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signal-scanner

Skill9 starsChanged 3 months agoMITView on GitHub

Use when looking for companies showing buying signals in your ICP's domain, when needing to find new target accounts, or when running periodic signal detection scans

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SKILL.md

Signal Scanner

Signal Sources

ScannerScriptLookback
LinkedIn activityscripts.linkedin_activity48 hours
GitHub reposscripts.github_rl_scanner7 days
ArXiv papersscripts.arxiv_monitor7 days
HuggingFace modelsscripts.hf_model_monitor7 days
Job postingsscripts.job_posting_scanner7 days
Funding roundsscripts.funding_tracker30 days

The LinkedIn activity scanner is the highest-ROI signal source. Checking for 48-hour LinkedIn activity alone doubles response rates. Run this scanner with data from Sales Navigator, Phantom Buster, or manual LinkedIn research.

Steps

1. Validate API keys

python3 -c "from scripts.config import Config; Config.load()"

Fix any missing keys before proceeding. GITHUB_TOKEN is required; others are optional but improve coverage.

2. Run scanners

LinkedIn activity scanner accepts pre-collected data (Sales Navigator export, Phantom Buster, or manual research):

from scripts.linkedin_activity import LinkedInActivityScanner

scanner = LinkedInActivityScanner(max_age_hours=48)
result = scanner.scan_from_data(activity_data)  # list of activity dicts

Each activity dict requires: name, company, activity_type (posted/commented/liked/shared), topic, timestamp (ISO format).

python3 -m scripts.github_rl_scanner --lookback-days 7 --output /tmp/github_signals.json
python3 -m scripts.arxiv_monitor --lookback-days 7 --output /tmp/arxiv_signals.json
python3 -m scripts.hf_model_monitor --lookback-days 7 --output /tmp/hf_signals.json
python3 -m scripts.job_posting_scanner --lookback-days 7 --output /tmp/job_signals.json
python3 -m scripts.funding_tracker --lookback-days 30 --output /tmp/funding_signals.json

3. Stack signals

python3 -m scripts.signal_stacker \
  --inputs /tmp/linkedin_signals.json /tmp/github_signals.json /tmp/arxiv_signals.json \
           /tmp/hf_signals.json /tmp/job_signals.json /tmp/funding_signals.json \
  --output /tmp/stacked.json

4. Present results

Show a summary table:

Company | ICP Tier | Domain Maturity | Signals (sources) | Composite Score | Grade

Sort by composite score descending. Group by grade (A → B → C → skip D).

5. Recommend next action

  • Grade A accounts: "Run prospect-researcher on these accounts before outreach."
  • Grade B accounts: "Ready for contact-finder + email-writer."
  • Grade C accounts: "Log for 30-day nurture revisit."
  • Grade D: skip, log with reason.

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