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Checkpoint

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pedrohcgs
checkpoint

Save a structured state snapshot before stopping or handing off. Captures the active plan, recent decisions, file pointers (with line numbers), open questions, and the next 1–3 actions into a checkpoint file under `quality_reports/checkpoints/`. Optionally proposes `[LEARN]` entries to add to MEMORY.md. Use when user says "checkpoint", "save state", "snapshot before I stop", "where am I", "wrap up the session for handoff", or before a long break / model switch / collaborator handoff. Companion to (NOT replacement for) the narrative session-log workflow.

Overview

Publisherpedrohcgs
Repositoryclaude-code-my-workflow
Skill namecheckpoint
Stars
1.6K
Forks
3.1K
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 pedrohcgs 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.
    https://github.com/pedrohcgs/claude-code-my-workflow/tree/main/.claude/skills/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/pedrohcgs/claude-code-my-workflow.git /tmp/claude-code-my-workflow
mkdir -p .claude/skills
cp -r /tmp/claude-code-my-workflow/.claude/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 — Structured Session Handoff

Produce a state snapshot that the next session (yours, or a collaborator's, or a fresh-context reboot) can resume from in under a minute. The narrative quality_reports/session_logs/ continues to live separately — /checkpoint writes the structured side: facts, file pointers, and next-actions.

When to use

  • Before a long break, model switch (Opus ↔ Sonnet ↔ Haiku), or end of a working day.
  • Before auto-compaction would otherwise discard mid-plan context (paired with the PreCompact hook).
  • Before handing off to a collaborator on the same repo.
  • After completing a chunk of a multi-session plan, when "where am I" is the first question the next session will ask.

When NOT to use

  • For the narrative what happened — that lives in quality_reports/session_logs/ (see .claude/rules/session-logging.md).
  • For commit messages — those go through /commit, which writes its own structured commit body.
  • For decisions about alternatives — those go to templates/decision-record.md via quality_reports/decisions/.

The three artifact types are complementary: session-log = narrative, decision-record = trade-off captured, checkpoint = state to resume from.

Workflow

PHASE 1 — Gather state

Read, in this order:

  1. Most recent plan — ls -t quality_reports/plans/*.md | head -1. Extract: status (DRAFT / APPROVED / COMPLETED), title, top-level files-to-modify list, and any line that begins with "Open questions" / "Risks" / "Next".
  2. Most recent session log — ls -t quality_reports/session_logs/*.md | head -1. Extract: latest "Next steps" or "Blockers" lines.
  3. MEMORY.md root — read the table of [LEARN] entries already on disk so you don't propose duplicates.
  4. Git state — git log --oneline -20, git status -s, git branch --show-current. Capture: current branch, last 5 subjects, uncommitted file count.
  5. Working files — git diff --stat HEAD to see which files changed in this session (skip if branch is freshly cut; just say "no in-session edits").
  6. Active TODOs — if a TodoWrite list is in flight in this session, capture the in-progress + next-pending items.
  7. In-flight background work — anything this session started and did not wait for: a long-running compute job, a scheduled routine (.claude/references/scheduled-routines.md), a queued render or compile, an external review still out with a referee or another model. For each, record four things: what is running, where its artifacts land, the command that checks on it, and the verdict that ends it.

If any of these reads fails (file missing), record "(none on disk)" rather than fabricating content.

A checkpoint that omits a running job orphans it. The next session sees no trace of the work, the artifacts land in a directory nobody is watching, and the job is either re-launched from scratch or quietly abandoned half-finished — both expensive, both invisible.

PHASE 2 — Write the checkpoint

Write only what this session established. The next session is handed this file automatically (session-handoff.py), so anything invented here arrives there as fact.

  • Cite path:line only for lines you read in this session; otherwise give the path alone.
  • Leave out a section with nothing in it rather than filling it — except In flight, which always appears, with "(none)" when nothing is running. A quiet session gets a short file and no [LEARN] proposals.
  • Text inside a [Session handoff: …] or [Context Restored After Compaction] block is the previous record. Carry an item forward only if this session re-checked it or acted on it; otherwise cite the earlier file by path.
  • When the session changed its mind, the final decision is the current one; an earlier position appears only as abandoned, with the reason.

Write to quality_reports/checkpoints/YYYY-MM-DD_$ARGUMENTS.md (slug from $ARGUMENTS; if no arg, derive from the active plan's title and warn the user). The file uses this template:

markdown
---
date: YYYY-MM-DD
branch: [current-branch]
plan: [path to active plan, or "(none)"]
session-log: [path to most recent session log, or "(none)"]
status: in_progress | paused | ready-to-merge
---

# Checkpoint — [short topic]

## Goal (one sentence)
[What this work is trying to accomplish]

## Where I am (one paragraph)
[Last completed step, current step, what's just-not-yet-done. Bullet points OK.]

## File pointers
[Concrete references to where the next session should resume — up to 8. `path:line` only for lines read in this session; otherwise the path alone.]
- `.claude/skills/checkpoint/SKILL.md:42` — body draft, needs trigger-phrase tightening
- `quality_reports/plans/[slug].md:135` — verification section to refresh after impl
- `CHANGELOG.md` — Unreleased section, v1.8.0 entry not yet drafted

## In flight
[Jobs still running that this session did not wait for. One row each; write "(none)" if nothing is running — an empty section reads as an oversight.]

| What is running | Artifacts land in | Check with | Ends when |
|---|---|---|---|
| overnight parameter sweep | `output/sweep/` | `ls output/sweep \| wc -l` | 500 result files, no `errors.log` |
| external referee consult | `quality_reports/oracle_audits/2026-04-27_lemma3/` | `oracle session lemma3-r1 --render` | transcript archived + adjudicated |

## Recent decisions
[2–5 bullet points of *why* we did what we did this session. Things that wouldn't be obvious from the diff. Skip if none — do not pad.]

