checkpoint

Capture and archive AI session state into structured markdown files.

10|1|Updated Jun 29, 2026
One-click install
npx skills add https://github.com/mishahanin/heading-os --skill checkpoint-mishahanin
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: checkpoint
Source: https://github.com/mishahanin/heading-os/tree/main/.claude/skills/checkpoint
Command: npx skills add https://github.com/mishahanin/heading-os --skill checkpoint-mishahanin

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill solves the issue of losing context or session progress when switching tasks or approaching token limits, allowing for a clean, organized handoff of work.

Core Features & Use Cases

  • Manual Session Handoff: Captures objectives, current state, and next steps into a structured archive file.
  • Pointer Management: Automatically updates latest summary and prompt files to ensure the AI resumes exactly where you left off.
  • Use Case: When you are mid-implementation and need to switch to an urgent task, use this skill to snapshot your current progress so you can return to the exact same state later without re-explaining the context.

Quick Start

Type /checkpoint followed by a brief note about your current progress to save your session state.

Frequently Asked Questions about checkpoint

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

FAQPage Schema
How do I save my AI session state before hitting token limits?

To save AI session state before hitting token limits, capture current objectives, decisions, and pending actions into structured markdown archive files for future resumption. This prevents context loss by archiving progress and updating pointer files so the AI resumes exactly where you left off.

What is the best way to switch coding tasks without losing context?

The best way to switch tasks without losing context is creating a manual session handoff that snapshots your current progress. Documenting your current state and next steps into an archive file allows you to return to the exact same workflow state later without re-explaining context.

How does an AI session checkpoint work for project continuity?

An AI session checkpoint works by archiving the current workflow state into structured markdown files and automatically updating latest summary and prompt pointers. This mechanism maintains long-term project continuity by ensuring the AI resumes exactly where the previous session ended.

Do I need specific directory access to archive an AI session?

Yes, you need write access to the operations handoff directory to archive an AI session. This directory access is required to maintain session pointers and historical logs that ensure seamless resumption of your workflow state across different tasks.

Can I resume an interrupted implementation from a markdown handoff file?

Yes, you can resume an interrupted implementation from a markdown handoff file because the system automatically updates latest summary and prompt pointers. Reading these structured files ensures the AI resumes exactly where you left off without losing prior decisions or pending actions.