Memory Auto Track

Memory Auto-Track Skill

⚡ IMPORTANT: This skill is ALWAYS ACTIVE. Use it automatically whenever the user asks questions.

Purpose

Automatic memory search integrated into conversation:

• ALWAYS search memory when user asks ANY question about the project
• Check for established patterns before suggesting implementations
• Surface relevant past work automatically
• No manual /memory-recall needed - you do it automatically

Note: Storage is handled automatically by hooks. This skill makes retrieval AUTOMATIC.

When to Search Memory (Automatically!)

ALWAYS invoke mcp__memory-store__recall when:

1. User asks ANY question about the project

   • "How did we implement X?"
   • "What patterns do we use?"
   • "Why did we choose Y?"
   • → AUTOMATICALLY search memory BEFORE answering

2. User asks you to implement something

   • "Create an API endpoint"
   • "Add authentication"
   • "Fix the bug in X"
   • → AUTOMATICALLY search for similar past work

3. User asks about team/ownership

   • "Who worked on X?"
   • "What does the team use for Y?"
   • → AUTOMATICALLY search memory

4. User mentions uncertainty

   • "I'm not sure how we..."
   • "What's our convention for..."
   • → AUTOMATICALLY search memory

5. You're about to suggest something

   • Before proposing an approach
   • → AUTOMATICALLY search to check for existing patterns

Cues for retrieval:

• Project name
• File/directory names being worked on
• Technology stack keywords
• Feature names
• Problem domain terms

What to Do

For Storing Memories

When storing (reactive to hooks):

1. Parse the context to extract:

   • The main memory text (concise summary)
   • Background details (full context)
   • Importance level (low, normal, high)

2. Invoke mcp__memory__record tool with the extracted information

3. Confirm silently - No need to tell the user unless there's an error

For Retrieving Context

When retrieving (proactive for guidance):

1. Identify what you need to know:

   • What patterns exist for this type of work?
   • How was similar functionality implemented?
   • What decisions were made about this?

2. Create search cues (3-7 relevant terms):

   ["authentication", "API endpoint", "OAuth", "patterns"]
   

3. Invoke mcp__memory__recall with:

   • cues: Array of search terms
   • background: Context about why you're searching
   • k: Number of results (default 10)

4. Use the results to:

   • Follow established patterns
   • Reference past decisions
   • Ensure consistency with team conventions
   • Provide better, context-aware responses

5. Mention to user when using past context:

   • "Based on our previous work with authentication..."
   • "Following the API pattern we established in auth.ts..."
   • "Consistent with the decision we made on 2024-11-01..."

Examples

Example 1: Storing - Session Start

Input additionalContext:

🚀 Development session starting in project mem-plugin on branch main.
Session ID: mem-20251113-ABC123. This session context should be stored
in memory using memory__record tool with importance: normal.

Action:

// Invoke mcp__memory__record
{
  "memory": "Development session started in mem-plugin on branch main",
  "background": "Session ID: mem-20251113-ABC123. Session initialized at 2025-11-13T17:00:00Z",
  "importance": "normal"
}

Example 2: File Change

Input additionalContext:

📝 File created: auth.ts (TypeScript). Pattern: API endpoint. Store this
in memory using memory__record with importance: low, background: 'File
auth.ts was created in session mem-123. Language: TypeScript. Pattern:
API endpoint. Change #5.'

Action:

// Invoke mcp__memory__record
{
  "memory": "File created: auth.ts (TypeScript). Pattern: API endpoint",
  "background": "File auth.ts was created in session mem-123. Language: TypeScript. Pattern: API endpoint. Change #5.",
  "importance": "low"
}

Example 3: Commit Analysis

Input additionalContext:

💾 Commit: feat: add OAuth2 authentication (feature) - Ticket AUTH-123.
Store this commit in memory using memory__record with importance: high,
background: 'Commit abc1234 on branch feature/auth. Type: feature. Files
changed: 8. Breaking change: false.'

Action:

// Invoke mcp__memory__record
{
  "memory": "Commit: feat: add OAuth2 authentication (feature) - Ticket AUTH-123",
  "background": "Commit abc1234 on branch feature/auth. Type: feature. Files changed: 8. Breaking change: false.",
  "importance": "high"
}

Important Notes

• Always invoke immediately when you see the pattern
• Don't ask for confirmation - hooks have already decided this should be stored
• Extract importance carefully - defaults to "normal" if not specified
• Handle errors gracefully - if mcp__memory__record fails, mention it to the user
• Be silent on success - no need to confirm to user unless they ask

Parsing Guidelines

Look for these patterns in additionalContext:

1. Memory text: Usually the first sentence or main statement
2. Importance level: Look for "importance: (low|normal|high)"
3. Background: Either explicitly marked as "background: '...'" or the detailed context following the main statement

Example 4: Retrieving - User Asks About Past Work

User says:

"How did we implement authentication in this project?"

Action:

// Invoke mcp__memory__recall
{
  "cues": ["authentication", "implement", "auth", "login", "OAuth"],
  "background": "User asking about authentication implementation. Need to retrieve past work on auth systems in this project.",
  "k": 10
}

Expected results:

• Past commits related to authentication
• Files that implement auth (auth.ts, login.tsx, etc.)
• Decisions made about auth approach
• Patterns used for authentication

Response to user:

Based on our previous work, we implemented OAuth2 authentication.
See auth.ts:45 where we established the pattern. The flow uses...

Example 5: Retrieving - Starting Similar Work

User says:

"Create a new API endpoint for user profiles"

Before implementing, retrieve context:

// Invoke mcp__memory__recall
{
  "cues": ["API endpoint", "create endpoint", "user", "REST API", "patterns"],
  "background": "About to create new API endpoint. Need to check existing API patterns and conventions.",
  "k": 5
}

Use results to:

• Follow same authentication pattern
• Use same error handling approach
• Match existing API structure
• Maintain consistency

Response:

I'll create the user profiles endpoint following our established API pattern.
Based on our existing endpoints (like auth.ts), I'll use the same middleware
structure and error handling...

Example 6: Retrieving - Avoiding Going Off Track

Scenario: Claude is about to suggest using MongoDB, but team uses PostgreSQL

Before suggesting:

// Invoke mcp__memory__recall
{
  "cues": ["database", "data storage", "PostgreSQL", "MongoDB", "decision"],
  "background": "Need to recommend database approach. Checking if team has established database preferences.",
  "k": 5
}

Results show: "Decision: Use PostgreSQL for ACID compliance (2024-10-15)"

Corrected response:

I'll use PostgreSQL for this, consistent with our team's decision from
October 2024. This ensures ACID compliance and aligns with our existing
database infrastructure.

Error Handling

If mcp__memory__record fails:

1. Don't silently fail - inform the user
2. Provide context - tell them what you were trying to store
3. Suggest solutions - check MCP server connection, retry later

Example error message:

⚠️ Unable to store memory: "File created: auth.ts".
The memory store server may be unavailable. Your work is still tracked
locally and will sync when the connection is restored.

Testing

For Storage:

1. Make a file change
2. Hook fires → outputs additionalContext
3. This skill activates → invokes memory__record
4. Memory is stored

For Retrieval:

1. Ask Claude about past work: "How did we implement X?"
2. Skill should invoke memory__recall automatically
3. Claude uses retrieved context in response
4. Mentions where pattern/decision came from

End-to-end test:

1. Create file with specific pattern (e.g., API endpoint)
2. Verify stored via memory__record
3. Later ask: "How should I create an API endpoint?"
4. Claude retrieves the pattern and follows it
5. Consistency maintained! ✅