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tatsuki-washimi
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handover_session

Generate structured handover prompts and documentation for AI coding tools.

1|Updated Dec 17, 2025
Search Tags:#workflow#documentation#context#ai#handover#model-prompt
One-click install
npx skills add https://github.com/tatsuki-washimi/gwexpy --skill handover-session
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: handover_session
Source: https://github.com/tatsuki-washimi/gwexpy/tree/main/.agent/skills/handover_session
Command: npx skills add https://github.com/tatsuki-washimi/gwexpy --skill handover-session

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Handover sessions across AI tools often lose context, causing duplicated work and miscommunication. This skill provides a structured approach to document handovers and generate prompts that preserve continuity.

Core Features & Use Cases

  • Consolidates current status, references, and remaining tasks into a clear handover package.
  • Defines a repeatable workflow: archive_work, construct a handover prompt, and tailor it for the next worker.
  • Supports model-to-model handoffs in coding and planning tasks, reducing context loss.

Quick Start

Use archive_work to summarize the latest state, then generate a handover prompt for the next model.

Frequently Asked Questions about handover_session

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

FAQPage Schema
How do I preserve context when handing over AI coding tasks to a different model?▼

You preserve context during an AI handover by consolidating current status, references, and remaining tasks into a structured handover prompt. This approach prevents context loss and ensures reliable task continuation across different models.

What is the best way to document workflow context for multi-tool AI environments?▼

Documenting workflow context involves enforcing a frontmatter-defined identity and capturing current status, references, and next steps. This creates a repeatable handover package that supports reliable continuation in multi-tool environments.

How do I archive work and generate a handover prompt for the next AI model?▼

To generate a handover prompt, first use the archive_work function to summarize the latest project state. Then, construct a structured handover prompt tailored for the next AI model to ensure seamless task continuation.

Does this handover skill support model-to-model handoffs for both coding and planning tasks?▼

Yes, this skill supports model-to-model handoffs for coding and planning tasks. It reduces context loss by capturing current status, references, and next steps into a clear handover package for the next model.

Why do AI tools lose context during a handover session and how can I prevent it?▼

AI tools lose context during handovers because they lack shared memory of previous interactions. Prevent this by enforcing a documented workflow that captures current status, references, and next steps into a structured handover prompt.

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