viz（即）

Viz Skill: Data Visualization and Inspection

Purpose

This skill directly executes visualizations. The calling agent provides a visualization spec along with data context, and the skill:

1. Infers the data loading code from the provided context
2. Generates the complete plotting script
3. Executes it via the viz_runner.py helper
4. Returns artifact paths for the caller to reference

Key pattern:

Caller (with data context) → Skill (infers data loading, generates script, executes) → Plot appears

The caller does NOT need to write any execution code. The skill handles everything.

Input Specification

The calling agent should provide:

Required

• Visualization spec: What to plot (chart type, axes, title, special features)

Data Context (one of these forms)

• Database + query: "Data from /full/path/to/operational_forecast.ddb, table forecast, columns month, members"
• SQL query: "Run this SQL: SELECT * FROM forecast WHERE year >= 2024"
• Code snippet: "Load data like this: df = pd.read_parquet('/full/path/to/data.parquet')"
• File path: "CSV at /tmp/data.csv with columns X, Y, Z"

CRITICAL: Absolute Paths Required

The viz_runner.py executes scripts from /tmp/viz/, NOT the caller's working directory. All file paths in generated scripts MUST be absolute paths. The calling agent should:

1. Determine the absolute path to any data files before invoking the skill
2. Pass the full absolute path in the data context
3. Never use relative paths like ./data.ddb or data.parquet

Example - WRONG:

con = duckdb.connect('operational_forecast.ddb')  # Will fail!

Example - CORRECT:

con = duckdb.connect('/Users/rob/projects/forecast/operational_forecast.ddb')

Optional

• Suggested ID: A name hint (e.g., pop_bar, churn_trend). The runner ensures uniqueness.

Intent Detection

Before generating any code, analyze the user's request to determine the appropriate mode.

Inspection Mode (use --show)

Use when the user wants to see the data itself, not a visualization:

• "Show me the dataframe"
• "Display the first N rows"
• "What does the data look like?"
• "Print the data"
• "What columns are in X?"
• "Inspect the data"
• "Let me see the data"

Action: Use --show flag. Do NOT generate plot code.

Visualization Mode (generate plot)

Use when the user wants a chart, graph, or visual representation:

• "Plot the data"
• "Create a chart of..."
• "Visualize the trend"
• "Show a graph of..."
• "Bar chart showing..."
• "Scatter plot of..."

Action: Generate matplotlib/seaborn code and pass via stdin.

Ambiguous Requests

If unclear (e.g., "show me X over time"), default to asking or interpret based on context:

• If the request mentions chart types (bar, line, scatter) → visualization
• If the request is about structure/columns/rows → inspection
• When in doubt, use --show first (it's cheaper), then offer to plot

Artifact Management

All artifacts are managed in /tmp/viz/ via the helper script.

Helper: viz_runner.py

python /Users/rob/.claude/skills/viz/viz_runner.py [--id NAME] [--desc "Description"] << 'EOF'
<generated script>
EOF

The runner:

1. Creates /tmp/viz/ if needed
2. Ensures ID uniqueness (appends _2, _3, etc. if collision)
3. Injects plt.savefig('/tmp/viz/<id>.png', dpi=150, bbox_inches='tight') before plt.show()
4. Writes the script to /tmp/viz/<id>.py
5. Executes the script
6. Writes metadata to /tmp/viz/<id>.json
7. Prints human-readable results to stdout

Output Format

Terminal output:

Plot: pop_bar
  "Bar chart of members by month"
  png: /tmp/viz/pop_bar.png

Sidecar JSON (/tmp/viz/<id>.json):

{
  "id": "pop_bar",
  "desc": "Bar chart of members by month",
  "png": "/tmp/viz/pop_bar.png",
  "script": "/tmp/viz/pop_bar.py",
  "created": "2025-01-22T11:31:00",
  "pid": 46368
}

The caller can then:

• Read the PNG into context to discuss the plot
• Reference the script for modifications
• Look up plots by ID or description via the JSON metadata

List

To see all available visualizations:

python /Users/rob/.claude/skills/viz/viz_runner.py --list

Output:

ID              Description                          Created
--------------  -----------------------------------  ----------------
pop_bar         Bar chart of members by month        2025-01-22 11:31
churn_trend     Monthly churn rate                   2025-01-22 10:45
test_scatter    -                                    2025-01-22 09:20

Cleanup

To remove all visualization files from /tmp/viz/:

python /Users/rob/.claude/skills/viz/viz_runner.py --clean

Output:

Cleaned 12 files from /tmp/viz

Skill Workflow

1. Infer data loading: From the provided context, generate Python code to load/create the DataFrame. Use absolute paths for all file references - the script runs from /tmp/viz/, not the caller's directory.
2. Generate visualization: Add matplotlib/seaborn code for the requested plot
3. Execute via runner (always include --desc with a short summary): python /Users/rob/.claude/skills/viz/viz_runner.py --id suggested_name --desc "Short description of plot" << 'EOF' <complete script> EOF
4. Parse output: Capture the ID and paths from stdout
5. Return to caller: Report final ID and paths. Do NOT read the PNG into context unless the user needs analysis.