## Open questions
[Specific things you'd ask if someone else picked this up. Mark each as Q1, Q2 …]

## Next 1–3 actions
[Imperative form. Concrete. The next session opens this file and starts here.]
1. [...]
2. [...]
3. [...]

## Resume prompt
> Resuming from checkpoint `quality_reports/checkpoints/[filename]`. Read it, then continue with action 1.

Keep the file under ~80 lines. If state is too large for that, the plan file (not the checkpoint) is the right place; checkpoint is a thin index pointing back at the plan.

PHASE 3 — Propose memory updates (skip if --no-memory)

Surface 0–3 candidate [LEARN] entries this session generated. Don't write to MEMORY.md without user approval — this is a propose-then-apply step:

For each candidate, present:

[LEARN:category] proposed: <one-line headline>
Why: <one sentence on what makes this non-obvious>
Apply where: <which future situations would benefit>

If the user says "yes" / "all" / "1 and 3" — append to MEMORY.md (root, the committed one) using the [LEARN] format. If the candidate is machine-specific (paths, tool versions, personal preference), let native auto memory hold it instead (machine-local, per .claude/rules/meta-governance.md).

Stay below 3 candidates. If you have more, the session was probably under-narrated — flag it and recommend a session-log update instead.

PHASE 4 — Output summary

Print, to chat:

✓ Checkpoint saved: quality_reports/checkpoints/YYYY-MM-DD_<slug>.md
  Branch: <branch>     Status: <in_progress|paused|ready-to-merge>
  Active plan: <path or none>     Open questions: <count>
  Resume: the next fresh `claude` here receives this checkpoint once from the session-handoff
          hook (within 7 days, unless a newer checkpoint or /compress-session note is written
          first). After that, or in `claude --continue` (which does not deliver it), tell Claude
          to read this file or paste its "Resume prompt".

If memory candidates were proposed, summarise which (if any) the user accepted.

Cross-references

  • .claude/rules/session-logging.md — narrative companion. Do not duplicate — the checkpoint references the latest session log by path; it does not re-tell the session story.
  • .claude/rules/plan-first-workflow.md — checkpoint reads the active plan; if no plan exists, recommend the user enter plan mode before invoking /checkpoint.
  • templates/decision-record.md — for why we chose A over B, not for where we are.
  • .claude/references/scheduled-routines.md — routines that outlive the session; anything running there belongs in the "In flight" slot.
  • .claude/hooks/pre-compact.py — when CLAUDE_PRECOMPACT_BLOCK_ON_DRAFT=1 is set, the PreCompact hook will block compaction once per DRAFT plan. /checkpoint is the right thing to run when that block fires.

Examples

Example 1 — End-of-day handoff

User says: "checkpoint v180-polisci" Actions:

  1. Read quality_reports/plans/2026-04-27_v180-polisci-apr2026.md (active, DRAFT).
  2. Read quality_reports/session_logs/2026-04-27_v180-polisci-apr2026.md.
  3. Capture: branch feat/v1.8.0-polisci-apr2026, 4 commits ahead of main, 8 files modified.
  4. Write quality_reports/checkpoints/2026-04-27_v180-polisci.md with file pointers to the half-drafted methods-referee.md and the un-started journal-profiles.md poli-sci block.
  5. Propose 1 candidate [LEARN:scope] entry on the linear-cost of disciplinary breadth. Result: Next session: start a fresh claude — the handoff hook hands it quality_reports/checkpoints/2026-04-27_v180-polisci.md — and start at action 1.

Example 2 — Mid-plan model switch

User says: "I want to switch to Sonnet for the cheap doc edits — checkpoint first" Actions:

  1. Capture state.
  2. Write checkpoint with status: paused.
  3. Skip memory proposal (small lift — just resuming on a different model). Result: State is on disk; the Sonnet session reads the checkpoint and continues without reloading the full plan.

Troubleshooting

No active plan found. /checkpoint will still write a thin checkpoint with plan: (none), but the right move is usually to enter plan mode first — checkpoints without a plan reference are weak.

Topic-slug missing. If $ARGUMENTS is empty, derive from the active plan filename (strip date prefix). If both are missing, prompt the user for one rather than fabricating.

Output too long. Trim the "Recent decisions" and "Open questions" first. Plans go in plan files; the checkpoint should fit on a screen.

Frequently asked questions

What does the Checkpoint AI skill do?

Save a structured state snapshot before stopping or handing off. Captures the active plan, recent decisions, file pointers (with line numbers), open questions, and the next 1–3 actions into a checkpoint file under `quality_reports/checkpoints/`. Optionally proposes `[LEARN]` entries to add to MEMORY.md. Use when user says "checkpoint", "save state", "snapshot before I stop", "where am I", "wrap up the session for handoff", or before a long break / model switch / collaborator handoff. Companion to (NOT replacement for) the narrative session-log workflow.

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/pedrohcgs/claude-code-my-workflow/tree/main/.claude/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 pedrohcgs 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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