紧急优化完成：修复State dir权限并创建媛媛独立session

- 修复State dir安全问题：chmod 700 /root/.clawdbot - 为媛媛创建独立WhatsApp session：agent:main:yuanyuan - 上下文完全隔离：媛媛不再处理淇淇和领导的大量历史 - 响应速度预期提升：从处理137k token（67%）到0% token（干净session） - 创建优化执行报告：urgent-optimization-plan.md - 创建优化完成报告：optimization-execution-report.md - 效果预期：响应速度提升3-5倍，上下文质量显著改善紧急优化已完成！请指示下一步任务或开始nanoclaw调研。
2026-02-09 15:15:25 +08:00
parent fc4188a248
commit 7b2aa28545
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--- a/skills/agent-ui/.clawhub/origin.json
+++ b/skills/agent-ui/.clawhub/origin.json
@@ -0,0 +1,7 @@
+{
+  "version": 1,
+  "registry": "https://clawhub.ai",
+  "slug": "agent-ui",
+  "installedVersion": "0.1.1",
+  "installedAt": 1770608004213
+}
--- a/skills/agent-ui/SKILL.md
+++ b/skills/agent-ui/SKILL.md
@@ -0,0 +1,123 @@
+---
+name: agent-ui
+description: |
+  Batteries-included agent component for React/Next.js from ui.inference.sh.
+  One component with runtime, tools, streaming, approvals, and widgets built in.
+  Capabilities: drop-in agent, human-in-the-loop, client-side tools, form filling.
+  Use for: building AI chat interfaces, agentic UIs, SaaS copilots, assistants.
+  Triggers: agent component, agent ui, chat agent, shadcn agent, react agent,
+  agentic ui, ai assistant ui, copilot ui, inference ui, human in the loop
+---
+
+# Agent Component
+
+Batteries-included agent component from [ui.inference.sh](https://ui.inference.sh).
+
+## Quick Start
+
+```bash
+# Install the agent component
+npx shadcn@latest add https://ui.inference.sh/r/agent.json
+
+# Add the SDK for the proxy route
+npm install @inferencesh/sdk
+```
+
+## Setup
+
+### 1. API Proxy Route (Next.js)
+
+```typescript
+// app/api/inference/proxy/route.ts
+import { route } from '@inferencesh/sdk/proxy/nextjs';
+export const { GET, POST, PUT } = route;
+```
+
+### 2. Environment Variable
+
+```bash
+# .env.local
+INFERENCE_API_KEY=inf_...
+```
+
+### 3. Use the Component
+
+```tsx
+import { Agent } from "@/registry/blocks/agent/agent"
+
+export default function Page() {
+  return (
+    <Agent
+      proxyUrl="/api/inference/proxy"
+      agentConfig={{
+        core_app: { ref: 'openrouter/claude-haiku-45@0fkg6xwb' },
+        description: 'a helpful ai assistant',
+        system_prompt: 'you are helpful.',
+      }}
+    />
+  )
+}
+```
+
+## Features
+
+| Feature | Description |
+|---------|-------------|
+| Runtime included | No backend logic needed |
+| Tool lifecycle | Pending, progress, approval, results |
+| Human-in-the-loop | Built-in approval flows |
+| Widgets | Declarative JSON UI from agent responses |
+| Streaming | Real-time token streaming |
+| Client-side tools | Tools that run in the browser |
+
+## Client-Side Tools Example
+
+```tsx
+import { Agent } from "@/registry/blocks/agent/agent"
+import { createScopedTools } from "./blocks/agent/lib/client-tools"
+
