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

Flag	Behavior
(default)	Write to graph
--success	Mark as successful pattern
--failed	Mark as anti-pattern
--category <cat>	Set category
--agent <agent-id>	Scope memory to a specific agent
--global	Store as cross-project best practice

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

Keywords	Category
chose, decided, selected	decision
architecture, design, system	architecture
pattern, convention, style	pattern
blocked, issue, bug, workaround	blocker
must, cannot, required, constraint	constraint
pagination, cursor, offset, page	pagination
database, sql, postgres, query	database
auth, jwt, oauth, token, session	authentication
api, endpoint, rest, graphql	api
react, component, frontend, ui	frontend
performance, slow, fast, cache	performance

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?"

4-6. Extract Entities and Create Graph

Extract entities (Technology, Agent, Pattern, Project, AntiPattern) from the text, detect relationship patterns ("X uses Y", "chose X over Y", etc.), then create entities and relations in the knowledge graph.

Load entity extraction rules, type assignment, relationship patterns, and graph creation examples: Read("${CLAUDE_SKILL_DIR}/references/graph-operations.md")

7. Confirm Storage

Display confirmation using the appropriate template (success, anti-pattern, or neutral) showing created entities, relations, and graph stats.

Load output templates and examples: Read("${CLAUDE_SKILL_DIR}/references/confirmation-templates.md")

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

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References

Load on demand with Read("${CLAUDE_SKILL_DIR}/references/<file>"):

File	Content
category-detection.md	Auto-detection rules for categorizing memories (priority order)
graph-operations.md	Entity extraction, type assignment, relationship patterns, graph creation
confirmation-templates.md	Output templates (success, anti-pattern, neutral) and usage examples

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Related Skills

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

Error Handling

• Knowledge graph unavailable → show configuration instructions
• Empty text → ask user for content; text >2000 chars → truncate with notice
• Both --success and --failed → ask user to clarify
• Entity extraction fails → create generic Decision entity; relation fails → create entities first, retry