Skip to main content Zero-config persistent memory for Claude with automatic cost savings. Use when you need to remember project context, reduce API token costs, track learned patterns, manage memories across sessions, or curate/clean up memories. Automatically compresses context 6x and saves 84% on API costs. Keywords: memory, remember, recall, context, cost savings, reduce tokens, learn, patterns, insights, curate, clean up memories, review memories.
npx skills add majiayu000/claude-skill-registry --skill memorylane Bash, Read, Write, Glob, Grep
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 monitoringPassive Learning : Automatically learns from git commits and file changesZero 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 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>'
Meta observations about the current session
Debug notes and action statements
Status summaries
Duplicates of CLAUDE.md content
Incomplete fragments
Architectural decisions with rationale
Bug fixes with solutions
Actual project context (not about MemoryLane itself)
Configuration knowledge
❌ "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
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?"
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/learner.py initial
# Step 2: Review learned patterns
python3 src/cli.py recall "primary language"
python3 src/cli.py insights
# Step 3: Export context for sharing
python3 src/cli.py export-markdown --output project-context.md
Configuration Options Key settings in .memorylane/config.json:
memory.max_context_tokens: Target token count for compression (default: 2000)memory.compression_ratio_target: Target compression ratio (default: 7.0x)context_rot.model_context_tokens: Advertised model window for context rot guard (default: 200000)context_rot.safe_fraction: Fraction of model window allowed for prompt + injected context (default: 0.5)context_rot.reserve_tokens: Buffer reserved for assistant response (default: 1200)learning.watch_file_changes: Enable file watching (default: true)learning.watch_git_commits: Learn from commits (default: true)privacy.exclude_patterns: Files to ignore (default: *.env, secrets/, etc.)
Performance Metrics Validated through comprehensive testing:
Cost Savings : 84.3% (baseline $17.25/week → $2.70/week)Compression Ratio : 6.4x average (2.3M tokens → 360K tokens)Retrieval Latency : <100ms (target, server not benchmarked yet)Memory Quality : Relevance scoring with automatic pruning
Important Notes
Privacy : All data stored locally in .memorylane/ directoryNo Dependencies : Pure Python 3.8+ (no external packages needed for production)Automatic Exclusions : Respects .env, secrets/, and other sensitive patternsBackup Before Reset : Always creates backup before destructive operationsServer Optional : CLI works standalone; server adds performance for frequent operations
Example Conversation Flow User : "Remember that our API uses JWT tokens with 24-hour expiration"
You : Let me store that in MemoryLane's context.
python3 -c "..." # Add to memory
✓ Remembered in context category with 0.9 relevance.
User : "What do you know about our authentication?"
You : Let me recall what I know about authentication.
python3 src/cli.py recall "authentication"
[context] API uses JWT tokens with 24-hour expiration ⭐⭐⭐⭐⭐
[patterns] Project uses authentication middleware ⭐⭐⭐⭐
Based on what I've learned, your project uses JWT tokens for authentication with 24-hour expiration...
Troubleshooting Server won't start : Check if already running with python3 src/server.py status
No memories found : Run initial learning with python3 src/learner.py initial
Cost tracking shows $0 : Metrics file not initialized yet (will populate after server usage)
Tests failing : Install dev dependencies with pip install -r requirements-dev.txt
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