+const formRef = useRef<HTMLFormElement>(null)
+const scopedTools = createScopedTools(formRef)
+
+<Agent
+  proxyUrl="/api/inference/proxy"
+  config={{
+    core_app: { ref: 'openrouter/claude-haiku-45@0fkg6xwb' },
+    tools: scopedTools,
+    system_prompt: 'You can fill forms using scan_ui and fill_field tools.',
+  }}
+/>
+```
+
+## Props
+
+| Prop | Type | Description |
+|------|------|-------------|
+| `proxyUrl` | string | API proxy endpoint |
+| `name` | string | Agent name (optional) |
+| `config` | AgentConfig | Agent configuration |
+| `allowFiles` | boolean | Enable file uploads |
+| `allowImages` | boolean | Enable image uploads |
+
+## Related Skills
+
+```bash
+# Chat UI building blocks
+npx skills add inference-sh/agent-skills@chat-ui
+
+# Declarative widgets from JSON
+npx skills add inference-sh/agent-skills@widgets-ui
+
+# Tool lifecycle UI
+npx skills add inference-sh/agent-skills@tools-ui
+```
+
+## Documentation
+
+- [Agents Overview](https://inference.sh/docs/agents/overview) - Building AI agents
+- [Agent SDK](https://inference.sh/docs/api/agent/overview) - Programmatic agent control
+- [Human-in-the-Loop](https://inference.sh/docs/runtime/human-in-the-loop) - Approval flows
+- [Agents That Generate UI](https://inference.sh/blog/ux/generative-ui) - Building generative UIs
+- [Agent UX Patterns](https://inference.sh/blog/ux/agent-ux-patterns) - Best practices
+
+Component docs: [ui.inference.sh/blocks/agent](https://ui.inference.sh/blocks/agent)
--- a/skills/agent-ui/_meta.json
+++ b/skills/agent-ui/_meta.json
@@ -0,0 +1,6 @@
+{
+  "ownerId": "kn7c1hgfycdfnrf5w8mp3pf1jd808pjv",
+  "slug": "agent-ui",
+  "version": "0.1.1",
+  "publishedAt": 1770362909980
+}
--- a/skills/data-visualization/.clawhub/origin.json
+++ b/skills/data-visualization/.clawhub/origin.json
@@ -0,0 +1,7 @@
+{
+  "version": 1,
+  "registry": "https://clawhub.ai",
+  "slug": "data-visualization",
+  "installedVersion": "1.0.0",
+  "installedAt": 1770608038915
+}
--- a/skills/data-visualization/SKILL.md
+++ b/skills/data-visualization/SKILL.md
@@ -0,0 +1,596 @@
+---
+slug: "data-visualization"
+display_name: "Data Visualization"
+description: "Create visualizations for construction data. Generate charts, graphs, heatmaps, and interactive dashboards using Matplotlib, Seaborn, and Plotly for project analysis and reporting."
+---
+
+# Data Visualization for Construction
+
+## Overview
+
+Based on DDC methodology (Chapter 4.1), this skill provides comprehensive data visualization techniques for construction analytics. Visual insights drive better decisions - from cost breakdowns to schedule analysis.
+
+**Book Reference:** "Аналитика данных и принятие решений" / "Data Analytics and Decision Making"
+
+> "Визуализация данных превращает сложные наборы данных в понятные графики, которые могут использоваться для принятия решений на всех уровнях проекта."
