Checkpoint

Checkpoint

Persistent memory layer for AI coding assistants. Eliminate rediscovery. Ship faster.

[](LICENSE) [](https://docs.anthropic.com/en/docs/claude-code) [](https://cursor.sh) [](https://github.com/features/copilot) [](https://github.com/openai/codex)

claude install-skill Jyo238/checkpoint

[繁體中文](README.zh-TW.md)

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> “Every new AI session starts from zero. Checkpoint makes it start from where you left off.”

Your AI coding assistant is brilliant within a single conversation — but start a new session and everything is forgotten. The API quirk it debugged for 30 minutes? Gone. The user preference you corrected? Forgotten. The architectural decision it finally understood? Rediscovered from scratch.

Checkpoint fixes this. One command (/checkpoint) captures every breakthrough, failed approach, user preference, and project context into structured, persistent memory files that survive across sessions — so your AI never solves the same problem twice.

One /checkpoint command — 8 memory files saved, 4 updated, 1 deleted, index refreshed.

The Problem

AI coding assistants are brilliant within a single conversation — but start a new session and everything is forgotten:

• Session 1: Spend 30 minutes discovering the API requires site-based paths
• Session 2: Spend 30 minutes rediscovering the same thing
• Session 3: You're frustrated. The AI has amnesia.

The Solution

Checkpoint creates a structured, persistent memory system that survives across conversations:

You:  /checkpoint
AI:   Scanning conversation... Found 3 new discoveries:

      1. [SAVED] project_auth_config.md
         - Tenant blocks app consent, must use pre-authorized client ID
         - Type: project

      2. [SAVED] feedback_no_admin.md
         - Never suggest "ask your admin" — user has no admin rights
         - Type: feedback

      3. [UPDATED] reference_api_paths.md
         - Added newly discovered endpoint paths
         - Type: reference

      Memory index updated. 3 entries saved/updated.

How It Works

Checkpoint operates on two layers:

Active Layer: /checkpoint Command

Explicitly trigger a full knowledge archival. The AI will:

1. Scan the conversation for all discoveries
2. Check existing memory to avoid duplicates
3. Consolidate — fix index issues (ghosts, orphans, cross-scope duplicates)
4. Save new findings as structured memory files (or update existing ones)
5. Update the MEMORY.md index
6. Verify completeness and report consolidation findings

Memory Consolidation: /checkpoint:consolidate

Over time, memories accumulate duplicates and stale entries. Run /checkpoint:consolidate for deep maintenance:

• Duplicate detection — find and merge memories covering the same topic (with your approval)
• Cross-scope resolution — detect duplicates between project and global memory
• Staleness detection — flag project memories with past dates or outdated references
• Quality checks — identify memories missing required structure (Why/How to apply)
• Index repair — remove dead links, add unindexed files

Passive Layer: Anti-Amnesia Protocol

A set of rules added to your CLAUDE.md (or equivalent) that runs automatically:

• Before work: Check memory for prior discoveries
• After breakthroughs: Save immediately, don't wait
• Before retries: Check if already solved — don't rediscover

Memory Types

| Type | Purpose | Example | |------|---------|---------| | user | Who you are | "Senior backend engineer, new to React" | | feedback | What you corrected | "Don't mock the database in integration tests" | | project | Context behind the work | "Auth rewrite is compliance-driven, not tech debt" | | reference | Where to find things | "Pipeline bugs tracked in Linear project INGEST" |

Installation

Claude Code

claude install-skill Jyo238/checkpoint

This installs:

• /checkpoint slash command
• /checkpoint:consolidate sub-command for memory maintenance
• Skill definition for the AI to follow
• Reference docs for memory types, consolidation algorithm, and anti-amnesia protocol

