User: Check my current token usage

from context_manager import check_context_status

status = check_context_status(current_tokens=<current_count>)
# Returns: ContextStatus with usage percentage and recommendations

User: Create a context snapshot named "auth-implementation"

from context_manager import create_context_snapshot

snapshot = create_context_snapshot(
    conversation_data=<conversation_history>,
    name="auth-implementation"
)
# Returns: ContextSnapshot with snapshot_id, file_path, and token_count

User: Restore context from snapshot <snapshot_id> at essential level

from context_manager import rehydrate_from_snapshot

context = rehydrate_from_snapshot(
    snapshot_id="20251116_143522",
    level="essential"  # or "standard" or "comprehensive"
)
# Returns: Formatted context text ready to process

User: List my context snapshots

from context_manager import list_context_snapshots

snapshots = list_context_snapshots()
# Returns: List of snapshot metadata dicts

from context_manager import check_context_status

status = check_context_status(current_tokens=750000)
print(f"Usage: {status.percentage}%")
print(f"Status: {status.threshold_status}")
print(f"Recommendation: {status.recommendation}")

from context_manager import create_context_snapshot

snapshot = create_context_snapshot(
    conversation_data=messages,
    name="feature-name"  # Optional
)
print(f"Snapshot ID: {snapshot.snapshot_id}")
print(f"Token count: {snapshot.token_count}")
print(f"Saved to: {snapshot.file_path}")

from context_manager import rehydrate_from_snapshot

context = rehydrate_from_snapshot(
    snapshot_id="20251116_143522",
    level="standard"  # essential, standard, or comprehensive
)
print(context)  # Display restored context

from context_manager import list_context_snapshots

snapshots = list_context_snapshots()
for snapshot in snapshots:
    print(f"{snapshot['id']}: {snapshot['name']} ({snapshot['size']})")

# The system tracks token burn rate over time
# When checking status, you get predictive insights

status = check_context_status(current_tokens=500000)

# Status includes predictions (when automation is running):
# - Estimated tool uses until 70% threshold
# - Time estimate based on current burn rate
# - Early warning before you hit capacity

# Example output interpretation:
# "At current rate, you'll hit 70% in ~15 tool uses"
# "Consider creating a checkpoint before your next major operation"

# In your statusline script, check context health:
# The automation state file contains health status

# Example statusline addition:
if [ -f ".claude/runtime/context-automation-state.json" ]; then
    LAST_PCT=$(jq -r '.last_percentage // 0' .claude/runtime/context-automation-state.json)
    if [ "$LAST_PCT" -lt 30 ]; then
        echo "[CTX:OK]"
    elif [ "$LAST_PCT" -lt 50 ]; then
        echo "[CTX:WATCH]"
    elif [ "$LAST_PCT" -lt 70 ]; then
        echo "[CTX:WARN]"
    else
        echo "[CTX:CRITICAL]"
    fi
fi

# Create snapshot with priority awareness
snapshot = create_context_snapshot(
    conversation_data=messages,
    name='feature-checkpoint'
)

# Essential level (~200 tokens): Only high priority content
# Standard level (~800 tokens): High + medium priority
# Comprehensive level (~1250 tokens): Everything

# Start minimal, upgrade as needed:
context = rehydrate_from_snapshot(snapshot_id, level='essential')

# The automation tracks consumption velocity
# Adaptive checking frequency based on burn rate:

# Low burn rate (< 1K tokens/tool): Check every 50 tools
# Medium burn rate (1-5K tokens/tool): Check every 10 tools
# High burn rate (> 5K tokens/tool): Check every 3 tools
# Critical zone (70%+): Check every tool

# This means:
# - Normal development: Minimal overhead (checks rarely)
# - Large file operations: Increased monitoring
# - Approaching limits: Continuous monitoring

# Automatic snapshot triggers (already implemented):
# - 30% usage: First checkpoint created
# - 40% usage: Second checkpoint created
# - 50% usage: Third checkpoint created (for 1M models)

# For smaller context windows (< 800K):
# - 55% usage: First checkpoint
# - 70% usage: Second checkpoint
# - 85% usage: Urgent checkpoint

# After compaction detected (30% token drop):
# - Automatically rehydrates from most recent snapshot
# - Uses smart level selection based on previous usage

status = check_context_status(current_tokens=current)

if status.threshold_status == 'consider':
    # Usage at 70%+ - consider creating snapshot
    print("Consider creating a snapshot soon")
elif status.threshold_status == 'recommended':
    # Usage at 85%+ - snapshot recommended
    create_context_snapshot(messages, name='current-work')
elif status.threshold_status == 'urgent':
    # Usage at 95%+ - create snapshot immediately
    create_context_snapshot(messages, name='urgent-backup')

snapshot = create_context_snapshot(
    conversation_data=messages,
    name='descriptive-name'
)
# Save snapshot ID for later rehydration

# Start minimal
context = rehydrate_from_snapshot(
    snapshot_id='20251116_143522',
    level='essential'
)

# If more context needed, upgrade to standard
context = rehydrate_from_snapshot(
    snapshot_id='20251116_143522',
    level='standard'
)

# For complete context, use comprehensive
context = rehydrate_from_snapshot(
    snapshot_id='20251116_143522',
    level='comprehensive'
)

status = check_context_status(current_tokens=current)
if status.threshold_status in ['recommended', 'urgent']:
    create_context_snapshot(messages, name='before-refactoring')

# Pausing work on Feature A
create_context_snapshot(messages, name='feature-a-paused')

# [... work on Feature B ...]

# Resume Feature A later
context = rehydrate_from_snapshot('feature-a-snapshot-id', level='standard')

snapshot = create_context_snapshot(
    messages,
    name='handoff-to-alice-api-work'
)
# Share snapshot ID with teammate
# Alice can rehydrate and continue work