Stores decisions and patterns in knowledge graph. Use when saving patterns, remembering outcomes, or recording decisions.
Stores decisions and patterns in a knowledge graph for tracking outcomes and building best practices.
/plugin marketplace add yonatangross/orchestkit/plugin install orkl@orchestkitThis skill is limited to using the following tools:
references/category-detection.mdStore important decisions, patterns, or context in the knowledge graph for future sessions. Supports tracking success/failure outcomes for building a Best Practice Library.
The remember skill uses knowledge graph as storage:
mcp__memory__create_entities and mcp__memory__create_relations - FREE, zero-config, always worksBenefits:
Automatic Entity Extraction:
/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
| 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 |
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 patternsfrontend - Frontend patternsperformance - Performance optimizations--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).
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
| 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 |
If outcome is "failed", look for:
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:
| Pattern | Relation Type |
|---|---|
| "X uses Y" | USES |
| "X recommends Y" | RECOMMENDS |
| "X requires Y" | REQUIRES |
| "X enables Y" | ENABLES |
| "X prefers Y" | PREFERS |
| "chose X over Y" | CHOSE_OVER |
| "X for Y" | USED_FOR |
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:
Use mcp__memory__create_relations:
{
"relations": [
{
"from": "database-engineer",
"to": "pgvector",
"relationType": "RECOMMENDS"
},
{
"from": "pgvector",
"to": "RAG",
"relationType": "USED_FOR"
}
]
}
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
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
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
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
Before storing, search for similar patterns in graph:
mcp__memory__search_nodes for entity namesmcp__memory__add_observationsWhen updating .claude/memory/MEMORY.md or project memory files:
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