funding-signal-outreach

name	funding-signal-outreach
description	Across a list of companies, detect recent funding events, qualify them against the seller's context, find the right people (buyers/champions/users), and draft personalized outreach using fresh capital as the "why now" — the full chain from signal to outreach-ready. Composite that hands off to cold-email-outreach or linkedin-outreach.
metadata	{"version":"1.0.1","category":"outreach","type":"composite"}

Funding Signal Outreach

A composite: scripts detect funding evidence and find contacts; the agent qualifies, scores, and drafts (via email-drafting); then it hands off to a launch skill. Detection is keyless; only the send key gates an actual launch.

When to use

• "Check if any of these companies raised funding" / "reach out to recently funded companies."
• A company list + an upstream monitor surfaces funding to act on.

How it works (scripts + agent)

1. Detect — detect_signal.py --signal funding runs keyless searches per company and returns evidence snippets (+ optional page text). (agent) read the evidence, extract amount/stage/date/investors, and confirm recency against lookback; drop unconfirmed rounds rather than over-claim.
2. (agent) Qualify + rank each funded company against ICP and the round's "why now" relevance.
3. Find people — find_contacts.py (Apollo by domain + persona titles; --enrich --verify for emails). Degrades to email-pattern guesses (unverified) without Apollo.
4. (agent) Draft sequences with email-drafting using the funding event as the hook (Signal-Proof-Ask). Don't target the round itself — connect new capital to a concrete budget-now angle (hiring spree, new market, scaling pain).
5. Hand off to cold-email-outreach (lemlist/instantly) or linkedin-outreach; request launch approval before sending.

How to run

# 1 — detect funding evidence (keyless)
python3 ${SKILL_DIR}/scripts/detect_signal.py \
  --companies ${WORKSPACE}/companies.json --signal funding \
  --per-company 4 --extract --output ${WORKSPACE}/funding_evidence.json

# 3 — find + verify contacts at the companies the agent qualified
python3 ${SKILL_DIR}/scripts/find_contacts.py \
  --domains ${WORKSPACE}/qualified.json \
  --titles "VP Sales,Head of RevOps,COO" \
  --per-company 3 --enrich --verify --output ${WORKSPACE}/contacts.json

Then draft (email-drafting) and launch (cold-email-outreach / linkedin-outreach).

Outputs

• funding_evidence.json (raw evidence per company), the agent's qualified-funded-companies (ranked), contacts.json (people + emails/status), drafted sequences — handed to the launch skill + a review table to the channel.

Credentials / env

• env.required: none. Detection is keyless, drafting is the agent, and the launch step hands off to cold-email-outreach (CSV export with no key).
• env.optional (all degrade): APOLLO_API_KEY — if set → real people-finding + structured funding data; else → keyless serp + pattern-guess. MILLIONVERIFIER_API_KEY — if set → verify; else → local syntax/dedup (bounce risk). DROPCONTACT_API_KEY — email-finding fallback. Send/launch — if a send key (LEMLIST_API_KEY / INSTANTLY_API_KEY / SENDGRID_API_KEY / RESEND_API_KEY, or PHANTOMBUSTER_API_KEY for LinkedIn) is set → launch the sequence; else → export a CSV to send manually (the keyless default).

Notes & edge cases

• Funding detection from search can be noisy/stale — the agent must extract the date and confirm recency; drop unconfirmed rounds.
• Verify emails before send; dedup people across companies. Throttle search.