--- name: context-checkpoint description: "Save, compress, version, encrypt, and resume full chat context when approaching token/message limits. Use when the conversation is long, the user says save our progress or context is running out, approaching token limits, resuming a session across chat windows or different AI models, creating named bookmarks, merging parallel chats, or exporting conversation history as knowledge assets. Supports commands like /save, /resume, /resume light, /bookmark, /list bookmarks, /recover_last, /merge, /diff, /export." metadata: author: Himanshu version: '2.0' --- # Context Checkpoint Skill ## When to Use This Skill Trigger this skill when any of the following are true: - User says: "save context", "we're running out of tokens", "checkpoint this", "save progress", "I want to continue in a new window" - User types a slash command: `/save`, `/resume [code]`, `/resume light`, `/bookmark "name"`, `/list bookmarks`, `/recover_last`, `/merge`, `/diff`, `/export` - Token usage is estimated at >= 80% of the context window (auto-trigger; show dashboard first) - The conversation has exceeded ~40 messages or ~15,000 words - A complex task (coding, research, debugging) is mid-flight and must be resumed later Read the relevant reference files before acting: | Task | Read | |------|------| | Saving context | `references/save-pipeline.md` | | Compression and forgetting | `references/compression.md` | | Cross-model resume | `references/cross-model.md` | | Encryption and privacy | `references/security.md` | | Versioning and bookmarks | `references/versioning.md` | | Resume modes and commands | `references/resume-modes.md` | | Health dashboard | `references/dashboard.md` | | Analytics and export | `references/analytics.md` | | Emergency and self-healing | `references/recovery.md` | --- ## Core Commands | Command | Action | |---------|--------| | `/save` | Full save — compress, sign, export `.md` | | `/save encrypt` | Save with GPG/password encryption to `.md.gpg` | | `/save light` | Aggressive compression, minimal file size | | `/resume [code or file]` | Full resume from code or uploaded `.md` | | `/resume light` | Inject only critical state, skip full transcript | | `/bookmark "name"` | Create a named checkpoint mid-conversation | | `/list bookmarks` | Show all bookmarks with token cost and date | | `/diff v1 v2` | Show what changed between two snapshots | | `/merge` | Merge two parallel chat contexts | | `/recover_last` | Emergency recovery of incomplete save | | `/export pdf or timeline or quiz` | Convert saved context to other formats | | `/health` | Show token usage dashboard | | `/expiry 7d` | Set TTL on the next save | --- ## Save Pipeline (Overview) When `/save` is triggered, follow these steps in order: ### Step 1 — Show Health Dashboard Before saving, always show the Context Health Dashboard (see `references/dashboard.md`). This lets the user decide compression level. ### Step 2 — Estimate Output Size Before compressing, tell the user: > "Saving will produce approximately N characters (~X KB). Choose compression level: `low` (fast, full fidelity) / `medium` (drop low-value turns) / `high` (aggressive summarisation + code hashing)." Default to `medium` if the user does not respond. ### Step 3 — Compression and Selective Forgetting Read `references/compression.md`. Drop low-value messages. Optionally hash long code blocks. Produce compressed conversation representation. ### Step 4 — Assemble Checkpoint File Build `context-checkpoint.md` with this exact top-level structure: ``` # Context Checkpoint: [Title] Date: [ISO date] Model: [model name or "unknown"] Version: [vN — increment on each save] Checksum: [SHA-256 of Sections 1-6, computed last] TTL: [expiry date if set, else "none"] Summary: [one-sentence description] ## 1. Objectives ## 2. Key Decisions and Conclusions ## 3. Artifacts [full code reproduction] ## 4. Current State [what works / broken / last error] ## 5. Open Issues and Next Steps [numbered, priority order] ## 6. Continuation Prompt [paste-ready, model-agnostic] ## 7. Pending Actions [unexecuted tasks, if any] ## 8. Bookmarks Index [named checkpoints, if any] ## 9. Redundancy Block [duplicate of critical facts for self-healing] ## 10. Checksum Block [SHA-256 verification] ``` For format details of each section, see `references/save-pipeline.md`. ### Step 5 — Self-Healing Integrity Compute SHA-256 of the full file content (Sections 1-9). Write it into Section 10 as: ``` SHA256: REDUNDANCY: [repeat 3-5 most critical facts verbatim] ``` See `references/recovery.md` for checksum computation and repair logic. ### Step 6 — Versioning Save as `context-checkpoint_v[N].md`. Maintain rolling history of last 5 versions. Update `_master_context_index.md` with a one-line entry for this snapshot. See `references/versioning.md`. ### Step 7 — Encryption (if requested) If `/save encrypt` was used, apply encryption before writing to disk. See `references/security.md`. ### Step 8 — Output and Instructions Present the file for download. Then tell the user exactly: 1. Download `context-checkpoint_vN.md` 2. Open a new chat window 3. Paste the Continuation Prompt (Section 6) as the first message 4. Then paste or upload the Artifacts section (Section 3) 5. If resuming on a different AI model, paste the model-specific adapter from Section 6 (see `references/cross-model.md`) --- ## Quality Checklist Before finalising the file, verify every item: - [ ] Token dashboard was shown before saving - [ ] Output size was estimated and compression level confirmed - [ ] All code artifacts reproduced in full — no truncation or placeholder comments - [ ] Continuation prompt is self-contained and model-agnostic - [ ] All error messages and tracebacks reproduced exactly - [ ] Pending actions (unexecuted tool calls) captured in Section 7 - [ ] Checksum written and redundancy block populated - [ ] Version number incremented; `_master_context_index.md` updated - [ ] TTL set if user requested expiry - [ ] Encryption applied if `/save encrypt` was used --- ## Adaptive Behaviour Track the following across sessions (store in `_master_context_index.md`): - How often the user actually resumes from saved context - Average token usage at time of save - Preferred compression level If the user has never resumed from a saved context across 5+ saves: stop auto-triggering at 80% — only save when explicitly commanded. If the user resumes frequently and the save threshold feels late: lower auto-trigger to 70%. Log the adapted threshold in `_master_context_index.md` under `adaptive_config`. --- ## Emergency Hard Stop If the token limit is hit unexpectedly mid-response: 1. Immediately save whatever is in the buffer 2. Mark the file header: [INCOMPLETE — possible message loss] 3. Log which messages may be truncated in Section 4 (Current State) 4. Output recovery instructions (see `references/recovery.md`) --- ## Notes - Never store encryption keys — user provides them each time - For voice/multimodal sessions, see `references/analytics.md` for audio fingerprint guidance - The skill works on any AI model — the continuation prompt in Section 6 is always model-agnostic - For collaborative handovers, generate a read-only summary variant (omit Section 3 raw code if confidential)