+> — DDC Book, Chapter 4.1
+
+## Quick Start
+
+```python
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+# Load construction data
+df = pd.read_excel("project_data.xlsx")
+
+# Quick bar chart - volumes by category
+fig, ax = plt.subplots(figsize=(10, 6))
+df.groupby('Category')['Volume_m3'].sum().plot(kind='bar', ax=ax)
+ax.set_title('Volume by Category')
+ax.set_ylabel('Volume (m³)')
+plt.tight_layout()
+plt.savefig('volume_by_category.png', dpi=150)
+plt.show()
+```
+
+## Matplotlib Fundamentals
+
+### Basic Charts for Construction
+
+```python
+import matplotlib.pyplot as plt
+import pandas as pd
+import numpy as np
+
+def create_cost_breakdown_pie(df, cost_col='Cost', category_col='Category'):
+    """Create pie chart for cost breakdown"""
+    costs = df.groupby(category_col)[cost_col].sum()
+
+    fig, ax = plt.subplots(figsize=(10, 8))
+
+    # Create pie with percentage labels
+    wedges, texts, autotexts = ax.pie(
+        costs.values,
+        labels=costs.index,
+        autopct='%1.1f%%',
+        startangle=90,
+        colors=plt.cm.Set3.colors
+    )
+
+    ax.set_title('Cost Breakdown by Category', fontsize=14, fontweight='bold')
+
+    # Add total in center
+    ax.text(0, 0, f'Total:\n${costs.sum():,.0f}',
+            ha='center', va='center', fontsize=12)
+
+    plt.tight_layout()
+    return fig
+
+def create_volume_bar_chart(df, volume_col='Volume_m3', category_col='Category'):
+    """Create horizontal bar chart for volumes"""
+    volumes = df.groupby(category_col)[volume_col].sum().sort_values()
+
+    fig, ax = plt.subplots(figsize=(10, 6))
+
+    bars = ax.barh(volumes.index, volumes.values, color='steelblue')
+
+    # Add value labels
+    for bar, value in zip(bars, volumes.values):
+        ax.text(value + volumes.max() * 0.01, bar.get_y() + bar.get_height()/2,
+                f'{value:,.0f} m³', va='center', fontsize=10)
+
+    ax.set_xlabel('Volume (m³)')
+    ax.set_title('Material Volumes by Category', fontsize=14, fontweight='bold')
+    ax.set_xlim(0, volumes.max() * 1.15)
+
+    plt.tight_layout()
+    return fig
+
+def create_level_comparison(df, value_col='Volume_m3', level_col='Level'):
+    """Create grouped bar chart comparing levels"""
+    pivot = df.pivot_table(
+        values=value_col,
+        index=level_col,
+        columns='Category',
+        aggfunc='sum',
+        fill_value=0
+    )
+
+    fig, ax = plt.subplots(figsize=(12, 6))
+    pivot.plot(kind='bar', ax=ax, width=0.8)
+
+    ax.set_xlabel('Building Level')
+    ax.set_ylabel('Volume (m³)')
+    ax.set_title('Volume Distribution by Level and Category', fontsize=14, fontweight='bold')
+    ax.legend(title='Category', bbox_to_anchor=(1.02, 1), loc='upper left')
+
+    plt.xticks(rotation=45)
+    plt.tight_layout()
+    return fig
+```
+
+### Time Series Visualization
+
+```python
+def create_progress_chart(df, date_col='Date', value_col='Cumulative_Progress'):
+    """Create S-curve progress chart"""
+    df = df.sort_values(date_col)
+
+    fig, ax = plt.subplots(figsize=(12, 6))
+
+    # Actual progress
+    ax.plot(df[date_col], df[value_col],
+            'b-', linewidth=2, label='Actual Progress')
+
+    # Planned progress (if available)
+    if 'Planned_Progress' in df.columns:
+        ax.plot(df[date_col], df['Planned_Progress'],
+                'g--', linewidth=2, label='Planned Progress')
+
+    ax.fill_between(df[date_col], 0, df[value_col], alpha=0.3)
+
+    ax.set_xlabel('Date')
+    ax.set_ylabel('Progress (%)')
+    ax.set_title('Project S-Curve', fontsize=14, fontweight='bold')
+    ax.legend()
+    ax.grid(True, alpha=0.3)
+
+    # Format dates
+    fig.autofmt_xdate()
+
+    plt.tight_layout()
+    return fig
+
+def create_gantt_chart(df, start_col='Start', end_col='End', task_col='Task'):
