gh-julep-ai-memory-store-plugin/skills/memory-queue-processor/skill.md

description, proactive

description	proactive
Automatically process queued memory storage requests from hooks. Always checks .memory-queue.jsonl and processes items, reporting to user what was saved.	true

Memory Queue Processor Skill

⚡ CRITICAL: This skill MUST check and process the queue on EVERY user message automatically, and report what was saved.

Purpose

Fully autonomous memory processing with user feedback:

• Hooks write to .memory-queue.jsonl file
• This skill checks the queue on EVERY interaction
• Processes all queued items automatically
• Reports to user what was saved
• Zero user intervention required

When to Activate

ALWAYS activate on EVERY user message:

1. Before responding - Check queue first thing
2. Process all items - Invoke MCP tools automatically
3. Report to user - Tell them what was saved
4. Clear queue - Remove processed items
5. Continue - Respond to their question normally

No exceptions - this happens automatically every single time.

How It Works

1. Check for Queue File

ls -la .memory-queue.jsonl

If file exists and is non-empty, process it.

2. Read Queue Contents

Each line is a JSON object:

{"memory":"Session started...","background":"Project: mem-plugin...","importance":"normal","queued_at":"2025-11-21T15:00:00Z"}

3. Process Each Item

For each line:

1. Parse JSON
2. Invoke mcp__memory-store__record with the parameters
3. Mark as processed (remove from queue or move to .memory-queue-processed.jsonl)

4. Clean Up

After processing:

• Clear the queue file or archive it
• Report how many items were processed (optional, only if > 5 items)

Implementation

HYBRID APPROACH: Hooks provide foundational metadata, Claude adds conversational intelligence.

ALWAYS check the queue at the start of EVERY response:

// 1. Check if queue exists
if (fileExists('.memory-queue.jsonl')) {

  // 2. Read all queued items
  const queue = readFileLines('.memory-queue.jsonl')
    .filter(line => line.trim())
    .map(line => JSON.parse(line));

  // 3. ENRICH each item with conversational context before storing
  for (const item of queue) {
    // Hook provided: File: X, Action: created, Session: Y, Language: Z...
    const foundationalContext = item.background;

    // Claude adds: What's being discussed? What problem is being solved?
    const conversationalContext = analyzeCurrentContext({
      recentMessages: last5UserMessages,
      currentTask: whatUserIsWorkingOn,
      fileBeingModified: item.memory,
      projectInfo: loadedFrom_claude_project_context
    });

    // Combine: Foundational (reliable) + Conversational (intelligent)
    const enrichedBackground = `${foundationalContext}. Context: ${conversationalContext}`;

    await mcp__memory-store__record({
      memory: item.memory,
      background: enrichedBackground,  // ← Now includes BOTH
      importance: item.importance
    });
  }

  // 4. Clear queue
  clearFile('.memory-queue.jsonl');
}

// 5. Continue with normal response

Rules:

• ✅ ALWAYS CHECK - Every single message, no exceptions
• ✅ ENRICH CONTEXT - Add conversation context to hook metadata
• ✅ REPORT TO USER - Tell user what was saved in Memory Store
• ✅ FAIL GRACEFULLY - If error, continue anyway
• ✅ BE BRIEF - Quick confirmation, don't interrupt workflow

Context Enrichment Strategy:

1. What user is working on: "Fixing OAuth authentication", "Adding MCP validation", "Refactoring queue system"
2. Why this change matters: "This script validates MCP connection at session start to catch config issues early"
3. How it relates to conversation: "Part of implementing the hybrid context approach discussed with user"
4. Project-specific details: From .claude-project-context - architecture, purpose, components

Hybrid Context Examples

Example 1: File Change with Conversational Enrichment

Hook writes (foundational):

{
  "memory": "File modified: scripts/session-start.sh (Shell)",
  "background": "File: scripts/session-start.sh, Action: modified, Session: mem-xyz, Language: Shell, Pattern: , Change: #5, Project: mem-plugin, Version: 1.2.3, Component: script"
}

Claude enriches (before storing):

• Sees conversation: User said "add MCP validation at session start"
• Understands context: This is part of fixing broken MCP detection
• Reads .claude-project-context: Understands mem-plugin architecture
• Adds: "Context: User implementing MCP connection validation at session start. This script now checks if memory-store MCP is configured using claude mcp list and displays status/setup command. Part of making the plugin more user-friendly by catching configuration issues early. Related to queue-based architecture where hooks need reliable MCP connection."

