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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:
- 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 patternspattern - Code conventions and standardsblocker - Known issues and workaroundsconstraint - Limitations and requirementspreference - User/team preferencespagination - Pagination strategiesdatabase - Database patternsauthentication - Auth approachesapi - API design patterns
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("${CLAUDE_SKILL_DIR}/references/confirmation-templates.md")
File-Based Memory Updates
When updating .claude/memory/MEMORY.md or project memory files:
- PREFER Edit over Write to preserve existing content and avoid overwriting
- Use stable anchor lines:
## Recent Decisions, ## Patterns, ## Preferences
- See the
memory skill's "Permission-Free File Operations" section for the full Edit pattern
- This applies to the calling agent's file operations, not to the knowledge graph operations above
References
Load on demand with Read("${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 |
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
Weekly Installs
93
Repository
yonatangross/orchestkit
GitHub Stars
134
First Seen
Jan 22, 2026
Security Audits
Gen Agent Trust HubPassSocketPassSnykPass
Installed on
gemini-cli85
opencode84
codex84
github-copilot83
cursor81
amp77