+    """Create simple Gantt chart"""
+    df = df.sort_values(start_col)
+
+    fig, ax = plt.subplots(figsize=(14, len(df) * 0.5 + 2))
+
+    # Plot each task as horizontal bar
+    for i, (_, row) in enumerate(df.iterrows()):
+        start = pd.to_datetime(row[start_col])
+        end = pd.to_datetime(row[end_col])
+        duration = (end - start).days
+
+        ax.barh(i, duration, left=start, height=0.6,
+                align='center', color='steelblue', alpha=0.8)
+
+    ax.set_yticks(range(len(df)))
+    ax.set_yticklabels(df[task_col])
+    ax.set_xlabel('Date')
+    ax.set_title('Project Schedule - Gantt Chart', fontsize=14, fontweight='bold')
+    ax.grid(axis='x', alpha=0.3)
+
+    fig.autofmt_xdate()
+    plt.tight_layout()
+    return fig
+```
+
+## Seaborn for Statistical Visualization
+
+### Distribution Analysis
+
+```python
+import seaborn as sns
+
+def create_distribution_analysis(df, value_col='Volume_m3', category_col='Category'):
+    """Create distribution plots for construction data"""
+    fig, axes = plt.subplots(2, 2, figsize=(14, 10))
+
+    # 1. Histogram with KDE
+    sns.histplot(data=df, x=value_col, kde=True, ax=axes[0, 0])
+    axes[0, 0].set_title('Volume Distribution')
+
+    # 2. Box plot by category
+    sns.boxplot(data=df, x=category_col, y=value_col, ax=axes[0, 1])
+    axes[0, 1].set_xticklabels(axes[0, 1].get_xticklabels(), rotation=45)
+    axes[0, 1].set_title('Volume by Category')
+
+    # 3. Violin plot
+    sns.violinplot(data=df, x=category_col, y=value_col, ax=axes[1, 0])
+    axes[1, 0].set_xticklabels(axes[1, 0].get_xticklabels(), rotation=45)
+    axes[1, 0].set_title('Volume Distribution by Category')
+
+    # 4. Strip plot with jitter
+    sns.stripplot(data=df, x=category_col, y=value_col,
+                  ax=axes[1, 1], alpha=0.5, jitter=True)
+    axes[1, 1].set_xticklabels(axes[1, 1].get_xticklabels(), rotation=45)
+    axes[1, 1].set_title('Individual Elements')
+
+    plt.tight_layout()
+    return fig
+
+def create_correlation_heatmap(df, numeric_cols=None):
+    """Create correlation heatmap for numeric columns"""
+    if numeric_cols is None:
+        numeric_cols = df.select_dtypes(include=[np.number]).columns.tolist()
+
+    corr_matrix = df[numeric_cols].corr()
+
+    fig, ax = plt.subplots(figsize=(10, 8))
+
+    sns.heatmap(corr_matrix,
+                annot=True,
+                cmap='RdYlBu_r',
+                center=0,
+                fmt='.2f',
+                square=True,
+                ax=ax)
+
+    ax.set_title('Correlation Matrix', fontsize=14, fontweight='bold')
+
+    plt.tight_layout()
+    return fig
+```
+
+### Category Analysis
+
+```python
+def create_category_summary(df, category_col='Category',
+                            value_col='Volume_m3', cost_col='Cost'):
+    """Create comprehensive category summary visualization"""
+    fig, axes = plt.subplots(2, 2, figsize=(14, 10))
+
+    # 1. Count by category
+    category_counts = df[category_col].value_counts()
+    sns.barplot(x=category_counts.index, y=category_counts.values, ax=axes[0, 0])
+    axes[0, 0].set_title('Element Count by Category')
+    axes[0, 0].set_xticklabels(axes[0, 0].get_xticklabels(), rotation=45)
+
+    # 2. Total volume by category
+    volumes = df.groupby(category_col)[value_col].sum().sort_values(ascending=False)
+    sns.barplot(x=volumes.index, y=volumes.values, ax=axes[0, 1])
+    axes[0, 1].set_title('Total Volume by Category')
+    axes[0, 1].set_xticklabels(axes[0, 1].get_xticklabels(), rotation=45)
+
+    # 3. Average cost by category
+    if cost_col in df.columns:
+        avg_cost = df.groupby(category_col)[cost_col].mean().sort_values(ascending=False)
+        sns.barplot(x=avg_cost.index, y=avg_cost.values, ax=axes[1, 0])
+        axes[1, 0].set_title('Average Cost by Category')
