/funding-signal-monitor

name: funding-signal-monitor
version: 1.0.0
description: >
  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.
tags: [lead-generation]

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

pip3 install requests

2. Apify API Token (for Twitter/Reddit scrapers)

export APIFY_API_TOKEN="apify_api_YOUR_TOKEN_HERE"

Not required if you only want Web Search + HN results.

Usage

Phase 1: Configuration

Accept parameters from the user:

| Parameter | Required | Default | Description | |-----------|----------|---------|-------------| | target-stages | Yes | — | Comma-separated: "Series A, Series B, Series C" | | target-industries | No | all | Filter: "SaaS, AI, fintech, healthtech" | | min-amount | No | none | Minimum raise amount (e.g., "$5M") | | lookback-days | No | 7 | How far back to search | | output-path | No | stdout | Where to save the markdown report |

Phase 2: Multi-Source Search

Run these searches in parallel to maximize coverage:

A) Web Search (WebSearch tool)

Run 4-6 queries using the WebSearch tool. Vary the phrasing to catch different announcement styles:

• `"Series A announced this week 2026"`
• `"Series B funding round 2026"`
• `"startup raised Series A"`
• `"seed funding announcement startup"`
• `"[industry] startup funding"` (if industry filter specified)
• `"raised $" AND "Series" AND "2026"`

For each result, extract:

• Company name
• Amount raised
• Stage (Seed, A, B, C, etc.)
• Date of announcement
• Lead investors

B) Twitter Search (twitter-mention-tracker)

python3 skills/twitter-mention-tracker/scripts/search_twitter.py \
  --query "\"excited to announce\" AND (\"raised\" OR \"Series A\" OR \"Series B\" OR \"funding\")" \
  --since <7-days-ago> --until <today> --max-tweets 50 --output json

Funding announcements often break on Twitter first. Founders post "excited to announce" or "thrilled to share" when rounds close.

C) Hacker News (funding-signal-monitor helper script)

python3 skills/funding-signal-monitor/scripts/search_funding.py \
  --stages "Series A,Series B" --days 7 --min-points 5 --output json

Or use the hacker-news-scraper directly:

python3 skills/hacker-news-scraper/scripts/search_hn.py \
  --query "raised funding Series" --days 7 --output json

D) Reddit Search (reddit-post-finder)

python3 skills/reddit-post-finder/scripts/search_reddit.py \
  --subreddit "startups,SaaS,technology" \
  --keywords "raised,Series A,Series B,funding round" \
  --days 7 --sort hot --output json

Phase 3: Consolidation & Qualification

After collecting results from all sources:

1. **Deduplicate** across sources. Same company appearing in multiple sources = higher confidence signal.

2. **For each company, assess:**

| Criterion | How to Evaluate | |-----------|----------------| | Stage | Seed, A, B, C, or later — must match target-stages | | Amount raised | Parse from announcement — filter by min-amount if specified | | Industry | Infer from company description — filter if target-industries specified | | Cloud likelihood | Tech/SaaS/AI companies = high; traditional industries = lower | | Team size estimate | Series A = 10-30, Series B = 30-100, Series C = 100-300 | | Recency | More recent = more urgent buying window |

3. **Score each company:**

• +3 points: Appears in multiple sources
• +2 points: Stage matches target exactly
• +2 points: Industry matches target
• +1 point: High cloud likelihood (tech/SaaS/AI)
• +1 point: Announced within last 3 days
• -1 point: Stage is outside target range
• -2 points: Non-tech industry (unless specifically targeted)

4. **Rank** by score descending.

Phase 4: Output

Produce a ranked report with the following columns:

| Column | Description | |--------|-------------| | Rank | Score-based ranking | | Company | Company name | | Amount | Amount raised | | Stage | Funding stage | | Date | Announcement date | | Investors | Lead investors | | Industry | Company's industry/vertical | | Source(s) | Where the signal was found (web, Twitter, HN, Reddit) | | Cloud Likelihood | High / Medium / Low | | Outreach Angle | Suggested approach based on stage and industry |

**Outreach angle templates:**

• **"Scale fast with fresh capital"** — Best for Series A. They're building the team and need tools to move fast before the money runs out.
• **"Operationalize before the next round"** — Best for Series B. They need to professionalize processes before Series C diligence.
• **"Enterprise-ready at scale"** — Best for Series C. They're going upmarket and need enterprise-grade tooling.

Save to the specified output path as markdown, or print to stdou