Agent skill

funding-signal-monitor

Monitor web sources for Series A-C funding announcements. Aggregates signals from TechCrunch, Crunchbase (via web search), Twitter, Hacker News, and LinkedIn. Filters by stage, amount, and industry. Returns qualified recently-funded companies ready for outreach.

gooseworks-aigithub.com/gooseworks-aiGitHub ↗
claude-codecodexcursorships scriptsMIT
Install
npx skills add gooseworks-ai/goose-skills --skill funding-signal-monitor --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 3
SKILL.md size: 9 KB
Bundled scripts: yes
Version: 1.0.0
Path: skills/monitoring/composites/funding-signal-monitor/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 1,091
Language: Python

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# Funding Signal Monitor Detect recently-funded startups as buying signals. When a company raises a round, they have fresh capital, aggressive growth plans, and urgent needs for tools and services. This skill finds those companies across multiple sources, qualifies them, and outputs a ranked list ready for outreach. ## Why This Works When a company announces funding, they've: - Received capital earmarked for growth (hiring, tooling, infrastructure) - Committed to investors on aggressive milestones - Entered a 12-18 month sprint to hit next-stage metrics - Begun evaluating vendors immediately (the "post-raise buying window" is 1-3 months) Series A-C companies are the sweet spot: enough money to buy, small enough to move fast. ## Cost | Component | Cost | |-----------|------| | Web Search (WebSearch tool) | Free | | Hacker News (Algolia API) | Free | | Twitter scraper (Apify) | ~$0.05-0.10 per run | | Reddit scraper (Apify) | ~$0.05-0.10 per run | **Typical run:** $0.10-0.20 total. Web Search + HN are free and provide the bulk of results. ## Setup ### 1. Dependencies ```bash pip3 install requests ``` ### 2. Apify API Token (for Twitter/Reddit scrapers) ```bash export APIFY_API_TOKEN=

What's inside
Steps it walks through
  1. Why This Works
  2. Cost
  3. Setup
  4. 1. Dependencies
  5. 2. Apify API Token (for Twitter/Reddit scrapers)
  6. Usage
  7. Phase 1: Configuration
  8. Phase 2: Multi-Source Search
  9. Phase 3: Consolidation & Qualification
  10. Phase 4: Output
  11. Helper Script
  12. AI Agent Integration
  13. Tips
  14. Troubleshooting
Ships with 2 files
  • scripts/search_funding.py
  • skill.meta.json
Commands it runs
pip3 install requests
export APIFY_API_TOKEN="apify_api_YOUR_TOKEN_HERE"
python3 skills/twitter-mention-tracker/scripts/search_twitter.py \
python3 skills/funding-signal-monitor/scripts/search_funding.py \
python3 skills/hacker-news-scraper/scripts/search_hn.py \
python3 skills/reddit-post-finder/scripts/search_reddit.py \
Search HN for Series A and B announcements in last 7 days
Filter to high-engagement posts only
Search all stages with industry keyword
More from goose-skills
All skills →
About this skill
What does the funding-signal-monitor skill do?

Monitor web sources for Series A-C funding announcements. Aggregates signals from TechCrunch, Crunchbase (via web search), Twitter, Hacker News, and LinkedIn. Filters by stage, amount, and industry. Returns qualified recently-funded companies ready for outreach.

How do I install it?

Run `npx skills add gooseworks-ai/goose-skills --skill funding-signal-monitor --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From gooseworks-ai/goose-skills, a repository with 1,091 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

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