Warm Sourcing & Referrals

Trigger

Keyword: referrals (or variants: "warm sourcing", "buscar contactos", "solicitar referido")

The user says referrals or launches warm sourcing for a target company/role. Also executed as step 0 of the apply and targets flows to maximize conversion.

Flow

0. Pre-flight

• Verify active browser session (see AGENTS.md "Browser session"): node scripts/browser.js open <url> --headed (Gold Rule 5) if session closed
• Load profile, university background, past companies, and job preferences from Postgres DB:

  node scripts/db.js "SELECT data->'profile' AS profile, data->'job_preferences' AS prefs, data->'style_profile' AS style FROM users WHERE id = 1"
  

• Load strategy (see AGENTS.md "Strategy levels"):

  node scripts/db.js "SELECT data->'strategy' AS strategy FROM users WHERE id = 1"
  

  Respect: cold_outreach (gates the recruiter-outreach branch in step 3). If referrals is not in sources_active, the flow should not run standalone — when invoked as step 0 of apply/targets, those flows handle the gate.
• Load active preferences (see memory skill):

  node scripts/db.js "SELECT category, key, value, confidence, source FROM preferences WHERE user_id = 1 AND status = 'active' ORDER BY category, key"
  

1. Warm Contact & Recruiter Discovery

For a target company and role:

# Automated discovery script
node scripts/linkedin-warm-sourcing.js --company "<Company>" --role "<Role>" --json

The script searches for:

1. 1st & 2nd degree connections currently working at <Company>
2. University alumni (matching institutions from users.data.profile.education)
3. Ex-colleagues (matching past employers from users.data.profile.experience)
4. Recruiters & Hiring Managers assigned to the role/company

2. Referral Request Staging (Highest Conversion — Strategy #1)

If an internal contact, alumni, or ex-colleague is found:

1. Do NOT submit a cold application immediately. A referral yields a 40% hire rate vs 2-3% for cold Easy Apply.
2. Draft a personalized referral request message:
   • Must pass Gold Rule 7 (Anti-LLM Checklist): no em-dashes, no bullet points, conversational tone, max 2 short paragraphs, natural mention of shared background (alumni/ex-colleague/interest).
   • Tone: polite, non-demanding, asking for team insights or guidance on applying.
3. Stage the draft in DB:

   node scripts/db.js "INSERT INTO messages (user_id, channel, direction, sender, subject, body, draft, status, received_at, data) VALUES (1, 'linkedin', 'outbound', '<contact_name>', 'Solicitud de referido / consulta sobre equipo', '', '<draft_text>', 'draft', NOW(), '{\"category\": \"referral_request\", \"company\": \"<Company>\", \"vanity\": \"<vanity>\"}'::jsonb)" --write
   

4. Register or update pipeline card in stage discovered:

   node scripts/pipeline.js --move <id> discovered
   

3. Recruiter Outreach Staging (Multi-channel Combo — Strategy #4)

If NO internal referral path exists:

1. Gate: if strategy.cold_outreach = false → skip this step. Proceed to step 4 (ATS micro-alignment) and cold apply only.
2. Extract the Recruiter / Hiring Manager profile vanity or email.
3. Prepare a personalized recruiter DM outreach draft (3-4 lines: trigger + credibility anchor + clear ask).
4. Stage the draft in DB (messages table with category: recruiter_outreach).
5. Proceed to cold postulation via apply or targets while keeping the recruiter outreach staged for user approval (surfaced by news flow).

4. Dynamic ATS Micro-Alignment (JD-to-CV Tailoring)

Before submitting an application via ATS or email:

1. Extract top 5 technical & domain keywords from the target Job Description (e.g., LangChain, System Architecture, PyTorch, Technical Leadership).
2. Compare against users.data.profile.skills and users.data.cv_markdown.
3. Highlight matching achievements in the top summary/highlights of the CV markdown.
4. Generate the micro-aligned PDF CV using scripts/generate-cv.js before submitting:

   node scripts/generate-cv.js --output assets/cv_tailored_<company>.pdf
   

5. Presentation & Summary

Present the warm sourcing results to the user:

• Internal contacts / Alumni found: list with profile URLs and proposed referral draft.
• Recruiters found: list with proposed DM outreach draft.
• Tailored CV generated: link to tailored PDF.

Dependencies

• Depends on onboarding (DB to register)
• Depends on profile (education & past experience data for alumni/ex-colleague matching)
• Integrated into apply and targets flows

Script reference

scripts/linkedin-invite.js -- Send connection requests

Navigates to /preload/custom-invite/?vanityName=<vanity>, clicks "Send without a note". Anti-ban delay of 3s between invites.

# Invite one or more vanities
node scripts/linkedin-invite.js <vanity-name>

# Invite multiple
node scripts/linkedin-invite.js vanity1 vanity2 vanity3

# Search + invite in one command (pipe search -> invite)
node scripts/linkedin-invite.js --from-search '"<Role>" "hiring" LATAM'

Flags: --from-search "<keywords>" (searches and invites all found) Exit codes: 0 = at least one sent, 1 = all failed, 2 = error