Gtm outreach

LinkedIn Outreach

Execute LinkedIn connection requests and personalized messages/InMails via PhantomBuster agents.

Read these files before executing:

• .agents/skills/_shared/conventions.md
• .agents/skills/_shared/phantombuster.md

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When to Use

• Contacts are enriched and ready for LinkedIn outreach
• You want to send connection requests and/or InMails from a CSV
• You want to put outreach on a paced loop (every N minutes)

Inputs

Input	Required	Source
Contacts CSV	Yes	People enrichment output or user-provided
linkedin_profile_url column	Yes	Must be present
linkedinMessage column	For messaging	Pre-generated by n8n message_agent or manually written
inmailSubject column	For InMail	Pre-generated or user-provided

If linkedinMessage / inmailSubject are missing, ask the user whether to generate them or use a single shared template message.

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Agents

Phase	Phantom Script	Local Config Key
1 — Connection request	LinkedIn Auto Connect	PB_AGENT_CONNECT
2 — Message / InMail	Sales Navigator Message Sender	PB_AGENT_MESSAGE

Agent IDs are account-specific. Resolve via _shared/local.md (preferred) or PhantomBuster MCP (PHANTOMBUSTER_GET_AGENTS_FETCH_ALL → match by script name). See _shared/phantombuster.md.

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Execution

Step 1: Inspect the CSV

Read 3–5 rows. Confirm:

• linkedin_profile_url present and not empty
• linkedinMessage and inmailSubject present if Phase 2 is needed
• Total row count
• Ask the user: run Phase 1 (connect), Phase 2 (message), or both?

Step 2: Generate scripts

Generate one Python script per phase, saved in the same directory as the CSV. Follow the script template in _shared/phantombuster.md exactly.

Script names:

• pb_connect.py — connection requests
• pb_message.py — messages / InMails

State files (auto-created alongside scripts):

• pb_connect_state.json
• pb_message_state.json

Use the argument templates from _shared/phantombuster.md. Do not save to the agent config permanently — always use the one-off argument override in the launch body.

Step 3: Test run each script

python3 "<path>/pb_connect.py"
python3 "<path>/pb_message.py"

Show full agent output. Look for:

• ✅ Connected successfully as [Your LinkedIn Name] — auth working
• Profile name in output — correct profile targeted
• ✅ 1 profile has been processed — success

If the output shows a type error (numberOfAddsPerLaunch => is the wrong type), the argument JSON has string values where booleans/ints are needed. Fix by ensuring json.dumps() is used on the argument dict.

Step 4: Suggest loop commands

After successful test runs, show the user:

✅ Both scripts tested successfully.

To process one profile every 20 minutes (10am–6pm Berlin time):

  Phase 1 (connect):
  /loop 20m python3 "<path>/pb_connect.py"

  Phase 2 (message) — run after connections are accepted (typically 1–3 days):
  /loop 20m python3 "<path>/pb_message.py"

Or run either script again immediately:
  python3 "<path>/pb_connect.py"

State is tracked in pb_connect_state.json and pb_message_state.json —
each script picks up where it left off. Scripts exit cleanly when all profiles are done.

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Rate Limit Guidance

Agent	Safe Rate	Notes
LinkedIn Auto Connect	1 per run, max ~14/day	100/week LinkedIn hard limit. Pacing: every 20–30 min
Sales Navigator Message Sender	1–3 per run	InMail credits apply for 3rd-degree / non-connections

Example throughput: 43 contacts × 1/run × 20 min interval = ~24 runs/day (10am–6pm) → all connected in ~2 days.

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Output

Append to run_log.md per conventions:

## Run Summary — outreach — {timestamp}
- Phase: {connect | message | both}
- Records in CSV: {total}
- Already processed: {done_count}
- Remaining: {total - done_count}
- Scripts: {paths}
- Loop commands: shown above