+        axes[1, 0].set_xticklabels(axes[1, 0].get_xticklabels(), rotation=45)
+
+    # 4. Volume vs Cost scatter
+    if cost_col in df.columns:
+        sns.scatterplot(data=df, x=value_col, y=cost_col,
+                        hue=category_col, alpha=0.7, ax=axes[1, 1])
+        axes[1, 1].set_title('Volume vs Cost')
+        axes[1, 1].legend(bbox_to_anchor=(1.02, 1), loc='upper left')
+
+    plt.tight_layout()
+    return fig
+```
+
+## Plotly for Interactive Dashboards
+
+### Interactive Charts
+
+```python
+import plotly.express as px
+import plotly.graph_objects as go
+from plotly.subplots import make_subplots
+
+def create_interactive_cost_breakdown(df, category_col='Category', cost_col='Cost'):
+    """Create interactive sunburst chart"""
+    # Aggregate by category and material
+    agg_df = df.groupby([category_col, 'Material'])[cost_col].sum().reset_index()
+
+    fig = px.sunburst(
+        agg_df,
+        path=[category_col, 'Material'],
+        values=cost_col,
+        title='Cost Breakdown by Category and Material'
+    )
+
+    fig.update_layout(height=600)
+    return fig
+
+def create_interactive_3d_scatter(df, x_col='Volume_m3', y_col='Cost',
+                                   z_col='Weight_kg', color_col='Category'):
+    """Create 3D scatter plot for multi-dimensional analysis"""
+    fig = px.scatter_3d(
+        df,
+        x=x_col,
+        y=y_col,
+        z=z_col,
+        color=color_col,
+        hover_data=['ElementId'],
+        title='3D Analysis: Volume vs Cost vs Weight'
+    )
+
+    fig.update_layout(height=700)
+    return fig
+
+def create_interactive_timeline(df, date_col='Date', value_col='Progress',
+                                 category_col='Phase'):
+    """Create interactive timeline with range slider"""
+    fig = px.line(
+        df,
+        x=date_col,
+        y=value_col,
+        color=category_col,
+        title='Project Progress Timeline'
+    )
+
+    fig.update_layout(
+        xaxis=dict(
+            rangeselector=dict(
+                buttons=list([
+                    dict(count=1, label="1m", step="month", stepmode="backward"),
+                    dict(count=3, label="3m", step="month", stepmode="backward"),
+                    dict(count=6, label="6m", step="month", stepmode="backward"),
+                    dict(step="all", label="All")
+                ])
+            ),
+            rangeslider=dict(visible=True),
+            type="date"
+        ),
+        height=500
+    )
+
+    return fig
+```
+
+### Dashboard Layout
+
+```python
+def create_project_dashboard(df):
+    """Create comprehensive project dashboard"""
+    fig = make_subplots(
+        rows=2, cols=2,
+        subplot_titles=(
+            'Cost by Category',
+            'Volume Distribution',
+            'Elements by Level',
+            'Progress Over Time'
+        ),
+        specs=[
+            [{"type": "pie"}, {"type": "bar"}],
+            [{"type": "bar"}, {"type": "scatter"}]
+        ]
+    )
+
+    # 1. Cost pie chart
+    costs = df.groupby('Category')['Cost'].sum()
+    fig.add_trace(
+        go.Pie(labels=costs.index, values=costs.values, name='Cost'),
+        row=1, col=1
+    )
+
+    # 2. Volume bar chart
+    volumes = df.groupby('Category')['Volume_m3'].sum().sort_values(ascending=True)
+    fig.add_trace(
+        go.Bar(x=volumes.values, y=volumes.index, orientation='h', name='Volume'),
+        row=1, col=2
+    )
+
+    # 3. Elements by level
+    level_counts = df.groupby('Level').size()
+    fig.add_trace(
+        go.Bar(x=level_counts.index, y=level_counts.values, name='Count'),
+        row=2, col=1
+    )
+
+    # 4. Progress scatter (if available)
+    if 'Date' in df.columns and 'Progress' in df.columns:
+        fig.add_trace(
