competitors using Apify Actors to extract data from multiple platforms.
使用Apify Actors从多个平台提取数据，分析竞争对手。
Prerequisites
前提条件
(No need to check it upfront)
- file with
.envAPIFY_TOKEN - Node.js 20.6+ (for native support)
--env-file - CLI tool:
mcpcnpm install -g @apify/mcpc
（无需预先检查）
- 包含的
APIFY_TOKEN文件.env - Node.js 20.6+（支持原生）
--env-file - CLI工具：
mcpcnpm install -g @apify/mcpc
Workflow
工作流程
Copy this checklist and track progress:
Task Progress:
- [ ] Step 1: Identify competitor analysis type (select Actor)
- [ ] Step 2: Fetch Actor schema via mcpc
- [ ] Step 3: Ask user preferences (format, filename)
- [ ] Step 4: Run the analysis script
- [ ] Step 5: Summarize findings复制此清单并跟踪进度：
任务进度：
- [ ] 步骤1：确定竞品分析类型（选择Actor）
- [ ] 步骤2：通过mcpc获取Actor schema
- [ ] 步骤3：询问用户偏好（格式、文件名）
- [ ] 步骤4：运行分析脚本
- [ ] 步骤5：总结分析结果Step 1: Identify Competitor Analysis Type
步骤1：确定竞品分析类型
Select the appropriate Actor based on analysis needs:
| User Need | Actor ID | Best For |
|---|---|---|
| Competitor business data | | Location analysis |
| Competitor contact discovery | | Email extraction |
| Feature benchmarking | | Detailed business data |
| Competitor review analysis | | Review comparison |
| Hotel competitor data | | Hotel benchmarking |
| Hotel review comparison | | Review analysis |
| Competitor ad strategies | | Ad creative analysis |
| Competitor page metrics | | Page performance |
| Competitor content analysis | | Post strategies |
| Competitor reels performance | | Reels analysis |
| Competitor audience analysis | | Comment sentiment |
| Competitor event monitoring | | Event tracking |
| Competitor audience overlap | | Follower analysis |
| Competitor review benchmarking | | Review comparison |
| Competitor ad monitoring | | Ad discovery |
| Competitor profile metrics | | Profile analysis |
| Competitor content monitoring | | Post tracking |
| Competitor engagement analysis | | Comment analysis |
| Competitor reel performance | | Reel metrics |
| Competitor growth tracking | | Follower tracking |
| Comprehensive competitor data | | Full analysis |
| API-based competitor analysis | | API access |
| Competitor video analysis | | Video metrics |
| Competitor sentiment analysis | | Comment sentiment |
| Competitor channel metrics | | Channel analysis |
| TikTok competitor analysis | | TikTok data |
| Competitor video strategies | | Video analysis |
| Competitor TikTok profiles | | Profile data |
根据分析需求选择合适的Actor：
| 用户需求 | Actor ID | 最佳适用场景 |
|---|---|---|
| 竞品业务数据 | | 地理位置分析 |
| 竞品联系方式挖掘 | | 邮箱提取 |
| 功能对标 | | 详细业务数据 |
| 竞品评论分析 | | 评论对比 |
| 酒店竞品数据 | | 酒店对标 |
| 酒店评论对比 | | 评论分析 |
| 竞品广告策略 | | 广告创意分析 |
| 竞品主页指标 | | 主页表现 |
| 竞品内容分析 | | 帖子策略 |
| 竞品Reels表现 | | Reels分析 |
| 竞品受众分析 | | 评论情感分析 |
| 竞品活动监控 | | 活动追踪 |
| 竞品受众重叠分析 | | 粉丝分析 |
| 竞品评论对标 | | 评论对比 |
| 竞品广告监控 | | 广告挖掘 |
| 竞品主页指标 | | 主页分析 |
| 竞品内容监控 | | 帖子追踪 |
| 竞品互动分析 | | 评论分析 |
| 竞品Reel表现 | | Reel指标 |
| 竞品增长追踪 | | 粉丝追踪 |
| 全面竞品数据 | | 全维度分析 |
| 基于API的竞品分析 | | API访问 |
| 竞品视频分析 | | 视频指标 |
| 竞品情感分析 | | 评论情感分析 |
| 竞品频道指标 | | 频道分析 |
| TikTok竞品分析 | | TikTok数据 |
| 竞品视频策略 | | 视频分析 |
| 竞品TikTok主页 | | 主页数据 |
Step 2: Fetch Actor Schema
步骤2：获取Actor Schema
Fetch the Actor's input schema and details dynamically using mcpc:
bash
export $(grep APIFY_TOKEN .env | xargs) && mcpc --json mcp.apify.com --header "Authorization: Bearer $APIFY_TOKEN" tools-call fetch-actor-details actor:="ACTOR_ID" | jq -r ".content"Replace with the selected Actor (e.g., ).