Final stored background:

File: scripts/session-start.sh, Action: modified, Session: mem-xyz, Language: Shell, Pattern: , Change: #5, Project: mem-plugin, Version: 1.2.3, Component: script. Context: User implementing MCP connection validation at session start. This script now checks if memory-store MCP is configured using `claude mcp list` and displays status/setup command. Part of making the plugin more user-friendly by catching configuration issues early. Related to queue-based architecture where hooks need reliable MCP connection.

Example 2: Session Start with Intelligence

Hook writes (foundational):

{
  "memory": "Session mem-20251121-ABC started in mem-plugin on branch main",
  "background": "Session: mem-20251121-ABC, Started: 2025-11-21T15:00:00Z, Project: mem-plugin, Branch: main, Commit: 7005850, Files: 48, MCP: configured, Version: 1.2.3"
}

Claude enriches:

• Sees recent commits: "feat: add enriched background context", "feat: add MCP validation"
• Understands ongoing work: Improving context quality in memory storage
• Adds: "Context: Continuing work on hybrid context approach - hooks provide foundational metadata, Claude adds conversational intelligence. Recent work includes MCP validation and enriched background context. User wants reliable but intelligent memory storage."

Result: Future sessions know what was being worked on and why!

Example 3: Original Approach

Scenario: User starts new session, previous session queued 3 items

Action:

1. Check .memory-queue.jsonl exists
2. Read 3 items
3. Invoke mcp__memory-store__record for each
4. Clear queue
5. Report: "💾 Saved 3 items to Memory Store (session start, 2 file changes)"

Example 2: File Changes

Scenario: User edits 5 files rapidly, hooks queue each

Action:

1. After user's next message, check queue
2. Find 5 items
3. Process all 5
4. Report: "💾 Saved 5 file changes to Memory Store"

Example 3: Empty Queue

Scenario: No queued items

Action:

1. Check file (doesn't exist or empty)
2. Skip processing
3. Continue normally (no report needed)

Reporting Format

When items processed, always tell the user:

• 1-2 items: "💾 Saved to Memory Store: [brief description]"
• 3-5 items: "💾 Saved 4 items to Memory Store (session, 3 files)"
• 6+ items: "💾 Saved 8 items to Memory Store"

Keep it brief - just confirm what was saved, don't interrupt their workflow.

Integration with Hooks

Hooks use queue-memory.sh instead of outputting signals:

Old approach (didn't work):

cat <<EOF
{
  "additionalContext": "🤖 MEMORY_STORE_AUTO_RECORD: {...}"
}
EOF

New approach (works):

bash "${CLAUDE_PLUGIN_ROOT}/scripts/queue-memory.sh" \
  --memory "Session started..." \
  --background "Project: mem-plugin..." \
  --importance "normal"

Advantages

1. Bypasses visibility issue - File-based, not conversation-based
2. Reliable - Files persist across Claude Code restarts
3. Auditable - Can inspect .memory-queue.jsonl manually
4. Batching - Process multiple items efficiently
5. Resilient - Failures don't lose data (re-process on next check)

Error Handling

If processing fails:

• Keep items in queue for retry
• Log error to .memory-queue-errors.log
• Don't block user workflow
• Retry on next session/check

If auth fails:

• Keep all items in queue
• Warn user once: "Memory Store authentication needed"
• Don't spam warnings on every check

Testing

To verify this skill works:

1. Manually queue an item:

   ./scripts/queue-memory.sh --memory "Test item" --background "Testing queue" --importance "low"
   

2. Trigger skill: Say: "Check for queued memories"

3. Verify processing:

   • Queue file should be empty
   • Item should be in Memory Store
   • Can retrieve with recall

Success criteria: ✓ Item queued, processed, retrievable, queue cleared

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💡 Pro Tip: This queue-based approach is MORE reliable than signal-based because it persists data to disk and doesn't depend on conversation context visibility!