Context Health Check

When this skill is triggered, perform the following assessment and output a report.

Token Estimation Method

LLMs cannot directly read consumed token counts. Use these proxy formulas:

Estimation Formula

Consumed ≈ System Overhead + Σ(Per-turn Consumption)

System Overhead ≈ 15K-25K tokens (system prompt + skills metadata + workspace info)

Per-turn Consumption ≈ User message (~200-500) 
           + Tool call results (each ~500-5000, large file reads ~3000-8000)
           + Assistant response (~500-2000)

High-Consumption Operation Reference

Model Context Windows

⚠️ MUST read

USER.md

model preference

Assessment Process

1. Identify current model (read

   USER.md

   →

   model preference

   field. If not found, default to Claude 200K. Do not guess, do not use Gemini 1M)
2. Count conversation turns (user↔assistant interaction count)
3. Estimate tool call token consumption (accumulate per the table above)
4. Calculate total estimated consumption
5. Calculate remaining percentage = (available window - consumed) / available window
6. Assign health level

Health Levels

Quick Judgment Rules of Thumb (when you don't want to calculate)

For Claude 200K window:

• ≤5 turns + 1 large file → 🟢
• 10-15 turns + 2-3 large files + multiple code edits → 🟡
• >18 turns or >5 large file reads or starting to forget early content → 🔴

Output Format

## 🧠 Context Health Check

**Model**: [Claude/GPT-4o/Gemini/...] (context window XK)

| Metric | Current Value | Estimated Tokens |
|--------|:------------:|:----------------:|
| System overhead | — | ~20K |
| Conversation turns | X turns | ~YK |
| Large file reads | [list files + line counts] | ~ZK |
| Tool calls | ~N calls | ~WK |
| **Total estimated** | — | **~TK** |
| **Remaining estimated** | — | **~RK (P%)** |

**Health Level**: 🟢/🟡/🔴

**Recommendation**: [specific advice]
**Estimated remaining capacity**: ~M normal turns / ~L large file reads

Defensive Auto-Save

Core problem: 🔴 detection depends on the Agent having an opportunity to execute — when context suddenly overflows, the Agent has no chance to save anything. Solution: Don't wait for 🔴 — proactively save every 10 turns.

Rules

1. Every 10 interaction turns (one user↔assistant round = 1 turn), Agent MUST silently update

   memory/CRASH_CONTEXT.md

2. Silent = don't interrupt the user. No announcement needed, no confirmation needed. Complete during response or between tool calls
3. Incremental update: Only update changed fields, don't full-rewrite
4. Format matches what's written during 🔴 detection (see Additional Rule #1 below)
5. Delete after recovery: After

   /resume crash

   successfully recovers, delete

   CRASH_CONTEXT.md

   (avoid stale data)
6. Trigger timing checklist:
   • Turn 10 → First write ✅
   • Turn 20 → Update ✅
   • Turn 30 → Update ✅ (usually approaching 🟡/🔴 by now)
   • 🔴 detection triggered → Final update (including "suggested next steps") ✅

Defense Layers

/ctx

/resume crash

convo/

Additional Rules

1. When 🔴 danger is detected, automatically execute memory snapshot + crash recovery file (MUSE compact:before protocol):
   • Write current progress to

     memory/YYYY-MM-DD.md

     (don't wait for user /bye)
   • Update

     task.md

     progress markers
   • Write

     memory/CRASH_CONTEXT.md

     (structured recovery file, auto-read by next

     /resume crash

     ):

     # Crash Context — YYYY-MM-DD HH:MM
     ## Current Role: [strategy/build/growth/qa]
     ## Current Task: [what was being done]
     ## Unfinished: [list]
     ## Key Decisions: [decisions made this session]
     ## User's Last Question: [if any]
     ## Suggested Next Steps: [what to do after recovery]
     

   • Then notify the user:

     ⚠️ Context is about to overflow! Auto-saved:
     - memory/YYYY-MM-DD.md ✅
     - memory/CRASH_CONTEXT.md ✅
     Please end this conversation. Use `/resume crash` in a new conversation to auto-recover.
     To export this conversation, save to: convo/YYMMDD/YYMMDD-NN-desc_CRASH.md
     

2. If current conversation has not loaded any status files and is ≤3 turns, report 🟢 directly without detailed calculation
3. Natural language triggers also apply ("how much context left?", "context enough?", etc.)
4. If you detect yourself forgetting early conversation content or repeating yourself, even if the formula says there's room left, report 🔴 directly

🆕 Session Checkpoint (v2.14.0 — OpenViking-inspired)

Inspired by OpenViking's session compression — auto-checkpoint mid-conversation to prevent context loss. Complements Defensive Auto-Save (CRASH_CONTEXT.md): Auto-Save = crash recovery safety net, Session Checkpoint = quality preservation.

Why

Long conversations degrade quality because the Agent loses track of early context. By the time

/bye

When to Checkpoint

What to Write

File:

memory/YYYY-MM-DD.md

Format (compressed — use Semantic Compression rules from

/bye

## 📍 Checkpoint (HH:MM, ~turn N)
- 主线: [1-2 sentence summary of work so far]
- 决策: [key decisions if any]
- 状态: [current task status]

Rules

1. Silent: Do NOT announce checkpoints to the user. Do NOT ask for confirmation. Write silently between tool calls
2. Append-only: Never overwrite existing memory content. Always append
3. Compressed: Use the 5:1 compression ratio — max 3-4 lines per checkpoint
4. Idempotent: If the same milestone was already checkpointed, skip (check last checkpoint's content)
5. No performance impact: Checkpoint during response generation or between tool calls, never adding a delay
6. Checkpoint counter: Track

   _checkpoint_count

   mentally. First checkpoint = turn ~15, then every ~15 turns after

Interaction with Other Systems

/bye

/bye

/bye

/resume