Library Selection

Use Seaborn When:

• Statistical distributions (histogram + KDE, violin, box plots)
• Regression with confidence intervals
• Categorical comparisons with error bars
• Heatmaps and correlation matrices

Use Matplotlib When:

• Fine-grained control over appearance
• Time series with date formatting
• Custom annotations and reference lines
• Simple plots without statistical features

Combine Both:

Use seaborn for the statistical plot, matplotlib for customizations like reference lines.

Publication Quality Standards

• Labels: Descriptive axis labels with units, 12pt+ font
• Titles: Clear, informative, 14pt+ font
• Figure size: figsize=(10, 6) or appropriate aspect ratio
• Layout: Always use tight_layout() to prevent clipping
• Grids: Subtle guidance with alpha=0.3
• Colors: Colorblind-friendly palettes (viridis, coolwarm, Set2)
• Transparency: Alpha for overlapping points
• Imports: Inside the script for self-contained execution

End-to-End Example

Request from caller:

/viz id=pop_bar
     bar chart showing total_initial_members and total_final_members by month
     with dashed vertical line at history/forecast boundary (Dec 2025 / Jan 2026).
     Data from operational_forecast.ddb, forecast table.

Skill generates and executes:

python /Users/rob/.claude/skills/viz/viz_runner.py --id pop_bar --desc "Bar chart of members by month with forecast boundary" << 'EOF'
import duckdb
import matplotlib.pyplot as plt
import numpy as np

# Load data from DuckDB (MUST use absolute path!)
con = duckdb.connect('/Users/rob/projects/forecast/operational_forecast.ddb', read_only=True)
df = con.execute("""
    SELECT month, total_initial_members, total_final_members
    FROM forecast
    ORDER BY month
""").df()
con.close()

# Create grouped bar chart
fig, ax = plt.subplots(figsize=(12, 6))
x = np.arange(len(df))
width = 0.35

bars1 = ax.bar(x - width/2, df['total_initial_members'], width, label='Initial Members', color='steelblue')
bars2 = ax.bar(x + width/2, df['total_final_members'], width, label='Final Members', color='coral')

# History/forecast boundary
boundary_idx = df[df['month'] == '2025-12'].index[0] + 0.5
ax.axvline(x=boundary_idx, color='gray', linestyle='--', linewidth=1.5, label='Forecast Start')

ax.set_xlabel('Month', fontsize=12)
ax.set_ylabel('Members', fontsize=12)
ax.set_title('Member Population by Month: Historical vs Forecast', fontsize=14)
ax.set_xticks(x)
ax.set_xticklabels(df['month'], rotation=45, ha='right')
ax.legend()
ax.grid(True, alpha=0.3, axis='y')
plt.tight_layout()
plt.show()
EOF

Runner output:

Plot: pop_bar
  "Bar chart of members by month with forecast boundary"
  png: /tmp/viz/pop_bar.png

Skill returns to caller:

Plot generated successfully. - ID: pop_bar - Script: /tmp/viz/pop_bar.py - PNG: /tmp/viz/pop_bar.png

Important: Do NOT Auto-Read PNGs

Do NOT automatically read the PNG into context after generating a plot.

Reading images consumes significant context tokens and is usually unnecessary. The plot window opens automatically via plt.show(), so the user can already see the visualization.

Only read the PNG into context when:

• The user explicitly asks you to analyze or interpret the graph
• The user asks questions about what the graph shows
• You need to learn something from the visual output to answer a question

Instead of reading the PNG, offer to open it:

open /tmp/viz/pop_bar.png  # macOS

This displays the image in the system viewer without consuming context tokens.

Refinement Workflow

When refining an existing plot:

1. Caller provides the existing script path + requested changes
2. Skill reads the script, applies modifications
3. Executes with a new ID (e.g., pop_bar_2)
4. Both versions remain available for comparison

Regeneration

When a user asks to regenerate an existing plot (e.g., after data has changed):

By ID

Request: "regenerate pop_bar"

Run the saved script directly:

python /tmp/viz/pop_bar.py

The script already contains the hardcoded savefig path, so it overwrites the existing PNG.