ACTOR_IDcompass/crawler-google-placesThis returns:
- Actor description and README
- Required and optional input parameters
- Output fields (if available)
使用mcpc动态获取Actor的输入schema及详细信息：
bash
export $(grep APIFY_TOKEN .env | xargs) && mcpc --json mcp.apify.com --header "Authorization: Bearer $APIFY_TOKEN" tools-call fetch-actor-details actor:="ACTOR_ID" | jq -r ".content"将替换为所选Actor（例如）。
ACTOR_IDcompass/crawler-google-places此命令将返回：
- Actor描述及README
- 必填和可选输入参数
- 输出字段（若可用）
Step 3: Ask User Preferences
步骤3：询问用户偏好
Before running, ask:
- Output format:
- Quick answer - Display top few results in chat (no file saved)
- CSV - Full export with all fields
- JSON - Full export in JSON format
- Number of results: Based on character of use case
运行前，询问用户：
- 输出格式：
- 快速答复 - 在聊天中显示前几条结果（不保存文件）
- CSV - 导出包含所有字段的完整数据
- JSON - 以JSON格式导出完整数据
- 结果数量：根据使用场景的需求确定
Step 4: Run the Script
步骤4：运行脚本
Quick answer (display in chat, no file):
bash
node --env-file=.env ${CLAUDE_PLUGIN_ROOT}/reference/scripts/run_actor.js \
--actor "ACTOR_ID" \
--input 'JSON_INPUT'CSV:
bash
node --env-file=.env ${CLAUDE_PLUGIN_ROOT}/reference/scripts/run_actor.js \
--actor "ACTOR_ID" \
--input 'JSON_INPUT' \
--output YYYY-MM-DD_OUTPUT_FILE.csv \
--format csvJSON:
bash
node --env-file=.env ${CLAUDE_PLUGIN_ROOT}/reference/scripts/run_actor.js \
--actor "ACTOR_ID" \
--input 'JSON_INPUT' \
--output YYYY-MM-DD_OUTPUT_FILE.json \
--format json快速答复（在聊天中显示，不生成文件）：
bash
node --env-file=.env ${CLAUDE_PLUGIN_ROOT}/reference/scripts/run_actor.js \
--actor "ACTOR_ID" \
--input 'JSON_INPUT'CSV格式：
bash
node --env-file=.env ${CLAUDE_PLUGIN_ROOT}/reference/scripts/run_actor.js \
--actor "ACTOR_ID" \
--input 'JSON_INPUT' \
--output YYYY-MM-DD_OUTPUT_FILE.csv \
--format csvJSON格式：
bash
node --env-file=.env ${CLAUDE_PLUGIN_ROOT}/reference/scripts/run_actor.js \
--actor "ACTOR_ID" \
--input 'JSON_INPUT' \
--output YYYY-MM-DD_OUTPUT_FILE.json \
--format jsonStep 5: Summarize Findings
步骤5：总结分析结果
After completion, report:
- Number of competitors analyzed
- File location and name
- Key competitive insights
- Suggested next steps (deeper analysis, benchmarking)
完成后，报告以下内容：
- 分析的竞品数量
- 文件位置及名称
- 关键竞品洞察
- 建议后续步骤（深度分析、对标）
Error Handling
错误处理
APIFY_TOKEN not found.envAPIFY_TOKEN=your_tokenmcpc not foundnpm install -g @apify/mcpcActor not foundRun FAILEDTimeout--timeoutAPIFY_TOKEN not foundAPIFY_TOKEN=your_token.envmcpc not foundnpm install -g @apify/mcpcActor not foundRun FAILEDTimeout--timeout