checkpoint

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Save a compact continuation checkpoint so current work survives context compaction or a later session. Use when the active context is becoming long or work must pause without changing client or machine ownership. For an explicit cross-session/client transfer, use $handoff.

AI & Automation 144 stars 27 forks Updated 3 days ago MIT

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Skill Content

# Checkpoint — Save Session State Snapshot the current session's progress to a YAML file so it can be restored after context compaction or in a new session. This is NOT a session log (narrative) or context save (profile/focus) — it's a machine-readable state dump for continuity. ## When to Use - Before a session is likely to hit context compression (many tool calls, long outputs) - At phase boundaries in multi-phase work - When the user says "save state", "checkpoint", "save progress" - Proactively when you sense context is getting large — don't wait to be asked ## When This Skill Is Invoked ### Step 1: Gather State Collect the following from the current conversation context: 1. **Project** — which project directory are we working in? 2. **Task** — what was the user's original request this session? 3. **Files modified** — list every file you've created or edited this session (use `git diff --name-only` if in a git repo, plus any new untracked files) 4. **Decisions made** — key choices and their rationale (e.g., "chose LaTeX over Markdown because...", "used sonnet for cron jobs to reduce cost") 5. **Current step** — where in the plan are we? What just completed? What's next? 6. **Blocked items** — anything that couldn't be completed and why 7. **Open questions** — unresolved ambiguities that need user input ### Step 2: Write Checkpoint Write to `.planning/checkpoint.yaml` in the active project directory. If no project directory is obvious, write to the Task Management...

Details

Author
flonat
Repository
flonat/flonat-research
Created
7 months ago
Last Updated
3 days ago
Language
Python
License
MIT

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