By Description

Request: "regenerate the churn plot"

1. Run --list to find matching plot
2. Identify the ID from the description
3. Run python /tmp/viz/<id>.py

Ambiguous Request

Request: "regenerate a plot"

1. Run --list to show available plots
2. Ask user which one to regenerate
3. Run the selected script

Key Point

Regeneration does NOT require viz_runner.py - the saved .py scripts are self-contained and can be executed directly with python.

Interactive Backend Note

Generated scripts use plt.show() which works with the macosx backend for interactive display. The injected savefig() ensures a PNG copy is always saved before display.

Marimo Notebook Support

The viz skill can extract data from marimo notebooks and generate plots without modifying the original notebook.

How It Works

1. Copy notebook to /tmp/viz/<id>.py
2. Analyze dependencies to identify cells needed for target data
3. Prune unneeded cells from the copied notebook
4. Inject plotting code as a new cell at the end
5. Execute via subprocess with cwd set to original notebook's directory (so relative paths work)

CLI Interface

python /Users/rob/.claude/skills/viz/viz_runner.py \
    --marimo \
    --notebook /path/to/notebook.nb.py \
    --target-var df_forecast \
    --id forecast_plot \
    --desc "Monthly forecast visualization" \
    << 'EOF'
# Plotting code that uses df_forecast
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
ax.plot(df_forecast['date'], df_forecast['total_final_members'])
plt.show()
EOF

Parameters

• --marimo: Enable marimo notebook mode (required)
• --notebook: Path to the marimo notebook file (required)
• --target-var: Variable to extract from the notebook (required)
• --target-line: Optional line number for capturing intermediate state (for mutated variables)
• --id: Suggested ID for the visualization (optional)
• --desc: Description of the visualization (optional)
• --show: Show mode - print dataframe info to console instead of plotting (no stdin required)
• --rows: Number of rows to display in show mode (default: 5)

Dependency Analysis

Marimo notebooks encode dependencies explicitly:

• Cell parameters = variables the cell reads (refs)
• Cell return tuple = variables the cell defines (defs)

The skill walks backwards from the target variable through the dependency graph to find all required cells.

Target Line (Advanced)

When a variable is mutated within a cell, use --target-line to capture intermediate state:

@app.cell
def _(raw_data):
    df = raw_data.copy()           # line 45
    df = df[df['value'] > 0]       # line 46 - filtered
    df = df.groupby('cat').sum()   # line 47 - aggregated
    return (df,)

Use --target-var df --target-line 46 to capture df after filtering but before aggregation.

Show Mode (Data Inspection)

Use --show to print dataframe info to console instead of generating a plot. Useful for quickly inspecting data at a specific point in the notebook pipeline.

python /Users/rob/.claude/skills/viz/viz_runner.py \
    --marimo \
    --notebook /path/to/notebook.nb.py \
    --target-var df \
    --show \
    --rows 10

Output:

Shape: (12345, 5)
Columns: ['date', 'profile_id', 'kind', 'state', 'channel_type']

Dtypes:
date              datetime64[ns]
profile_id                 int64
kind                      object
state                     object
channel_type              object

First 10 rows:
        date  profile_id     kind state channel_type
0 2021-01-01      123456  monthly    CA      organic
1 2021-01-02      123457  monthly    TX         paid
...

No stdin (plot code) is required for show mode - it only prints dataframe metadata and contents.

Example Workflow

User request:

"Plot the member forecast over time from the operational forecast notebook"

Agent workflow:

1. Read the notebook to identify candidate variables
2. Ask clarifying questions if multiple candidates exist
3. Execute:

python /Users/rob/.claude/skills/viz/viz_runner.py \
    --marimo \
    --notebook /Users/rob/repos/project/forecast.nb.py \
    --target-var df_deliverable \
    --id member_forecast \
    --desc "Historical and forecast members" \
    << 'EOF'
import matplotlib.pyplot as plt

fig, ax = plt.subplots(figsize=(12, 6))
ax.plot(df_deliverable['date'], df_deliverable['total_final_members'])
ax.set_xlabel('Date')
ax.set_ylabel('Members')
ax.set_title('Member Population Over Time')
plt.tight_layout()
plt.show()
EOF

Important Notes

• The original notebook is never modified (read-only access)
• All work happens on a copy in /tmp/viz/
• The script runs with the notebook's directory as cwd, so relative file paths work
• Uses uv run python if the notebook directory contains pyproject.toml or uv.lock