LinkedIn Warm-Up

What Warm-Up Means

Warm-up is a controlled ramp for a connected LinkedIn sender. Zevari starts at a lower activity level and gradually increases toward the sender's full tier capacity over roughly two weeks.

Why Warm-Up Exists

LinkedIn accounts are safer when activity changes gradually. Warm-up keeps early Zevari activity lower while the sender builds a steadier operating pattern.

Timeline

• Days 1-3: Calibration

  Zevari starts cautiously and keeps LinkedIn activity well below full capacity.

• Days 4-7: Building

  Activity can increase, but the account still operates under a reduced warm-up cap.

• Days 8-10: Accelerating

  The sender moves closer to full capacity while Zevari keeps daily and burst guardrails active.

• Days 11-14: Approaching full

  The account is near full tier capacity but still under the warm-up ramp.

• Day 15+: Complete

  Warm-up is complete. Standard tier, daily, weekly, burst, and safety checks still apply.

What Users See

• Warm-up percentage

  Shows how much of full tier capacity the sender is currently allowed to use.

• Stage label

  Shows whether the sender is calibrating, building, approaching full capacity, complete, or disabled.

• Effective daily caps

  Shows the current daily cap after warm-up and tier rules are applied.

• Full capacity date

  Shows when Zevari expects the sender to reach full tier capacity, if available.

Docs Links

• Help Center

  Human-facing guide to Zevari skills, workflows, agents, videos, safety, and support.

• Prompting Guide

  Copy-ready prompts that require AI assistants to read the MCP reference before acting.

• Workspaces and Sender Seats

  Human-facing setup guide for workspaces, members, billing, add-on LinkedIn sender seats, and teammate LinkedIn connections.

• Safety Center

  Human-facing guide to Zevari safety guardrails, warm-up, pauses, blocked actions, and recovery state.

• Warm-Up

  Human-facing guide to LinkedIn sender warm-up and gradual activity ramping.

• MCP Reference

  Agent-facing tool schemas, capability contracts, examples, gotchas, and recovery guidance.

• API Reference

  Public REST API reference powered by the Zevari OpenAPI document.

• MCP Reference JSON

  Machine-readable MCP tool reference.

• LLMs Full

  AI-readable Zevari documentation bundle.

• Support

  Send bug reports, support handoffs, and feature requests.