Stores decisions, patterns, and outcomes in the MCP memory knowledge graph as entities with typed observations and relations. Supports recording architectural decisions, anti-patterns, tool preferences, workflow outcomes, and project conventions that persist across sessions. Use when saving patterns, remembering outcomes, recording decisions, or building institutional knowledge.
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Stores decisions, patterns, and outcomes in the MCP memory knowledge graph as entities with typed observations and relations. Supports recording architectural decisions, anti-patterns, tool preferences, workflow outcomes, and project conventions that persist across sessions. Use when saving patterns, remembering outcomes, recording decisions, or building institutional knowledge.
{"keywords":["remember","save","store","record","best practice","anti-pattern","lets not forget","mark this"],"examples":["remember that we chose Postgres over MongoDB","save this decision: always use cursor-based pagination","store this pattern as a best practice"],"anti-triggers":["recall","search","show memory","knowledge graph","load context","explore"]}
Remember - Store Decisions and Patterns
Filesystem vs MCP memory (Opus 5 guidance, CC 2.1.111+): Opus 5 reads filesystem memory reliably across multi-session work. Use that to your advantage:
Durable auto-memory (user facts, feedback, project conventions) → ~/.claude/projects/<slug>/memory/*.md files with a one-line index in MEMORY.md. Read on every session start.
Cross-session knowledge graph (typed entities + relations for query traversal) → MCP memory server (this skill's default path). Best when future sessions will search for patterns.
The three are complementary, not alternatives. Prefer fs for anything you'd want to grep; prefer MCP for anything you'd want to traverse.
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)
/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.
7. Confirm Type + Scope (AskUserQuestion — M118 #1466)
Auto-classification is best-effort. Before writing, ask the user to confirm both the memory type and the persistence scope. Pre-select the auto-detected type as the default option when the classifier is confident (≥0.9):
# Skip when the invocation already specifies type and scope:# /ork:remember --type=preference --global → skip, use those values# /ork:remember --session → skip, no persistence## Otherwise, ask both:
AskUserQuestion(questions=[
{"question": "What type of memory?",
"header": "Type",
"options": [
{"label": "Preference", "description": "How I like to work (style, format, tooling)"},
{"label": "Project fact", "description": "State of THIS codebase (decisions, constraints)"},
{"label": "Reference", "description": "External system pointer (URL, doc, dashboard)"},
{"label": "Feedback", "description": "Correction to future behavior — applied as a rule"}
]},
{"question": "Apply to?",
"header": "Scope",
"options": [
{"label": "This project (default)", "description": "Scoped to .claude/projects/<this-project>/memory/"},
{"label": "All projects (global)", "description": "User-level memory at ~/.claude/memory/"},
{"label": "This session only", "description": "Print to context but don't persist"}
]}
])
Default selection rules:
If auto-detected category maps to one of the 4 type options with confidence ≥0.9, that option is pre-selected (the user can still override).
"This project" is always the scope default.
"This session only" returns immediately without writing — useful when the user wants to surface a fact for the conversation but not commit it.
Global memory writes route to ~/.claude/memory/<filename>.md instead of the project-local memory directory. The MEMORY.md index is updated in whichever scope was selected (project- or user-level).
Load entity extraction rules, type assignment, relationship patterns, and graph creation examples: Read("${CLAUDE_PLUGIN_ROOT}/skills/remember/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_PLUGIN_ROOT}/skills/remember/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
