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Checkpoint

Organization
codewithmukesh
checkpoint

Mid-session save point: create a descriptive git commit and a brief handoff note, then keep working. Use before a risky change or refactor, when switching tasks, or to bank progress without ending the session. Triggers on: /checkpoint, "checkpoint", "save progress", "commit and handoff", "save state", "pause here", "before a risky change". For the full end-of-session ritual with learning extraction, use /wrap-up instead.

Overview

Publishercodewithmukesh
Repositorydotnet-claude-kit
Skill namecheckpoint
Stars
721
Forks
170
Bundled files
Instructions only
LicenseMIT
Links
Skill sourceRepositoryHomepage
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by codewithmukesh on GitHub. Read the source before you install it.

Installation

Install the Checkpoint AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/codewithmukesh/dotnet-claude-kit.git /tmp/dotnet-claude-kit
mkdir -p .claude/skills
cp -r /tmp/dotnet-claude-kit/skills/checkpoint .claude/skills/checkpoint
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Checkpoint in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Checkpoint on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Checkpoint is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

/checkpoint

What

A quick mid-session save that banks the known-good state in two moves:

  1. Descriptive git commit — stage relevant changes and commit with a message that summarizes the work.
  2. Brief handoff note — write .claude/handoff.md so a resumed session (or you, after a botched refactor) knows exactly where things stand.

Checkpoint is the mid-session save; /wrap-up is the end-of-session ritual. Checkpoint commits and jots a note, then keeps working. Wrap-up does the full handoff plus learning extraction into MEMORY.md.

When

  • Before a risky refactor or destructive change — checkpoint the known-good state
  • When switching to a different task or feature mid-session
  • After completing a logical unit of work — bank it
  • User says "checkpoint", "save progress", "save state", "pause here"
  • If the session is actually ending, use /wrap-up for the full ritual

How

  1. Assess state — git status and git diff to see staged and unstaged changes.
  2. Stage changes — Stage specific files; never git add -A. Exclude generated files, build artifacts, and secrets (.env, credentials).
  3. Commit — Descriptive message following the repo's commit style (conventional prefixes: feat:, fix:, refactor:). New commit, never amend. If there is nothing to commit, skip to the handoff.
  4. Write handoff note — .claude/handoff.md, same format as /wrap-up but abbreviated: Completed, Pending (with specific next steps), Learned (only if something non-obvious surfaced), and Context (branch + commit hash). One file, always overwritten.
  5. Confirm — Report the commit hash and the handoff summary, then resume work.

Safety Rules

  • Never push, force-push, or touch remote branches
  • Never commit secrets
  • Never amend — a checkpoint is a new commit you can return to
  • No changes to commit? Update the handoff only

Example

User: /checkpoint — about to try the EF Core split-query refactor

Claude: 3 files modified, 1 new.

  Commit a1b2c3d: "feat: add CreateOrder validation with FluentValidation"
  - src/Orders/Features/CreateOrder.cs
  - src/Orders/Features/CreateOrderValidator.cs (new)
  - tests/Orders.Tests/Features/CreateOrderTests.cs

  Handoff updated (.claude/handoff.md):
  ## Completed
  - [x] CreateOrder validation + 3 test scenarios
  ## Pending
  - [ ] Split-query refactor in GetOrderDetails (about to attempt)
  ## Context
  - Branch: feature/order-validation | Checkpoint: a1b2c3d

  Checkpoint saved — safe to proceed with the risky refactor.

Related

  • /wrap-up — End-of-session ritual: full handoff format definition plus learning extraction into MEMORY.md
  • /build-fix — Get the build green before checkpointing

Frequently asked questions

What does the Checkpoint AI skill do?

Mid-session save point: create a descriptive git commit and a brief handoff note, then keep working. Use before a risky change or refactor, when switching tasks, or to bank progress without ending the session. Triggers on: /checkpoint, "checkpoint", "save progress", "commit and handoff", "save state", "pause here", "before a risky change". For the full end-of-session ritual with learning extraction, use /wrap-up instead.

Why use Checkpoint on TypingMind?

Because you install it once and use it with any model. Checkpoint is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Checkpoint in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/codewithmukesh/dotnet-claude-kit/tree/main/skills/checkpoint. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Checkpoint?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Checkpoint?

As many as you like. As long as a model supports skills, you can use Checkpoint with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Checkpoint AI skill free?

Yes. It is published on GitHub by codewithmukesh under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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