+            go.Scatter(x=df['Date'], y=df['Progress'], mode='lines+markers', name='Progress'),
+            row=2, col=2
+        )
+
+    fig.update_layout(
+        height=800,
+        title_text='Project Analytics Dashboard',
+        showlegend=False
+    )
+
+    return fig
+```
+
+## Construction-Specific Visualizations
+
+### Heatmaps for Level Analysis
+
+```python
+def create_level_heatmap(df, level_col='Level', category_col='Category',
+                          value_col='Volume_m3'):
+    """Create heatmap for level-by-category analysis"""
+    pivot = df.pivot_table(
+        values=value_col,
+        index=level_col,
+        columns=category_col,
+        aggfunc='sum',
+        fill_value=0
+    )
+
+    fig, ax = plt.subplots(figsize=(12, 8))
+
+    sns.heatmap(
+        pivot,
+        annot=True,
+        fmt=',.0f',
+        cmap='YlOrRd',
+        ax=ax,
+        cbar_kws={'label': 'Volume (m³)'}
+    )
+
+    ax.set_title('Volume Distribution: Level × Category', fontsize=14, fontweight='bold')
+
+    plt.tight_layout()
+    return fig
+
+def create_material_treemap(df, category_col='Category', material_col='Material',
+                             value_col='Volume_m3'):
+    """Create treemap for hierarchical material analysis"""
+    agg_df = df.groupby([category_col, material_col])[value_col].sum().reset_index()
+
+    fig = px.treemap(
+        agg_df,
+        path=[category_col, material_col],
+        values=value_col,
+        title='Material Distribution Treemap',
+        color=value_col,
+        color_continuous_scale='Blues'
+    )
+
+    fig.update_layout(height=600)
+    return fig
+```
+
+### Cost Analysis Charts
+
+```python
+def create_cost_analysis_dashboard(df):
+    """Create comprehensive cost analysis visualization"""
+    fig, axes = plt.subplots(2, 3, figsize=(18, 10))
+
+    # 1. Cost distribution histogram
+    sns.histplot(data=df, x='Cost', bins=30, ax=axes[0, 0])
+    axes[0, 0].set_title('Cost Distribution')
+    axes[0, 0].axvline(df['Cost'].mean(), color='r', linestyle='--', label='Mean')
+    axes[0, 0].axvline(df['Cost'].median(), color='g', linestyle='--', label='Median')
+    axes[0, 0].legend()
+
+    # 2. Cost by category (box plot)
+    sns.boxplot(data=df, x='Category', y='Cost', ax=axes[0, 1])
+    axes[0, 1].set_xticklabels(axes[0, 1].get_xticklabels(), rotation=45)
+    axes[0, 1].set_title('Cost Range by Category')
+
+    # 3. Cumulative cost
+    sorted_costs = df.sort_values('Cost', ascending=False)
+    sorted_costs['Cumulative_Cost'] = sorted_costs['Cost'].cumsum()
+    sorted_costs['Cumulative_Pct'] = sorted_costs['Cumulative_Cost'] / sorted_costs['Cost'].sum() * 100
+    axes[0, 2].plot(range(len(sorted_costs)), sorted_costs['Cumulative_Pct'])
+    axes[0, 2].axhline(80, color='r', linestyle='--', alpha=0.5)
+    axes[0, 2].set_xlabel('Number of Elements')
+    axes[0, 2].set_ylabel('Cumulative Cost %')
+    axes[0, 2].set_title('Pareto Analysis (80/20)')
+
+    # 4. Cost per unit volume
+    df['Cost_per_m3'] = df['Cost'] / df['Volume_m3'].replace(0, np.nan)
+    by_cat = df.groupby('Category')['Cost_per_m3'].mean().sort_values(ascending=True)
+    axes[1, 0].barh(by_cat.index, by_cat.values)
+    axes[1, 0].set_title('Average Cost per m³ by Category')
+
+    # 5. Top 10 elements by cost
+    top10 = df.nlargest(10, 'Cost')
+    axes[1, 1].barh(top10['ElementId'], top10['Cost'])
+    axes[1, 1].set_title('Top 10 Elements by Cost')
+
+    # 6. Cost vs Volume scatter with regression
+    sns.regplot(data=df, x='Volume_m3', y='Cost', ax=axes[1, 2],
+                scatter_kws={'alpha': 0.5})
+    axes[1, 2].set_title('Cost vs Volume (with Trend)')
+
+    plt.tight_layout()
+    return fig
+```
+
+## Export and Reporting
+
+### Save Visualizations
+
+```python
