tomevault-io WW Store Skill
Store information in World Weaver's tripartite memory system using the MCP gateway.
Purpose
This skill stores three types of memories:
- Episodes: Autobiographical events (what happened, when, outcome)
- Entities: Knowledge graph nodes (concepts, people, places)
- 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:
- Check if ww-memory server is running
- Verify Neo4j/Qdrant are accessible
- Fall back to logging the memory request for later storage
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