gtm-pipeline-outreach

name

gtm-pipeline:outreach

description

Execute LinkedIn outreach — send connection requests and personalized messages/InMails to a contact list via PhantomBuster, one profile at a time on a timed loop. Use after people-enrichment when contacts are ready for outreach. Also triggers on "linkedin outreach", "send connection requests", "connect with leads on linkedin", "send linkedin messages", "send inmails", "start outreach", "launch outreach campaign", "put contacts on 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

---

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.

---

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.

---

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