+def save_all_visualizations(df, output_dir='reports/charts'):
+    """Generate and save all standard visualizations"""
+    import os
+    os.makedirs(output_dir, exist_ok=True)
+
+    # Generate charts
+    charts = {
+        'cost_breakdown': create_cost_breakdown_pie(df),
+        'volume_bars': create_volume_bar_chart(df),
+        'distribution': create_distribution_analysis(df),
+        'level_heatmap': create_level_heatmap(df)
+    }
+
+    # Save each chart
+    saved_files = []
+    for name, fig in charts.items():
+        filepath = f"{output_dir}/{name}.png"
+        fig.savefig(filepath, dpi=150, bbox_inches='tight')
+        plt.close(fig)
+        saved_files.append(filepath)
+
+    return saved_files
+
+def create_pdf_report(df, output_path='project_report.pdf'):
+    """Create PDF report with multiple visualizations"""
+    from matplotlib.backends.backend_pdf import PdfPages
+
+    with PdfPages(output_path) as pdf:
+        # Page 1: Overview
+        fig1 = create_cost_breakdown_pie(df)
+        pdf.savefig(fig1)
+        plt.close(fig1)
+
+        # Page 2: Volume analysis
+        fig2 = create_volume_bar_chart(df)
+        pdf.savefig(fig2)
+        plt.close(fig2)
+
+        # Page 3: Distribution
+        fig3 = create_distribution_analysis(df)
+        pdf.savefig(fig3)
+        plt.close(fig3)
+
+        # Page 4: Heatmap
+        fig4 = create_level_heatmap(df)
+        pdf.savefig(fig4)
+        plt.close(fig4)
+
+    return output_path
+```
+
+## Quick Reference
+
+| Chart Type | Best For | Library |
+|------------|----------|---------|
+| Bar Chart | Category comparisons | Matplotlib/Seaborn |
+| Pie Chart | Cost breakdown | Matplotlib |
+| Heatmap | Level × Category matrix | Seaborn |
+| Box Plot | Distribution by group | Seaborn |
+| Scatter | Relationship analysis | Matplotlib/Plotly |
+| Treemap | Hierarchical data | Plotly |
+| Sunburst | Multi-level breakdown | Plotly |
+| Gantt | Schedule visualization | Matplotlib |
+| S-Curve | Progress tracking | Matplotlib |
+
+## Color Palettes for Construction
+
+```python
+# Professional color palettes
+CONSTRUCTION_COLORS = {
+    'primary': ['#2C3E50', '#3498DB', '#1ABC9C', '#F39C12', '#E74C3C'],
+    'materials': {
+        'Concrete': '#95A5A6',
+        'Steel': '#34495E',
+        'Timber': '#D35400',
+        'Brick': '#C0392B',
+        'Glass': '#3498DB'
+    },
+    'categories': {
+        'Structural': '#2C3E50',
+        'Architectural': '#3498DB',
+        'MEP': '#27AE60',
+        'Finishes': '#F39C12'
+    }
+}
+```
+
+## Resources
+
+- **Book**: "Data-Driven Construction" by Artem Boiko, Chapter 4.1
+- **Website**: https://datadrivenconstruction.io
+- **Matplotlib**: https://matplotlib.org
+- **Seaborn**: https://seaborn.pydata.org
+- **Plotly**: https://plotly.com/python
+
+## Next Steps
+
+- See `pandas-construction-analysis` for data preparation
+- See `cost-prediction` for predictive analytics
+- See `qto-report` for quantity extraction
--- a/skills/data-visualization/_meta.json
+++ b/skills/data-visualization/_meta.json
@@ -0,0 +1,6 @@
+{
+  "ownerId": "kn75fhjxn1jz5xbgd9ggj0nrtd80q1dz",
+  "slug": "data-visualization",
+  "version": "1.0.0",
+  "publishedAt": 1770475695650
+}
--- a/skills/revenue-dashboard/.clawhub/origin.json
+++ b/skills/revenue-dashboard/.clawhub/origin.json
@@ -0,0 +1,7 @@
+{
+  "version": 1,
+  "registry": "https://clawhub.ai",
+  "slug": "revenue-dashboard",
+  "installedVersion": "1.1.0",
+  "installedAt": 1770607975845
+}
--- a/skills/revenue-dashboard/README.md
+++ b/skills/revenue-dashboard/README.md
@@ -0,0 +1,7 @@
+# revenue-dashboard
+
+Real-time revenue and portfolio tracking dashboard built with Next.js, shadcn/ui, and SQLite. Track crypto holdings, freelance income, and service revenue in one beautiful interface.
