Remember

Remember - Store Decisions and Patterns

Store important decisions, patterns, or context in the knowledge graph for future sessions. Supports tracking success/failure outcomes for building a Best Practice Library.

Argument Resolution

TEXT = "$ARGUMENTS"        # Full argument string, e.g., "We use cursor pagination"
FLAG = "$ARGUMENTS[0]"     # First token — check for --success, --failed, --category, --agent
# Parse flags from $ARGUMENTS[0], $ARGUMENTS[1] etc. (CC 2.1.59 indexed access)
# Remaining tokens after flags = the text to remember

Architecture

The remember skill uses knowledge graph as storage:

1. Knowledge Graph: Entity and relationship storage via mcp__memory__create_entities and mcp__memory__create_relations - FREE, zero-config, always works

Benefits:

• Zero configuration required - works out of the box
• Explicit relationship queries (e.g., "what does X use?")
• Cross-referencing between entities
• No cloud dependency

Automatic Entity Extraction:

• Extracts capitalized terms as potential entities (PostgreSQL, React, pgvector)
• Detects agent names (database-engineer, backend-system-architect)
• Identifies pattern names (cursor-pagination, connection-pooling)
• Recognizes "X uses Y", "X recommends Y", "X requires Y" relationship patterns

Usage

Store Decisions (Default)

/ork:remember <text>
/ork:remember --category <category> <text>
/ork:remember --success <text>     # Mark as successful pattern
/ork:remember --failed <text>      # Mark as anti-pattern
/ork:remember --success --category <category> <text>

# Agent-scoped memory
/ork:remember --agent <agent-id> <text>         # Store in agent-specific scope
/ork:remember --global <text>                   # Store as cross-project best practice

Flags

Categories

• decision - Why we chose X over Y (default)
• architecture - System design and patterns
• pattern - Code conventions and standards
• blocker - Known issues and workarounds
• constraint - Limitations and requirements
• preference - User/team preferences
• pagination - Pagination strategies
• database - Database patterns
• authentication - Auth approaches
• api - API design patterns
• frontend - Frontend patterns
• performance - Performance optimizations

Outcome Flags

• --success - Pattern that worked well (positive outcome)
• --failed - Pattern that caused problems (anti-pattern)

If neither flag is provided, the memory is stored as neutral (informational).

Workflow

1. Parse Input

Check for --success flag → outcome: success
Check for --failed flag → outcome: failed
Check for --category <category> flag
Check for --agent <agent-id> flag → agent_id: "ork:{agent-id}"
Check for --global flag → use global user_id
Extract the text to remember
If no category specified, auto-detect from content

2. Auto-Detect Category

3. Extract Lesson (for anti-patterns)

If outcome is "failed", look for:

• "should have", "instead use", "better to"
• If not found, prompt user: "What should be done instead?"

4. Extract Entities from Text

Step A: Detect entities:

1. Find capitalized terms (PostgreSQL, React, FastAPI)
2. Find agent names (database-engineer, backend-system-architect)
3. Find pattern names (cursor-pagination, connection-pooling)
4. Find technology keywords (pgvector, HNSW, RAG)

Step B: Detect relationship patterns:

5. Create Graph Entities (PRIMARY)

Use mcp__memory__create_entities:

{
  "entities": [
    {
      "name": "pgvector",
      "entityType": "Technology",
      "observations": ["Used for vector search", "From remember: '{original text}'"]
    },
    {
      "name": "database-engineer",
      "entityType": "Agent",
      "observations": ["Recommends pgvector for RAG"]
    }
  ]
}

Entity Type Assignment:

• Capitalized single words ending in common suffixes: Technology (PostgreSQL, FastAPI)
• Words with hyphens matching agent pattern: Agent (database-engineer)
• Words with hyphens matching pattern names: Pattern (cursor-pagination)
• Project context: Project (current project name)
• Failed patterns: AntiPattern

6. Create Graph Relations

Use mcp__memory__create_relations:

{
  "relations": [
    {
      "from": "database-engineer",
      "to": "pgvector",
      "relationType": "RECOMMENDS"
    },
    {
      "from": "pgvector",
      "to": "RAG",
      "relationType": "USED_FOR"
    }
  ]
}

7. Confirm Storage

For success:

✅ Remembered SUCCESS (category): "summary of text"
   → Stored in knowledge graph
   → Created entity: {entity_name} ({entity_type})
   → Created relation: {from} → {relation_type} → {to}
   📊 Graph: {N} entities, {M} relations

For failed:

❌ Remembered ANTI-PATTERN (category): "summary of text"
   → Stored in knowledge graph
   → Created entity: {anti-pattern-name} (AntiPattern)
   💡 Lesson: {lesson if extracted}

For neutral:

✓ Remembered (category): "summary of text"
   → Stored in knowledge graph
   → Created entity: {entity_name} ({entity_type})
   📊 Graph: {N} entities, {M} relations

Examples

Basic Remember (Graph Only)

Input: /remember Cursor-based pagination scales well for large datasets

Output:

✓ Remembered (pagination): "Cursor-based pagination scales well for large datasets"
   → Stored in knowledge graph
   → Created entity: cursor-pagination (Pattern)
   📊 Graph: 1 entity, 0 relations

Anti-Pattern

Input: /remember --failed Offset pagination caused timeouts on tables with 1M+ rows

Output:

❌ Remembered ANTI-PATTERN (pagination): "Offset pagination caused timeouts on tables with 1M+ rows"
   → Stored in knowledge graph
   → Created entity: offset-pagination (AntiPattern)
   💡 Lesson: Use cursor-based pagination for large datasets
   📊 Graph: 1 entity, 0 relations

Agent-Scoped Memory

Input: /remember --agent backend-system-architect Use connection pooling with min=5, max=20

Output:

✓ Remembered (database): "Use connection pooling with min=5, max=20"
   → Stored in knowledge graph
   → Created entity: connection-pooling (Pattern)
   → Created relation: project → USES → connection-pooling
   📊 Graph: 1 entity, 1 relation
   🤖 Agent: backend-system-architect

Duplicate Detection

Before storing, search for similar patterns in graph:

1. Query graph with mcp__memory__search_nodes for entity names
2. If exact entity exists:
   • Add observation to existing entity via mcp__memory__add_observations
   • Inform user: "✓ Updated existing entity (added observation)"
3. If similar pattern found with opposite outcome:
   • Warn: "⚠️ This conflicts with an existing pattern. Store anyway?"

File-Based Memory Updates

When updating .claude/memory/MEMORY.md or project memory files:

• PREFER Edit over Write to preserve existing content and avoid overwriting
• Use stable anchor lines: ## Recent Decisions, ## Patterns, ## Preferences
• See the memory skill's "Permission-Free File Operations" section for the full Edit pattern
• This applies to the calling agent's file operations, not to the knowledge graph operations above

Related Skills

• ork:memory - Search, load, sync, visualize (read-side operations)

Error Handling

• If knowledge graph unavailable, show configuration instructions
• If text is empty, ask user to provide something to remember
• If text >2000 chars, truncate with notice
• If both --success and --failed provided, ask user to clarify
• If --agent used without agent-id, prompt for agent selection
• If entity extraction fails, create a generic Decision entity
• If relation creation fails (e.g., entity doesn't exist), create entities first then retry