Agents can sign plugins, rotate credentials without losing identity, and publicly attest to behavior.
日本語の概要は準備中です。原文の説明を表示しています。
Send personalized LinkedIn direct messages to a list of existing 1st-degree connections via browser automation. Use when the user wants to message LinkedIn connections with AI-personalized outreach — e.g. nurturing leads, following up after events, reconnecting with contacts, or announcing something. Takes a data file (CSV/TSV) or plain list with connection names and companies, asks for outreach context/goal, generates a tailored message per person, and sends each one via browser automation. Handles message compose flow, character limits, and incremental status tracking.
インストールする前に、エージェントに与えられる指示の中身を確認できます。
Sends personalized LinkedIn messages to existing 1st-degree connections. Each message has:
Ask the user for their data file or list. Must include (or be added):
If only a plain list is provided, offer to convert to TSV.
Before writing any messages, navigate to /in/me/ and read the sender's profile:
Store these facts. They are used to identify relationship hooks with each connection.
Ask the user:
"What's your pitch / product message? This will be the consistent part of every message. Describe it in 1–2 sentences."
Then draft a polished pitch section (2–4 sentences max, punchy and clear). Show it to the user and get explicit approval. Do not start sending until the pitch is confirmed.
Example prompt: "Here's the pitch I'll use for everyone — confirm or edit:
'I'm building an AI calling agent — you give it a phone number + context, and it handles the call end-to-end. Think customer follow-ups, research calls, vendor coordination — anything phone-based that eats into your day. Happy to show you a demo if this sounds useful.'"
profile="chrome"): extension attached to LinkedIn tab (badge ON) — recommended for flagged accountsprofile="openclaw"): openclaw-managed Chrome, LinkedIn logged inAsk the user for a Google Sheet ID/URL to log outreach results. If they don't have one, offer to set one up (create tab + write headers). Confirm gog is authenticated (gog auth list).
If the user skips this, fall back to local linkedin_dm_progress.json but remind them the follow-up skill needs the sheet.
Only proceed once:
Before writing a message, compare the connection's profile against the sender's profile to find the strongest hook. Use this hierarchy — pick the highest that applies:
| Priority | Hook | Example opener |
|---|---|---|
| 1 | Same company (current or past) | "You and I both spent time at CRED…" |
| 2 | Same college + overlapping years | "Fellow BITS Goa 2018 batch here…" |
| 3 | Same college (different years) | "BITS connect here — saw your journey from…" |
| 4 | Same industry/function | "Both been in fintech/product for a while…" |
| 5 | Mutual connection | "We're both connected to [Name]…" |
| 6 | Their work context (no personal hook) | "Seen what you've built at [Company]…" |
Combine the hook with a line about their current work to show you know what they do.
Send as two separate messages per person, back to back:
Message 1 — Personalized opener (unique per person)
[Relationship hook — 1 sentence]
[Acknowledgement of their work/role — 1 sentence]
Target: 100–180 chars. Feels like a genuine reach-out from someone who knows them.
Message 2 — Pitch (identical for everyone, confirmed upfront)
[Product description — 1–2 sentences]
[Relevant use case for their role — 1 sentence]
[Soft CTA — 1 sentence]
Target: 150–250 chars. Clear, punchy, no filler.
Why two messages?
Fallback: If sending two messages is technically difficult (e.g. bubble re-focusing issues), use Shift+Enter twice between the opener and pitch to create a paragraph break within a single message.
Do not:
Generate messages for the entire list first. Present them in a table:
| Name | Company | Relationship Hook Used | Message Preview |
|---|---|---|---|
| Shorya Saini | Razorpay | Same BITS batch | Hey Shorya, BITS Goa 2018 batch… |
Get user approval on the full batch before opening the browser. Allow edits per row.
/feed/ — mandatory, no exceptions, no skippinglinkedin.com/mynetwork/invite-connect/connections/ — type name in "Search by name"Not a Connection, skipgog sheets append with all fields (see CRM Tracking section)See references/browser-workflow.md for exact browser automation steps.
| Status | Meaning |
|---|---|
Sent | Message delivered this session |
Already Messaged | Recent conversation exists — skip |
Not a Connection | No Message button or not in connections search |
Profile Not Found | Could not identify the right person |
Skipped | User chose to skip |
Failed | Browser error — retry next session |
/feed/ before every single profile — non-negotiableAfter each message is sent, append a row to a Google Sheet. This sheet is the source of truth for all outreach — current session and future follow-up.
