Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
LinkedIn inbox management with scheduled scanning, auto-draft responses following user's communication style, and approval workflows. Use when monitoring LinkedIn messages, drafting replies, managing inbox during off-hours, or setting up morning ping summaries of LinkedIn activity.
LinkedIn inbox management with scheduled scanning, auto-draft responses following user's communication style, and approval workflows. Use when monitoring LinkedIn messages, drafting replies, managing inbox during off-hours, or setting up morning ping summaries of LinkedIn activity.
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run.
Automated LinkedIn inbox monitoring with human-in-the-loop approval for responses. Uses Peekaboo for UI automation (no API rate limits, works with any LinkedIn account).
macOS with Peekaboo CLI installed (brew install steipete/tap/peekaboo) Screen Recording + Accessibility permissions granted LinkedIn logged in via browser (Chrome recommended) Clawdbot with browser capability
# Grant Peekaboo permissions peekaboo permissions # Verify LinkedIn is accessible peekaboo app launch "Google Chrome" peekaboo see --app "Google Chrome" --annotate --path /tmp/linkedin-check.png
Create linkedin-inbox-config.json in your workspace: { "scan": { "intervalMinutes": 60, "activeHours": { "start": 9, "end": 18, "timezone": "America/Los_Angeles" }, "skipWeekends": true }, "drafting": { "styleProfile": "USER.md", "templates": { "decline": "Thanks for reaching out. Not a fit for us right now, but best of luck.", "interested": "This looks interesting. Happy to chat more. What's your availability?", "referral": "I might know someone. Let me check and get back to you." } }, "notifications": { "channel": "discord", "target": "#linkedin" } }
# Navigate to LinkedIn messaging peekaboo app launch "Google Chrome" peekaboo menu click --app "Google Chrome" --item "New Tab" peekaboo type "https://www.linkedin.com/messaging/" --return sleep 3 # Capture inbox state peekaboo see --app "Google Chrome" --window-title "Messaging" --annotate --path /tmp/linkedin-inbox.png The agent reads the annotated screenshot to identify: Unread messages (bold names, blue dots) Message previews Sender names and titles
For each unread message: Agent reads the conversation Classifies intent (pitch, networking, job inquiry, spam) Drafts response matching user's communication style Posts draft to notification channel for approval Example notification: ๐ผ LinkedIn: New message from **Alex M.** (Founder @ SomeCompany) Preview: "Hi, I noticed you're growing and wondered if..." **My read:** Services pitch. Doesn't fit current needs. **Draft reply:** > Thanks for reaching out. We're set on that side for now, but I'll keep you in mind if that changes. React โ to send, โ to skip, or reply with edits.
On approval: # Click into conversation peekaboo click --on [message-element-id] --app "Google Chrome" sleep 1 # Type response peekaboo type "Your approved message here" --app "Google Chrome" # Send (Enter or click Send button) peekaboo press return --app "Google Chrome"
The skill reads USER.md (or configured style file) to match the user's tone: Extract these signals: Formality level (casual vs professional) Typical greeting style Sign-off patterns Sentence length preference Banned words/phrases Response length norms Apply to drafts: Mirror detected patterns Use user's vocabulary Match their directness level Respect their guardrails (no "excited", no hype, etc.) See references/style-extraction.md for detailed guidance.
Add LinkedIn summary to your morning ping: ๐ฃ The Morning Ping โ Monday, Jan 27 **LinkedIn:** โข ๐ Sarah Chen replied โ "That sounds great, let's do Thursday" โ Draft ready โข ๐ Mike R. replied โ "Not interested right now" โ No action needed โข ๐ฉ 3 new connection requests (2 sales pitches, 1 relevant) โข ๐ฉ 1 unread message from Alex (job inquiry) โ Draft ready Reply "send sarah" to approve, "skip mike" to archive.
Users can respond with: send [name] - Send the drafted reply send all - Send all pending drafts skip [name] - Archive without replying edit [name]: [new message] - Replace draft and send show [name] - Show full conversation
{ "schedule": "0 */2 9-18 * * 1-5", "text": "Scan LinkedIn inbox and post any new messages to #linkedin with draft replies" }
Never send without explicit approval - Always wait for user confirmation Rate limit actions - Max 20 LinkedIn actions per hour Respect quiet hours - Don't scan outside configured activeHours Log everything - Record all actions in daily memory file Preserve originals - Never delete messages, only archive
Ensure Chrome is open with LinkedIn logged in Check window title matches (may vary by language) Use peekaboo list windows --app "Google Chrome" --json to debug
LinkedIn sessions expire periodically Re-authenticate manually in browser Skill will detect login page and notify user
peekaboo permissions # Check status # Grant via System Preferences > Privacy & Security > Screen Recording + Accessibility
scripts/scan_inbox.sh - Peekaboo commands for inbox capture scripts/send_message.sh - Peekaboo commands for sending references/style-extraction.md - Guide for communication style matching
Messaging, meetings, inboxes, CRM, and teammate communication surfaces.
Largest current source with strong distribution and engagement signals.