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Context Checkpoint

Community
adamentwistle
context-checkpoint

Externalize working state to a checkpoint file so long tasks survive context compaction, interruption, and handoff. Use at the start of any task expected to exceed ~30 minutes, touch many files, or run many commands; whenever a phase completes, the plan changes, or a dead end is confirmed; and immediately after any compaction or session resume, before editing. Not for short single-file edits.

Overview

Publisheradamentwistle
Repositoryfable-skills
Skill namecontext-checkpoint
Stars
98
Forks
11
Bundled files
Instructions only
LicenseMIT
Links
  • 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 adamentwistle on GitHub. Read the source before you install it.

Installation

Install the Context 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.
    https://github.com/adamentwistle/fable-skills/tree/main/context-checkpoint
  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/adamentwistle/fable-skills.git /tmp/fable-skills
mkdir -p .claude/skills
cp -r /tmp/fable-skills/context-checkpoint .claude/skills/context-checkpoint
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Context 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 Context 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 Context 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.

context-checkpoint

Weak checkpointing looks like: forty minutes of decisions living only in conversation history, then a compaction, then a confident post-compaction session redoing step 2 while overwriting completed step 4. Strong checkpointing keeps ground truth in a file a cold reader could resume from in under a minute.

1. Create the file at task start

Write checkpoint.md in the session scratchpad (or a gitignored .claude/ path in the repo — never a tracked file). Contents, in this order:

  • Goal — one line, in the user's words.
  • Acceptance — the concrete command or observation that proves done (e.g. the exact test command).
  • Steps — ordered, each with a status marker: [ ] todo, [x] done, [-] dead end.
  • Decisions — "chose X over Y because Z". The why is what evaporates first.
  • Resume point — see §3. Empty at start.

Tell the user the path once; don't narrate every update.

2. Update at phase boundaries, not continuously

Rewrite the file when a step completes, the plan changes, a dead end is confirmed, or you're about to start something long-running or risky. Record dead ends with what was tried and the evidence it failed, so they aren't retried. Don't checkpoint every edit — noise hides the signal.

3. Make the resume point executable

The resume section contains, verbatim: the last command you ran and its current output (trimmed to the failing lines), the file:line you were editing, and the next single concrete action. "Continue the migration" is useless after compaction; "run <test cmd> -k utf8; still failing at ingest.py:214 null check; next: guard row.name before .encode" resumes in seconds.

4. After compaction or resume, trust the file over the summary

Before any edit in a resumed session: re-read the checkpoint, run git status and git diff --stat, and reconcile them. The compacted summary is lossy; the file and the diff are ground truth. Where they disagree, the diff wins and the file gets corrected. Never redo a step marked [x] without evidence it is actually undone.

5. Commit at safe points

When the work is committable and committing is within your brief, a described commit is the strongest checkpoint there is (see git-hygiene). Don't let 400 lines of uncommitted work ride on one fragile context; at minimum list "uncommitted: files A, B, C" in the resume point.

Exit check

At the end of the task the checkpoint holds the final state and the report can be written from it alone. If the task is handed off unfinished, the resume point is the handoff.

Frequently asked questions

What does the Context Checkpoint AI skill do?

Externalize working state to a checkpoint file so long tasks survive context compaction, interruption, and handoff. Use at the start of any task expected to exceed ~30 minutes, touch many files, or run many commands; whenever a phase completes, the plan changes, or a dead end is confirmed; and immediately after any compaction or session resume, before editing. Not for short single-file edits.

Why use Context Checkpoint on TypingMind?

Because you install it once and use it with any model. Context 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 Context Checkpoint in TypingMind?

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

Which AI models can use Context 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 Context Checkpoint?

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

Is the Context Checkpoint AI skill free?

Yes. It is published on GitHub by adamentwistle 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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