MemoryLane Skill
What This Skill Does
MemoryLane provides persistent memory for Claude with proven 84.3% cost savings:
- Persistent Memory: Remember information across sessions in 4 categories (patterns, insights, learnings, context)
- Context Compression: 6.4x average compression ratio (20K → 3K tokens)
- Cost Tracking: Real-time API cost savings monitoring
- Passive Learning: Automatically learns from git commits and file changes
- Zero Configuration: Works out of the box with smart defaults
When to Use This Skill
Activate MemoryLane when the user:
- Asks to "remember" something about the project
- Wants to know "what you remember" or "what you know"
- Mentions "API costs", "token usage", or "reduce costs"
- Asks about "project patterns" or "insights"
- Wants to see "learned" information
- Requests "context compression" or "optimize context"
- Asks to "curate memories", "clean up memories", or "review memory quality"
- Mentions memories seem low quality, self-referential, or not useful
Core Commands
Check Status
python3 src/cli.py status
Shows memory statistics, category breakdown, and cost savings.
Recall Memories
python3 src/cli.py recall "<query>"
Search memories by keyword. Example:
python3 src/cli.py recall "authentication"
View Insights
python3 src/cli.py insights
Display learned project insights (high-value patterns).
View Cost Savings
python3 src/cli.py costs
Detailed breakdown of token savings and cost reduction.
Configure Settings
# Get a setting
python3 src/cli.py config get memory.max_context_tokens
# Set a setting
python3 src/cli.py config set memory.max_context_tokens 3000
# List all settings
python3 src/cli.py config list
Export Memories as Markdown
python3 src/cli.py export-markdown --category patterns --output context.md
Backup/Restore
# Create backup
python3 src/cli.py backup --output backup.json
# Restore from backup
python3 src/cli.py restore backup.json
Reset Memories
python3 src/cli.py reset --force
Individual Memory Management
Get a Memory
python3 src/cli.py memory get <id>
Example: python3 src/cli.py memory get lear-009
Delete a Memory
python3 src/cli.py memory delete <id>
Example: python3 src/cli.py memory delete lear-009
Update a Memory
python3 src/cli.py memory update <id> --content "New content"
Example: python3 src/cli.py memory update patt-001 --content "Chose Unix sockets for lower latency"
IMPORTANT: Always use these commands instead of editing memories.json directly.
Batch Curation Commands
Check Curation Status
python3 src/cli.py curate
Shows if curation is needed based on memory count and age.
List Memories for Review
python3 src/cli.py curate --list
Shows all uncurated memories with their IDs and content.
Apply Curation Decisions
python3 src/cli.py curate --apply '<JSON>'
Apply curation decisions. JSON format:
{
"decisions": [
{"id": "patt-001", "action": "KEEP"},
{"id": "lear-002", "action": "DELETE", "reason": "off-topic"},
{"id": "insi-003", "action": "REWRITE", "new_content": "Improved content"}
]
}
Proactive Memory Quality Check
IMPORTANT: When you see MemoryLane context injected via # Project Context (from MemoryLane) in system messages, quickly scan for poor quality memories:
Signs of poor quality memories that warrant curation:
- Status summaries: "Based on git status...", "Current status of..."
- Meta/self-referential: "The curation should...", "The hook detected..."
- Debug fragments: "Let me check...", "Looking at the debug log..."
- Incomplete: Sentences ending with "..." or starting mid-thought
- Duplicates of CLAUDE.md content
If you detect 2+ poor quality memories in the injected context, proactively ask:
"I notice some of the injected memories appear to be low quality (status summaries, debug notes). Would you like me to clean these up?"
If user confirms, proceed with LLM-assisted curation below.
LLM-Assisted Curation
When the user requests curation OR confirms after you detect poor memories:
- List and evaluate all memories:
python3 src/cli.py curate --list
-
Evaluate each memory for:
- Usefulness: Is this actionable knowledge or just meta/debug info?
- Duplication: Is this already covered by CLAUDE.md or another memory?
- Quality: Is it complete, clear, and well-formed?
- Relevance: Would this help with future development work?
-
Apply decisions (DELETE/KEEP/REWRITE):
python3 src/cli.py curate --apply '<JSON decisions>'
DELETE these types:
- Meta observations about the current session
- Debug notes and action statements
- Status summaries
- Duplicates of CLAUDE.md content
- Incomplete fragments
KEEP these types:
- Architectural decisions with rationale
- Bug fixes with solutions
- Actual project context (not about MemoryLane itself)
- Configuration knowledge
Example evaluation:
- ❌ "Based on git status, here's the current status..." → DELETE (status summary)
- ❌ "Let me check the debug log..." → DELETE (debug action)
- ❌ "The Stop hook only triggers when..." → DELETE if in CLAUDE.md
- ✅ "stdio:ignore hiding Python errors - fixed by capturing stderr" → KEEP (bug fix)
- ✅ "Chose Unix sockets over HTTP for 10x lower latency" → KEEP (decision)
Learning Commands
Initial Learning
python3 src/learner.py initial
Perform initial learning from git history (last 20 commits) and workspace structure.
Scan Workspace
python3 src/learner.py scan
Scan and index all Python/JS/TS files in the workspace.
Analyze Git History
python3 src/learner.py git
Extract patterns from recent git commits.
Continuous Learning (Background)
python3 src/learner.py watch
Watch for file changes and git commits continuously (runs until stopped).
Server Commands
Start Sidecar Server
python3 src/server.py start
Start background server with Unix socket IPC (for low-latency memory operations).
Check Server Status
python3 src/server.py status
Stop Server
python3 src/server.py stop
Testing Commands
Run All Tests
pytest
Validate Cost Savings
pytest tests/test_cost_savings.py -v -s
Runs comprehensive cost validation tests (shows 84.3% savings proof).
Test Memory Store
pytest tests/test_memory_store.py -v
Usage Patterns
Pattern 1: Remember Important Information
When the user says: "Remember that this project uses PostgreSQL with SSL mode required"
# (You would typically use the Python API, but for CLI:)
python3 -c "
import sys; sys.path.insert(0, 'src')
from memory_store import MemoryStore
store = MemoryStore('.memorylane/memories.json')
store.add_memory('context', 'Project uses PostgreSQL with SSL mode required', 'manual', 0.9)
print('✓ Remembered')
"
Pattern 2: Recall Project Knowledge
When the user asks: "What do you know about our database setup?"
python3 src/cli.py recall "database"
Pattern 3: Show Cost Savings
When the user asks: "How much money has MemoryLane saved me?"
python3 src/cli.py costs
Pattern 4: Learn from Project History
When starting work on a project:
python3 src/learner.py initial
python3 src/cli.py insights
Integration with Workflows
Daily Development Workflow
# Morning: Start server and check status
python3 src/server.py start
python3 src/cli.py status
# During work: MemoryLane learns passively
# (no action needed - watches git commits and file changes)
# Evening: Check what was learned
python3 src/cli.py insights
python3 src/cli.py costs
Project Onboarding Workflow
# Step 1: Initial learning
python3 src/lear