handshake

Review existing memory and ask targeted questions about expertise and collaboration norms.

1|Updated Apr 17, 2026

Search Tags:#collaboration #memory #calibration #preferences #project #memory-management

Ready to run

npx skills add https://github.com/sorawit-w/agent-skills --skill handshake

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: handshake
Source: https://github.com/sorawit-w/agent-skills/tree/main/skills/handshake
Command: npx skills add https://github.com/sorawit-w/agent-skills --skill handshake

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill helps establish clear mutual understanding and expectations between the AI and the user by conducting a transparent calibration process upfront.

Core Features & Use Cases

Proactive Memory Review: Surfaces existing user memory entries to verify or correct before further interaction.
Explicit Calibration: Asks targeted questions about expertise, working preferences, and project context to tailor subsequent responses.
Structured Memory Updates: Writes user preferences and project details into the shared memory store for consistent future use.
Use Case: Ideal for new sessions or onboarding, ensuring the AI understands and aligns with the user’s working style and project parameters.

Quick Start

Invoke the handshake to review your profile and set project preferences, ensuring the AI represents your working style accurately.

Frequently Asked Questions about handshake

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I calibrate AI memory and preferences before starting a project?▼

Calibrating AI memory involves reviewing existing stored preferences, asking targeted questions about your expertise, and writing project details into a shared memory store to ensure consistent future interactions and personalized responses.

What is explicit user calibration for AI collaboration?▼

Explicit user calibration is a transparent onboarding process that establishes mutual understanding between you and the AI by asking targeted questions about your expertise and collaboration norms to tailor subsequent responses.

How do I set AI collaboration norms and project context for a new session?▼

You can set collaboration norms by invoking a structured handshake process that captures your working preferences and project parameters, storing them in memory to align the AI with your specific needs.

Does proactive memory review help fix incorrect AI user profiles?▼

Yes, proactive memory review surfaces existing user memory entries so you can verify or correct them before further interaction, ensuring the AI represents your working style accurately and avoids persistent misunderstandings.

When should I use a structured memory update for AI interactions?▼

You should use a structured memory update during new sessions or onboarding to establish clear mutual expectations, ensuring the AI understands your working style and project parameters before collaborating.