Prompts for review-gated LinkedIn workflows in Claude and ChatGPT
These prompts help operators structure LinkedIn MCP work: research first, draft carefully, classify replies, and keep sensitive actions staged for review.
How Claude and ChatGPT works with Zevari
- Prospect research prompt
Research this target list against our ICP, summarize why each account might care now, and flag prospects that need more context before outreach.
- Connection note prompt
Draft three concise LinkedIn connection notes for this prospect. Use workspace positioning, avoid hype, and mark each note for human review.
- Approval check prompt
Before staging any LinkedIn action, show the exact message, target account, reason, duplicate-risk check, and recommended next step.
Common workflows
- Research prospects before outreach
Ask your AI client to research people, companies, triggers, and context before writing the first message.
- Draft LinkedIn connection notes, DMs, and content
Generate channel-aware outreach and content that matches your positioning, ICP, tone, and the prospect context.
- Stage actions for review
Prepare connection requests, replies, comments, follow-ups, content, and campaign actions in Zevari before sensitive actions execute.
- Classify replies and inbox intent
Turn inbound conversations into next steps, hot leads, follow-up tasks, and pipeline updates.
Safety controls
- Confirmation gates
Sensitive write actions, including messages, comments, connection requests, content publishing, and campaign activation, are routed through review boundaries.
- Account-level controls
Zevari applies pacing, duplicate prevention, stale-state checks, and account-state checks before risky GTM actions proceed.
- Reviewable workspace state
Operators can inspect staged work, recent activity, blocked actions, pending approvals, signals, campaigns, and inbox context in Zevari.