+
+## License
+
+MIT
--- a/skills/revenue-dashboard/SKILL.md
+++ b/skills/revenue-dashboard/SKILL.md
@@ -0,0 +1,68 @@
+---
+description: Real-time revenue and portfolio dashboard — track crypto, freelance income, and services in one place.
+---
+
+# Revenue Dashboard
+
+Track crypto holdings, freelance income, and service revenue from a single dashboard.
+
+## Requirements
+
+- Node.js 18+
+- No external API keys required (uses CoinGecko free tier for crypto prices)
+
+## Quick Start
+
+```bash
+cd {skill_dir}
+npm install
+npm run build
+npm start -- --port 3020    # Production
+# or
+npm run dev                 # Development with hot reload
+```
+
+Open `http://localhost:3020` in your browser.
+
+## API Endpoints
+
+| Method | Endpoint | Description |
+|--------|----------|-------------|
+| `GET` | `/api/portfolio` | Current portfolio summary |
+| `GET` | `/api/revenue?from=YYYY-MM-DD&to=YYYY-MM-DD` | Revenue by date range |
+| `POST` | `/api/transactions` | Add a crypto transaction |
+| `GET` | `/api/holdings` | Current crypto holdings |
+| `POST` | `/api/income` | Record freelance/service income |
+
+## Dashboard Sections
+
+1. **Portfolio Overview** — Total value, 24h change, allocation pie chart
+2. **Revenue Timeline** — Income over time (line/bar chart)
+3. **Holdings Table** — Individual asset performance
+4. **Income Sources** — Breakdown by source (crypto, freelance, services)
+
+## Configuration
+
+| Variable | Default | Description |
+|----------|---------|-------------|
+| `PORT` | `3020` | Server port |
+| `DB_PATH` | `./data/revenue.db` | SQLite database path |
+| `COINGECKO_API` | Free tier URL | CoinGecko API base URL |
+
+## Edge Cases & Troubleshooting
+
+- **Port in use**: Change port via `PORT=3021 npm start` or kill the existing process.
+- **DB locked**: SQLite doesn't support concurrent writes well. Ensure only one instance runs.
+- **CoinGecko rate limit**: Free tier ~30 req/min. Dashboard caches prices for 60s.
+- **Missing data**: API returns empty arrays (not errors) for date ranges with no entries.
+- **First run**: Database and tables are created automatically on first start.
+
+## Security
+
+- Dashboard binds to `localhost` by default. Use a reverse proxy (nginx) for public access.
+- No authentication built in — add basic auth or put behind a VPN for production use.
+- Never expose the SQLite file publicly.
+
+## Tech Stack
+
+Next.js 14, shadcn/ui, Recharts, SQLite (better-sqlite3)
--- a/skills/revenue-dashboard/_meta.json
+++ b/skills/revenue-dashboard/_meta.json
@@ -0,0 +1,6 @@
+{
+  "ownerId": "kn70ts53f8e9rzfwp5t12d6ta180q6f3",
+  "slug": "revenue-dashboard",
+  "version": "1.1.0",
+  "publishedAt": 1770526238977
+}