| name | context-management |
| version | 2.0.0 |
| description | Proactive context window management via token monitoring, intelligent extraction, and selective rehydration. Use when approaching token limits or needing to preserve essential context. Complements /transcripts and PreCompact hook with proactive optimization. |
Context Management Skill
Purpose
This skill enables proactive management of Claude Code's context window through intelligent token monitoring, context extraction, and selective rehydration. Instead of reactive recovery after compaction, this skill helps users preserve essential context before hitting limits and restore it efficiently when needed.
When to Use This Skill
- Token monitoring: Check current usage and get recommendations
- Approaching limits: Create snapshots at 70-85% usage
- After compaction: Restore essential context without full conversation
- Long sessions: Preserve key decisions and state proactively
- Complex tasks: Keep requirements and progress accessible
- Context switching: Save state when pausing work
- Team handoffs: Package context for others to continue
Quick Start
Check Token Status
User: Check my current token usage
I'll use the context_manager tool to check status:
from context_manager import check_context_status
status = check_context_status(current_tokens=<current_count>)
# Returns: ContextStatus with usage percentage and recommendations
Create a Snapshot
User: Create a context snapshot named "auth-implementation"
I'll use the context_manager tool to create a snapshot:
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
Restore Context
User: Restore context from snapshot <snapshot_id> at essential level
I'll use the context_manager tool to rehydrate:
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
List Snapshots
User: List my context snapshots
I'll use the context_manager tool to list snapshots:
from context_manager import list_context_snapshots
snapshots = list_context_snapshots()
# Returns: List of snapshot metadata dicts
Detail Levels
When rehydrating context, choose the appropriate detail level:
- Essential (smallest): Requirements + current state only (~250 tokens)
- Standard (balanced): + key decisions + open items (~800 tokens)
- Comprehensive (complete): + full decisions + tools used + metadata (~1,250 tokens)
Start with essential and upgrade if more context is needed.
Actions
Action: status
Check current token usage and get recommendations.
Usage:
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}")
Returns:
ContextStatusobject with usage detailsthreshold_status: 'ok', 'consider', 'recommended', or 'urgent'recommendation: Human-readable action suggestion
Action: snapshot
Create intelligent context snapshot.
Usage:
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}")
Returns:
ContextSnapshotobject with metadata- Snapshot saved to
.claude/runtime/context-snapshots/
Action: rehydrate
Restore context from snapshot at specified detail level.
Usage:
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
Returns:
- Formatted markdown text with restored context
- Ready to process and continue work
Action: list
List all available context snapshots.
Usage:
from context_manager import list_context_snapshots
snapshots = list_context_snapshots()
for snapshot in snapshots:
print(f"{snapshot['id']}: {snapshot['name']} ({snapshot['size']})")
Returns:
- List of snapshot metadata dicts
- Includes: id, name, timestamp, size, token_count
Proactive Usage Workflow
Step 1: Monitor Token Usage
Periodically check status during long sessions:
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')
Step 2: Create Snapshot at Threshold
When 70-85% threshold reached, create a named snapshot:
snapshot = create_context_snapshot(
conversation_data=messages,
name='descriptive-name'
)
# Save snapshot ID for later rehydration
Step 3: Continue Working
After snapshot creation:
- Continue conversation naturally
- Let Claude Code compact if needed
- Use
/transcriptsfor full history if desired - PreCompact hook saves everything automatically
Step 4: Rehydrate After Compaction
After compaction, restore essential context:
# 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'
)
Integration with Existing Systems
vs. PreCompact Hook
PreCompact Hook (automatic safety net):
- Triggered by Claude Code before compaction
- Saves complete conversation transcript
- Automatic, no user action needed
- Full conversation export to markdown
Context Skill (proactive optimization):
- Triggered by user when monitoring indicates
- Saves intelligent context extraction
- User-initiated, deliberate choice
- Essential context only, not full dump
Relationship: Complementary, not competing. Hook = safety net, Skill = optimization.
vs. /transcripts Command
/transcripts (reactive restoration):
- Restores full conversation after compaction
- Complete history, all messages
- Used when you need everything back
- Reactive recovery tool
Context Skill (proactive preservation):
- Preserves essential context before compaction
- Selective rehydration, not full history
- Used when you want efficient context
- Proactive optimization tool
Relationship: Transcripts for full recovery, skill for efficient management.
Storage Locations
- Snapshots:
.claude/runtime/context-snapshots/(JSON) - Transcripts:
.claude/runtime/logs/<session_id>/CONVERSATION_TRANSCRIPT.md - No conflicts: Different directories, different purposes
Automatic Management
Context management runs automatically via the post_tool_use hook:
- Monitors token usage every Nth tool use (adaptive frequency)
- Creates snapshots at thresholds (30%, 40%, 50% for 1M models)
- Detects compaction (token drop > 30%)
- Auto-rehydrates after compaction at appropriate level
This happens transparently without user intervention.
Implementation
All context management functionality is provided by:
- Tool:
.claude/tools/amplihack/context_manager.py - Hook Integration:
.claude/tools/amplihack/context_automation_hook.py - Hook System:
.claude/tools/amplihack/hooks/tool_registry.py
See tool documentation for complete API reference and implementation details.
Common Patterns
Pattern 1: Preventive Snapshotting
Check before long operation and create snapshot if needed:
status = check_context_status(current_tokens=current)
if status.threshold_status in ['recommended', 'urgent']:
create_context_snapshot(messages, name='before-refactoring')
Pattern 2: Context Switching
Save state when pausing work on one feature to start another:
# 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')
Pattern 3: Team Handoff
Create comprehensive snapshot for teammate:
snapshot = create_context_snapshot(
messages,
name='handoff-to-alice-api-work'
)
# Share snapshot ID with teammate
# Alice can rehydrate and continue work
Philosophy Alignment
Ruthless Simplicity
- Four single-purpose components in one tool
- On-demand invocation, no background processes
- Standard library only, no external dependencies
- Clear public API with convenience functions
Single Responsibility
- ContextManager coordinates all operations
- Token monitoring, extraction, rehydration in one place
- No duplicate code or scattered logic
Zero-BS Implementation
- No stubs or placeholders
- All functions work completely
- Real token estimation, not fake
- Actual file operations, not simulated
Trust in Emergence
- User decides when to snapshot, not automatic (unless via hook)
- User chooses detail level, not system
- Proactive choice empowers the user
Tips for Effective Context Management
- Monitor regularly: Check status at natural breakpoints
- Snapshot strategically: At 70-85% or before long operations
- Start minimal: Use essential level first, upgrade if needed
- Name descriptively: Use clear snapshot names for later reference
- List periodically: Review and clean old snapshots
- Combine tools: Use with /transcripts for full recovery option
- Trust emergence: Don't over-snapshot, let context flow naturally
Resources
- Tool:
.claude/tools/amplihack/context_manager.py - Hook:
.claude/tools/amplihack/context_automation_hook.py - Philosophy:
.claude/context/PHILOSOPHY.md - Patterns:
.claude/context/PATTERNS.md
Remember
This skill provides proactive context management through a clean, reusable tool. The tool can be called from skills, commands, and hooks. It complements existing tools (PreCompact hook, /transcripts) rather than replacing them. Use it to maintain clean, efficient context throughout long sessions.
Key Takeaway: Business logic lives in context_manager.py, this skill just tells you how to use it.