astoreyai/ww-store

WW Store Skill

Store information in World Weaver's tripartite memory system using the MCP gateway.

Purpose

This skill stores three types of memories:

1. Episodes: Autobiographical events (what happened, when, outcome)
2. Entities: Knowledge graph nodes (concepts, people, places)
3. Skills: Procedural patterns (how to do things)

When to Use

Invoke this skill when:

• User says "remember this", "store this", "save for later"
• A significant milestone is reached
• An important decision is made
• A new concept or entity is encountered
• A successful pattern emerges that could be reused

MCP Tools Available

The World Weaver MCP server (ww-memory) provides these tools:

mcp__ww-memory__create_episode     - Store autobiographical event
mcp__ww-memory__create_entity      - Add knowledge graph node
mcp__ww-memory__create_relation    - Link entities
mcp__ww-memory__create_skill       - Store procedural pattern

Episode Storage Workflow

When storing an episode:

1. Extract Context

Gather from current environment:

# Working directory
pwd

# Current project (from git or directory name)
basename $(pwd)

# Recent git activity
git log --oneline -3 2>/dev/null || echo "Not a git repo"

2. Classify Outcome

Determine outcome from conversation:

• success: Task completed, goal achieved
• failure: Task failed, error occurred
• partial: Some progress, incomplete
• neutral: Informational, no specific outcome

3. Assess Importance

Rate emotional valence (0.0 to 1.0):

• 1.0: Critical milestone, major breakthrough
• 0.7-0.9: Important achievement, significant learning
• 0.4-0.6: Normal work, routine task
• 0.1-0.3: Minor note, low importance

4. Store Episode

Call MCP tool:

mcp__ww-memory__create_episode(
  content: "Full description of what happened",
  outcome: "success|failure|partial|neutral",
  emotional_valence: 0.0-1.0,
  context: {
    project: "project-name",
    working_directory: "/path/to/project",
    tool: "tool-used-if-any",
    file: "file-modified-if-any"
  }
)

Entity Storage Workflow

When creating an entity:

1. Classify Entity Type

• CONCEPT: Abstract idea, technology, pattern
• PERSON: Individual (collaborator, author, etc.)
• PLACE: Location (server, environment, path)
• EVENT: Specific occurrence (release, meeting)
• OBJECT: Concrete thing (file, repository, tool)
• SKILL: Capability or procedure

2. Generate Summary

Create 1-2 sentence description of the entity.

3. Store Entity

mcp__ww-memory__create_entity(
  name: "Entity Name",
  entity_type: "CONCEPT|PERSON|PLACE|EVENT|OBJECT|SKILL",
  summary: "Brief description of the entity",
  details: "Optional longer description"
)

4. Create Relations

Link to existing entities:

mcp__ww-memory__create_relation(
  source_name: "Entity A",
  target_name: "Entity B",
  relation_type: "RELATES_TO|PART_OF|USED_BY|DEPENDS_ON",
  properties: {
    context: "How they relate"
  }
)

Skill Storage Workflow

When storing a procedural skill:

1. Extract Procedure

Document the steps that worked:

1. First, do X
2. Then, do Y
3. Finally, do Z

2. Define Parameters

What inputs does this skill need?

{
  "param1": {"type": "string", "description": "What it's for"},
  "param2": {"type": "number", "description": "What it controls"}
}

3. Define Conditions

• Preconditions: What must be true before executing
• Postconditions: What will be true after executing

4. Store Skill

mcp__ww-memory__create_skill(
  name: "skill-name",
  description: "What this skill does",
  procedure: "Step by step instructions",
  parameters: {...},
  preconditions: ["condition1", "condition2"],
  postconditions: ["result1", "result2"],
  tags: ["tag1", "tag2"]
)

Examples

Example 1: Store Episode After Bug Fix

User: "Remember that I fixed the batch query bug"

Action:
1. Extract: cwd=/home/aaron/ww, project=ww
2. Outcome: success (bug fixed)
3. Valence: 0.7 (important fix)
4. Store:
   mcp__ww-memory__create_episode(
     content="Fixed batch query bug in Neo4j store. Changed $id to id for UNWIND variable, fixed return structure to use other_id and nested properties.",
     outcome="success",
     emotional_valence=0.7,
     context={project: "ww", working_directory: "/home/aaron/ww"}
   )

Example 2: Store New Concept Entity

User: "Remember what Hebbian learning is"

Action:
1. Type: CONCEPT
2. Summary: Neural learning rule where connections strengthen through co-activation
3. Store:
   mcp__ww-memory__create_entity(
     name="Hebbian Learning",
     entity_type="CONCEPT",
     summary="Neural learning principle: neurons that fire together wire together. Connections strengthen when nodes are co-activated.",
     details="Used in World Weaver semantic memory to strengthen relationships between co-retrieved entities."
   )

Example 3: Store Procedural Skill

User: "Remember how to run the WW tests"

Action:
1. Procedure: activate venv, run pytest
2. Parameters: test path, markers
3. Store:
   mcp__ww-memory__create_skill(
     name="run-ww-tests",
     description="Run World Weaver test suite",
     procedure="1. source .venv/bin/activate\n2. pytest tests/ -v\n3. Check output for failures",
     parameters={
       "path": {"type": "string", "default": "tests/", "description": "Test path"},
       "markers": {"type": "string", "default": "", "description": "Pytest markers"}
     },
     preconditions=["In WW project directory", "Virtual environment exists"],
     postconditions=["Test results displayed", "Coverage report generated"],
     tags=["testing", "pytest", "ww"]
   )

Quality Checklist

Before storing:

• [ ] Content is specific and actionable
• [ ] Outcome classification is accurate
• [ ] Importance rating reflects actual significance
• [ ] Context includes relevant project/file info
• [ ] Entity types are correctly classified
• [ ] Relations link to existing entities where possible

Error Handling

If MCP tool fails:

1. Check if ww-memory server is running
2. Verify Neo4j/Qdrant are accessible
3. Fall back to logging the memory request for later storage