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Memory visualization and diagramming for World Weaver systems
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Baseado na classificação ocupacional SOC
Comprehensive SessionEnd hook that syncs and synthesizes all daily completions, notes, todos, and conversation data into unified daily notes and knowledge bases.
Unified worklog system. Synthesizes session work into daily markdown files with clean bullet points. Supports weekly rollups for Slack #progress sharing. Replaces fragmented checkpoint/session-state/weekly-notes systems.
Comprehensive pre-submission review orchestrator. Coordinates methodology checks, bias detection, AI text detection, statistical validation, and reporting guideline compliance for journal submissions.
Kymera Systems brand design - Jarvis-inspired HUD aesthetic with dark and light modes. Use for branded UI, dashboards, presentations, and technical artifacts.
Deep analysis workflows for World Weaver memory systems, code, and architecture
Architecture documentation generation and maintenance for World Weaver
| name | ww-visualize |
| description | Memory visualization and diagramming for World Weaver systems |
| version | 1.0.0 |
| allowed-tools | ["Bash","Read","Write","Grep","Glob"] |
Memory visualization and diagramming for World Weaver systems.
This skill provides visualization capabilities:
Invoke this skill when:
Generate Mermaid diagram of entity relationships:
def generate_knowledge_graph(entities, relationships):
"""Generate Mermaid graph from WW semantic memory."""
lines = ["graph LR"]
# Add entities
for entity in entities:
node_id = entity['name'].replace(' ', '_')
lines.append(f" {node_id}[{entity['name']}]")
# Add relationships
for rel in relationships:
src = rel['source'].replace(' ', '_')
tgt = rel['target'].replace(' ', '_')
label = rel['type']
weight = rel.get('weight', 1.0)
# Thicker line for stronger relationships
if weight > 0.7:
lines.append(f" {src} =={label}==> {tgt}")
else:
lines.append(f" {src} --{label}--> {tgt}")
return '\n'.join(lines)
Output:
graph LR
Hebbian_Learning[Hebbian Learning]
STDP[STDP]
Eligibility_Traces[Eligibility Traces]
Hebbian_Learning ==USES==> STDP
STDP --REQUIRES--> Eligibility_Traces
Generate timeline of episodes:
def generate_timeline(episodes):
"""Generate Mermaid timeline from episodes."""
lines = ["gantt", " title Memory Timeline", " dateFormat YYYY-MM-DD"]
# Group by project
by_project = defaultdict(list)
for ep in episodes:
project = ep.get('context', {}).get('project', 'Unknown')
by_project[project].append(ep)
for project, eps in by_project.items():
lines.append(f" section {project}")
for ep in eps:
date = ep['timestamp'][:10]
outcome = ep['outcome']
icon = "crit" if outcome == "failure" else ""
lines.append(f" {ep['content'][:30]} :{icon} {date}, 1d")
return '\n'.join(lines)
flowchart TB
subgraph Plugin["Claude Code Plugin"]
direction TB
Skills["Skills<br/>store, recall, context, consolidate<br/>analyze, diagnose, visualize, architecture"]
Commands["Commands<br/>/remember, /recall, /consolidate<br/>/ww-audit, /ww-visualize"]
Hooks["Hooks<br/>SessionStart, SessionEnd"]
Agents["Agents<br/>memory, retriever, synthesizer<br/>bio-auditor, race-hunter, leak-hunter<br/>hinton-validator, cache-analyzer, trace-debugger"]
end
subgraph MCP["MCP Server"]
direction TB
Gateway["Request Gateway"]
Memory["Memory Manager"]
Learning["Learning System"]
Consolidation["Consolidation Engine"]
end
subgraph Core["Core Systems"]
direction TB
Episodic["Episodic Store<br/>Fast learning, autobiographical"]
Semantic["Semantic Store<br/>Slow learning, knowledge graph"]
Procedural["Procedural Store<br/>Skills, patterns"]
end
subgraph Storage["Storage"]
Neo4j[(Neo4j<br/>Graph DB)]
Qdrant[(Qdrant<br/>Vector DB)]
end
Plugin --> MCP
MCP --> Core
Core --> Storage
def generate_bug_chart(bugs):
"""Generate bug distribution visualization."""
by_severity = defaultdict(int)
by_type = defaultdict(int)
for bug in bugs:
by_severity[bug['severity']] += 1
by_type[bug['type']] += 1
# Mermaid pie chart
lines = ["pie showData", " title Bug Distribution by Severity"]
for severity, count in by_severity.items():
lines.append(f' "{severity}" : {count}')
return '\n'.join(lines)
pie showData
title Bug Distribution by Severity
"CRITICAL" : 133
"HIGH" : 180
"MEDIUM" : 212
"LOW" : 145
def generate_learning_curve(metrics):
"""Generate learning progress chart."""
lines = [
"xychart-beta",
" title Learning Progress",
' x-axis ["Step 1", "Step 2", "Step 3", "Step 4", "Step 5"]',
f' y-axis "Loss" 0 --> 1',
f' line [{", ".join(str(m["loss"]) for m in metrics)}]'
]
return '\n'.join(lines)
sequenceDiagram
participant User
participant Plugin
participant MCP
participant Episodic
participant Semantic
participant Storage
User->>Plugin: /remember "Fixed bug"
Plugin->>MCP: create_episode()
MCP->>Episodic: store()
Episodic->>Storage: insert(Neo4j)
Episodic->>Storage: embed(Qdrant)
Storage-->>MCP: success
MCP-->>Plugin: episode_id
Plugin-->>User: Stored!
Visualizations can be generated as:
# Generate knowledge graph
/ww-visualize graph --limit 50 --min-weight 0.3
# Generate timeline
/ww-visualize timeline --days 7
# Generate architecture
/ww-visualize architecture
# Generate bug chart
/ww-visualize bugs --from /home/aaron/mem/MASTER_BUG_LIST.md
This skill integrates with:
Generated visualizations:
/home/aaron/mem/WW_GRAPH_{timestamp}.md - Knowledge graphs/home/aaron/mem/WW_TIMELINE_{timestamp}.md - Timelines/home/aaron/mem/WW_ARCHITECTURE_{timestamp}.md - Architecture/home/aaron/ww/docs/diagrams/ - Persistent diagramsIf visualization fails: