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herreraspaintingba-hubherreraspaintingba-hub/second-brain-starter-kit/skills/session-checkpoint/SKILL.md

session-checkpoint

Skill0 starsChanged 41 days agoMITView on GitHub

Saves the live working state of a long session to the vault (one rolling file per project), so nothing is lost when the conversation gets long and the AI's memory of its start is compressed. Runs on its own every 15 to 20 substantial exchanges, at every milestone, before any heavy step, and right after a compression to recover. Also on request: "checkpoint", "save state", "don't lose track", "guarda el estado", "haz un checkpoint", "no pierdas el hilo". Bilingual EN+ES.

7 other projects use a version of this.

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The file

We split this skill into the rules it gives an agent

6 rules. 0 are common, 5 are distinctive, and 1 is very specific to this project.

  • 0

    Common

    Most of the 6 edited versions we read keep these.

  • 5

    Distinctive

    This author's own choices. Worth a look.

  • 1

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    When a conversation gets long, the AI host compresses its earlier part to make room.

    line 19
  2. Distinctive:

    After that, the AI works from a summary: decisions blur, file names are forgotten, questions get asked twice.

    line 20
  3. Distinctive:

    Every 15 to 20 substantial exchanges of real work (not greetings or "ok").

    line 36
  4. Distinctive:

    When a decision changes direction.

    line 38
  5. Distinctive:

    When the user asks.

    line 40
  6. Very specific:

    Read 06 Session Logs/Active Session State, <Project>.md for the active project.

    line 50

“Common” is measured: kept by at least 6 in 10 of the 6 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Session Checkpoint: a save point for long sessions

Why it exists

When a conversation gets long, the AI host compresses its earlier part to make room. After that, the AI works from a summary: decisions blur, file names are forgotten, questions get asked twice. A checkpoint is a save point in a video game: a detailed snapshot of where the work stands, written to the vault, read back after a compression.

It does not replace save-to-obsidian:

SkillWhenSavesWhere
session-checkpointDuring the sessionLive working state06 Session Logs/Active Session State, <Project>.md, overwritten each time
save-to-obsidianAt the endPermanent knowledge and the session logSections 01 to 06, permanent
project-doorAt the endThe project's current state09 MOCs/Door, <Project>.md

When to checkpoint

  • Every 15 to 20 substantial exchanges of real work (not greetings or "ok").
  • At a milestone ("part 1 done, starting part 2").
  • When a decision changes direction.
  • Before a heavy step (a sub-agent, a long build) and right after it returns.
  • When the user asks.

Sessions under about 15 exchanges do not need one.

Recovering after a compression

You get no explicit signal. Signs: the start of the conversation feels summarized, you cannot recall an exact name or decision, or you are about to ask something already answered. Then:

  1. Read 06 Session Logs/Active Session State, <Project>.md for the active project.
  2. Tell the user: "The conversation was compressed; I re-read our checkpoint. We are at: ..." in one or two lines.
  3. Continue from there. When in doubt, re-read; it costs seconds.

The file

One file per project, overwritten in place: 06 Session Logs/Active Session State, <Project>.md. Never one shared file for all projects: two projects worked the same day would erase each other, and nobody notices until they try to resume. If the active project is unclear, ask before writing.

No .bak copies next to it. The file only reflects the current state, not a history.

---
title: "Active Session State, <Project>"
type: checkpoint
project: <slug>
date: YYYY-MM-DD
time: "HH:MM"
description: "Rolling state of the current <Project> session; read after a compression."
tags: [checkpoint]
status: active
---

# Active Session State, <Project>

## Goal of the session
What the user asked for, in their words.

## Where we are
Done, in progress, next. Specific: names, numbers, paths.

## Decisions so far
- Decision, and why (the why is what matters after a compression).

## Key details
Exact values that would take time to find again: file paths, IDs, names, settings.

## Files created or changed
- path: what changed

## Task list
- [x] done
- [ ] pending

## Open questions or blockers
## Preferences the user expressed this session

Get the time from the clock (date "+%Y-%m-%d %H:%M"), never guess it. Then validate:

python3 "{{VAULT_PATH}}/.brain/tools/check_frontmatter.py" "06 Session Logs/Active Session State, <Project>.md"

Writing guidelines

  • Specific beats generic. "Door for Garden created, sections 1 to 3 filled, numbers pending from the seed invoice" beats "working on the garden".
  • Keep the why. "We chose X because Y" survives a compression; "we chose X" does not.
  • Scannable. Headings and short lines; it is read in a hurry.
  • A snapshot, not a transcript.
  • It is a working file: lean formatting is fine, and it does not need links beyond the Door.

Changelog

  • 2.0.0 (2026-10-02): New in the kit. One rolling file per project, recovery steps, validated frontmatter.

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