/remember

description: "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."
argument-hint: "[decision-or-pattern]"
model: haiku
effort: low
context: inherit
user-invocable: true
name: remember
allowed-tools: [Read, Grep, Glob, Bash, AskUserQuestion, mcp__memory__create_entities, mcp__memory__create_relations, mcp__memory__add_observations, mcp__memory__search_nodes]

Auto-generated from skills/remember/SKILL.md

Source: https://github.com/yonatangross/orchestkit

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: > - **Short-lived handoff state** (current phase, task in-progress, pending approvals) → `.claude/chain/*.json` files. Small, structured, session-scoped. > - **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:

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.

7. Confirm Type + Scope (AskUserQuestion — M118 #1466)

Auto-classification is best-effort. Before writing, ask the user to confirm both the memory type an