multiAI Summary Pending
checkpoint
Save current progress to memory-keeper to prevent work loss.
231 stars
Installation
Claude Code / Cursor / Codex
$curl -o ~/.claude/skills/checkpoint/SKILL.md --create-dirs "https://raw.githubusercontent.com/aiskillstore/marketplace/main/skills/barnhardt-enterprises-inc/checkpoint/SKILL.md"
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/checkpoint/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How checkpoint Compares
| Feature / Agent | checkpoint | Standard Approach |
|---|---|---|
| Platform Support | multi | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Save current progress to memory-keeper to prevent work loss.
Which AI agents support this skill?
This skill is compatible with multi.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Checkpoint Skill Automatically checkpoint current progress to memory-keeper to prevent catastrophic work loss when context is exhausted. ## When to Use - Every 5-10 tool calls during implementation - After completing a significant piece of work - Before starting a large operation - When switching tasks - Before ending a session - When explicitly requested via `/checkpoint` ## Checkpoint Actions ### 1. Gather Current State Collect the following information: - Current task description from todo list - List of files modified this session - Implementation progress (percentage or phase) - Current blockers or issues - Next action to take ### 2. Save to Memory-Keeper ``` context_save(key: "current-task", value: "<task description>", category: "progress", priority: "high") context_save(key: "files-modified", value: "<comma-separated file list>", category: "progress") context_save(key: "implementation-progress", value: "<percentage or phase>", category: "progress") context_save(key: "next-action", value: "<exact next step>", category: "progress", priority: "high") ``` ### 3. Create Named Checkpoint ``` context_checkpoint( name: "checkpoint-<timestamp>", description: "Task: <task>, Progress: <progress>, Files: <count>, Next: <action>" ) ``` ### 4. Prepare for Compaction (if context is large) ``` context_prepare_compaction() ``` ## Checkpoint Frequency Guidelines | Activity | Checkpoint Frequency | |----------|---------------------| | File creation/modification | After every file | | Running tests | After each test run | | Research/exploration | Every 10 tool calls | | Debugging | After each hypothesis tested | | Multi-step implementation | After each step | ## Key Items to Always Save | Key | Description | Priority | |-----|-------------|----------| | `current-task` | What you're currently working on | high | | `files-modified` | All files touched this session | normal | | `implementation-progress` | How far along (%, phase) | normal | | `next-action` | Exact next step to take | high | | `blockers` | Current issues/blockers | high | | `todo-state` | Serialized todo list | normal | ## Checkpoint Output After checkpointing, confirm with: ``` Checkpoint saved: - Task: <current task> - Progress: <progress> - Files modified: <count> - Next action: <next step> ``` ## Recovery Reference If context is lost, use `/recover` to restore state from checkpoints.