handoff

Record durable learnings and continuation notes in a MEMORY.md index.

20|5|Updated Nov 30, 2025
One-click install
npx skills add https://github.com/tale-project/tale --skill handoff-tale-project
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: handoff
Source: https://github.com/tale-project/tale/tree/main/.claude/skills/handoff
Command: npx skills add https://github.com/tale-project/tale --skill handoff-tale-project

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Preserve durable learnings and concise continuation context across context boundaries and agent handoffs, preventing loss of decisions and non-obvious facts.

Core Features & Use Cases

  • Persist durable learnings to memory when a task spans sessions or requires handing off to another agent.
  • Compress context for quick re-orientation, including a concise continuation note and pointers to relevant files.
  • Use cases include long-running experiments, multi-agent coordination, and knowledge retention across iterations.

Quick Start

Record a key decision and a concise continuation note for the next agent.

Frequently Asked Questions about handoff

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

FAQPage Schema
How do I preserve context and decisions when handing off tasks between agents?▼

To preserve context during agent handoffs, record explicit memory notes and a concise continuation context. This approach prevents the loss of durable learnings and non-obvious facts across sessions.

Why does my agent lose key decisions when the context limit is reached?▼

Agents lose key decisions near context limits because durable learnings are not explicitly recorded. Compressing context into lightweight continuation notes and a centralized index prevents this loss.

What is the best way to manage memory across multi-session workflows?▼

Managing memory across multi-session workflows requires a centralized index and explicit memory recording. This setup ensures durable learnings and continuation context are preserved for future iterations.

Can I use a single memory index for long-running experiments and multi-agent coordination?▼

Yes, you can use a centralized memory index for long-running experiments and multi-agent coordination. It enforces explicit memory recording and provides pointers to relevant files for quick re-orientation.

How to compress context for quick re-orientation of a new agent?▼

Compress context for a new agent by creating a concise continuation note with pointers to relevant files. This lightweight summary enables quick re-orientation without reloading the full history.