使用方法
- 复制安装命令，在自己的智能体环境安装前先检查源码包。
- 从原始文档核对所需工具、凭据和支持的宿主。
- 说明输入材料与期望交付物，并对照来源检查输出。
npx skills add https://github.com/apidojo-io/apidojo-skills --skill monitoring-tech-acquisition-news-google开始前需了解
- 安装不包含模型订阅、API 额度或访问文件的权限。
- 仓库星数和安装量反映活跃情况，不等于独立质量或安全测试。
仓库原始内容
按源仓库内容呈现，保留标题、案例、代码、表格、链接以及原文引用的演示图片。
Monitoring Tech Acquisition News Google
Executes monitoring tech acquisition news google using apidojo scrapers. Part of the apidojo intelligence skills library.
Prerequisites
APIFY_TOKENenvironment variable set- Optional: Apify MCP server installed
Inputs
| Parameter | Type | Required | Default | Notes |
|---|---|---|---|---|
startUrls | array | Optional | [] | Google search URLs |
searchTerms | array | Optional | [] | Keywords to search on Google |
countryCode | string | Optional | US | Country for Google search (e.g. US, GB, TR) |
languageCode | string | Optional | — | Language for results (e.g. en) |
maxItems | number | Optional | Unlimited | Maximum results to return across all queries |
maxPagesPerQuery | integer | Optional | 1 | Maximum result pages per query |
mobileResults | boolean | Optional | false | Fetch mobile SERP layout |
customMapFunction | string | Optional | — | JavaScript function to transform each output object |
Workflow
Progress:
- [ ] Step 1: Define parameters
- [ ] Step 2: Run google-search-scraper
- [ ] Step 3: Filter and classify results
- [ ] Step 4: Score by quality and relevance
- [ ] Step 5: Deliver outputStep 2: Run the Actor
Recommended — run_actor.js (handles waiting, output, and file saving automatically):
bash
# Quick answer (prints table to chat)
node scripts/run_actor.js \
--actor "apidojo~google-search-scraper" \
--input '{"param": "value"}'
# Save as CSV
node scripts/run_actor.js \
--actor "apidojo~google-search-scraper" \
--input '{"param": "value"}' \
--output YYYY-MM-DD_results.csv --format csv
# Save as JSON
node scripts/run_actor.js \
--actor "apidojo~google-search-scraper" \
--input '{"param": "value"}' \
--output YYYY-MM-DD_results.json --format jsonAPIFY_TOKENmust be set in environment or.envfile.
If Apify MCP is available:
Tool: apify:run-actor
Actor: "apidojo~google-search-scraper"
Input:
{
"searchTerms": ["[SECTOR] acquisition 2026", "[SECTOR] acquired", "[COMPANY] acquires"],
"maxItems": 100
}REST API fallback:
bash
curl -X POST \
"https://api.apify.com/v2/acts/apidojo~google-search-scraper/runs?token=$APIFY_TOKEN" \
-H "Content-Type: application/json" \
-d '{"searchTerms": ["[SECTOR] acquisition 2026", "[SECTOR] acquired", "[COMPANY] acquires"], "maxItems": 100}'Wait for SUCCEEDED. Fetch dataset:
bash
curl "https://api.apify.com/v2/actor-runs/$RUN_ID/dataset/items?token=$APIFY_TOKEN"Step 3: Classify Results
classification: CONFIRMED (official announcement) | REPORTED (press rumor) | RUMORED (speculation) | DENIEDStep 4: Score Each Result
score = deal_significance = (named_price ? deal_value/1000000 : 0.5) * 0.50 + (acquirer_is_public ? 1 : 0.5) * 0.30 + (coverage_from_tier1_pub ? 1 : 0.5) * 0.20Step 5: Edge Cases
- M&A news often breaks before official press releases; use Google News search type to catch breaking reports, and flag STATUS = UNCONFIRMED until official press release found
Additional fallbacks:
- < 20 results: Broaden search terms; remove secondary filters
- No results: Verify the search terms are correct; try alternate phrasings
- Data quality issues: Remove entries with missing key fields; note count in output
Output Format
# Monitoring Tech Acquisition News Google
Results: [N] | Date: [DATE]
| # | [Key Field] | [Metric 1] | [Metric 2] | [Classification] | [Score] |
|---|------------|-----------|-----------|-----------------|---------|
| 1 | [value] | [value] | [value] | [type] | [0.XX] |
## Summary
Top result: [description]
Key finding: [insight]Troubleshooting
Too few results: Broaden the primary search term; remove restrictive filters. Low quality results: Apply minimum score threshold (≥ 0.50) to filter noise. Actor fails to run: Verify API key; check actor status at apify.com/apidojo.
