memorylane

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:

1. List and evaluate all memories:

python3 src/cli.py curate --list

1. Evaluate each memory for:
2. Usefulness: Is this actionable knowledge or just meta/debug info?
3. Duplication: Is this already covered by CLAUDE.md or another memory?
4. Quality: Is it complete, clear, and well-formed?
5. Relevance: Would this help with future development work?

1. 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