Ask the user for a Google Sheet ID or URL at the start of the session (or offer to create a new one). The sheet should have a tab named Outreach with these columns:
| Col | Field | Notes |
|---|---|---|
| A | Date Sent | ISO date, e.g. 2026-02-13 |
| B | Person Name | Full name |
| C | Role / Title | Their current headline from LinkedIn |
| D | Company | Current company |
| E | LinkedIn URL | Profile URL |
| F | Relationship Hook | What hook was used (e.g. "Same batch BITS Goa 2018", "Both at CRED 2022–23") |
| G | Opener Sent | Exact text of Message 1 |
| H | Pitch Sent | Exact text of Message 2 |
| I | Campaign | Short label for this batch (e.g. "AI Calling - Feb 2026") |
| J | Status | Always Sent when first logged — updated by follow-up skill |
| K | Notes | Anything notable (prior conversation, context, mutual connection used) |
| L | Last Updated | Timestamp of last status change |
Column I (Status) lifecycle — only Sent is written by this skill. The follow-up skill will update to:
Replied · Call Scheduled · Demo Done · Follow Up Sent · No Response · Closed Won · Closed Lost
After each message pair is sent, run:
gog sheets append <SHEET_ID> "Outreach!A:L" \
--values-json '[["<date>","<name>","<role>","<company>","<url>","<hook>","<opener>","<pitch>","<campaign>","Sent","<notes>","<timestamp>"]]' \
--insert INSERT_ROWS
If no sheet exists yet, tell the user:
"I'll need a Google Sheet to track outreach. Share an existing sheet ID/URL, or I can create one with the right columns."
To create a new sheet, use Drive (or ask user to create one and share the ID). Then write the header row:
gog sheets update <SHEET_ID> "Outreach!A1:L1" \
--values-json '[["Date Sent","Person Name","Role / Title","Company","LinkedIn URL","Relationship Hook","Opener Sent","Pitch Sent","Campaign","Status","Notes","Last Updated"]]' \
--input USER_ENTERED
If Google Sheets is not set up, fall back to a local linkedin_dm_progress.json:
{
"campaign": "AI Calling - Feb 2026",
"pitch": "confirmed pitch text",
"rows": [
{
"date": "2026-02-13",
"name": "Shorya Saini",
"role": "Senior Analytics Specialist",
"company": "Razorpay",
"url": "https://linkedin.com/in/shorya-saini",
"hook": "Same batch BITS Goa 2018",
"opener": "Hey Shorya...",
"pitch": "I'm building...",
"status": "Sent",
"notes": ""
}
]
}
まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
Agents can sign plugins, rotate credentials without losing identity, and publicly attest to behavior.
日本語の概要は準備中です。原文の説明を表示しています。
Query historical crypto market data from 0xArchive across Hyperliquid, Lighter.xyz, and HIP-3. Covers orderbooks, trades, candles, funding rates, open interest, liquidations, and data quality. Use when the user asks about crypto market data, orderbooks, trades, funding rates, or historical prices on Hyperliquid, Lighter.xyz, or HIP-3.
日本語の概要は準備中です。原文の説明を表示しています。
Calculate ROI for AI-as-a-Service (managed AI agents). Estimates cost savings, efficiency gains, and payback period for deploying AI agents across business operations. Use when evaluating whether managed AI agents make financial sense for a company.
日本語の概要は準備中です。原文の説明を表示しています。
Design, build, deploy, and operate production AI agent systems — single agents, multi-agent teams, and autonomous swarms. Complete methodology from agent architecture through orchestration, memory systems, safety guardrails, and operational excellence.
日本語の概要は準備中です。原文の説明を表示しています。
Design, build, test, document, and secure production-grade APIs. Covers the full lifecycle from schema design through deployment, monitoring, and versioning. Use when designing new APIs, reviewing existing ones, generating OpenAPI specs, building test suites, or debugging production issues.
日本語の概要は準備中です。原文の説明を表示しています。
Turn your AI agent into a business automation architect. Design, document, implement, and monitor automated workflows across sales, ops, finance, HR, and support — no n8n or Zapier required.
日本語の概要は準備中です。原文の説明を表示しています。