Remember - Store Decisions and Patterns Skill

Remember - Store Decisions and Patterns

Filesystem vs MCP memory (Opus 4.8 guidance, CC 2.1.111+): Opus 4.8 is substantially better than older tiers at reading filesystem memory 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:

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 |

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_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

Agent Skills: Remember - Store Decisions and Patterns

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