Optional: Add the Anti-Amnesia Protocol to your project's CLAUDE.md:

cat skills/checkpoint/references/anti-amnesia-protocol.md >> CLAUDE.md

Cursor

Copy the rule file to your project:

mkdir -p .cursor/rules
cp cursor/rules/checkpoint.mdc .cursor/rules/

VSCode / GitHub Copilot

Copy the instruction and prompt files:

cp vscode/instructions/checkpoint.md .github/copilot-instructions.md
cp vscode/prompts/checkpoint.prompt.md .github/prompts/

OpenAI Codex CLI

cp codex/checkpoint/SKILL.md your-project/
cp .codex/INSTALL.md your-project/.codex/

Manual Installation (Any AI Tool)

1. Copy the content from skills/checkpoint/SKILL.md into your AI tool's system prompt or instruction file
2. Copy the Anti-Amnesia Protocol from skills/checkpoint/references/anti-amnesia-protocol.md into your project config
3. Create a memory/ directory with an empty MEMORY.md index file

File Structure

checkpoint/
├── skills/checkpoint/
│   ├── SKILL.md                          # Core skill definition
│   └── references/
│       ├── anti-amnesia-protocol.md      # Passive defense for CLAUDE.md
│       ├── consolidation.md             # Memory consolidation algorithm
│       └── memory-types.md              # Detailed guide to 4 memory types
├── commands/
│   ├── checkpoint.md                    # /checkpoint command trigger
│   └── checkpoint/
│       └── consolidate.md              # /checkpoint:consolidate sub-command
├── cursor/rules/
│   └── checkpoint.mdc                   # Cursor AI rule
├── vscode/
│   ├── instructions/checkpoint.md       # Copilot instructions
│   └── prompts/checkpoint.prompt.md     # Copilot prompt
├── codex/checkpoint/
│   └── SKILL.md                         # Codex CLI version
├── .claude-plugin/
│   ├── plugin.json                      # Claude Code plugin manifest
│   └── marketplace.json                 # Marketplace metadata
├── .codex/
│   └── INSTALL.md                       # Codex install guide
├── .github/workflows/
│   └── release.yml                      # Auto-release on tag
├── LICENSE                              # MIT
├── README.md                            # This file
└── README.zh-TW.md                      # 繁體中文版

Real-World Impact

This skill was born from real pain. During a multi-session project working with OneNote APIs, the AI repeatedly:

• Forgot that shared notebooks require site-based API paths (/sites/{id}/onenote/)
• Forgot that the tenant blocks app consent and needs a pre-authorized client ID
• Forgot the user has no admin rights and kept suggesting "ask your admin"

Each rediscovery wasted 20-30 minutes. After implementing Checkpoint, the AI reads its memory at the start of every session and skips straight to working solutions.

Before Checkpoint: 3 sessions x 30 min rediscovery = 90 min wasted After Checkpoint: 0 min wasted. Memory is loaded in seconds.

Quick Start (30 seconds)

# Claude Code
claude install-skill Jyo238/checkpoint

# Then in any conversation:
/checkpoint              # Save discoveries from current session
/checkpoint:consolidate  # Deep clean: merge duplicates, fix index, flag stale entries

That's it. Your AI will scan the conversation, extract key discoveries, and save them as persistent memory files. Next session, it reads them automatically.

Use Cases

| Scenario | Without Checkpoint | With Checkpoint | |----------|-------------------|-----------------| | Multi-day debugging | Rediscover root cause every session | AI reads prior findings, picks up where it left off | | Team onboarding | Each person's AI re-learns the same gotchas | Shared memory files capture tribal knowledge | | Complex API integration | Forget auth quirks, endpoint patterns | All discoveries persisted and loaded automatically | | User preferences | "I told you not to do that" (again) | Feedback saved once, respected forever |

Contributing

Contributions are welcome! Feel free to:

• Add support for more AI coding tools
• Improve the memory type system
• Share your Anti-Amnesia Protocol variations
• Report issues or suggest improvements

License

[MIT](LICENSE)

Source & license

This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: Jyo238
• Source: Jyo238/checkpoint
• License: MIT
• Homepage: https://github.com/Jyo238/checkpoint#installation

Install and usage instructions live in the source repository linked above.