Create Viz

by anthropicsae1513ea94dcNo license27K starsListed Oct 8, 2026Updated Oct 8, 2026Repository updated today

Create publication-quality visualizations with Python. Use when turning query results or a DataFrame into a chart, selecting the right chart type for a trend or comparison, generating a plot for a report or presentation, or needing an interactive chart with hover and zoom.

FeaturedInstructions onlyData & Analytics
AI-generated overview

Generates publication-quality charts from data using Python, choosing chart types and applying design best practices.

What it does
Turns query results, pasted data, or CSV/Excel files into charts using Python libraries such as matplotlib, seaborn, and plotly. It recommends a chart type based on the data relationship, writes the plotting code, applies design and accuracy guidelines, and saves the result as a PNG. It also returns the code so the user can modify it and suggests variations.
When to use it
Use it when you need to turn data or a DataFrame into a chart for exploration, a report, a presentation, or a dashboard. It also fits when you want help selecting an appropriate chart type for a trend, comparison, distribution, or correlation, or when you need an interactive chart with hover and zoom.
Requirements
Requires a Python environment with pandas and a plotting library such as matplotlib, seaborn, or plotly. Data may come from a connected data warehouse, pasted input, or a CSV/Excel file. It ships no scripts; it is instructions only.

/create-viz - Create Visualizations

If you see unfamiliar placeholders or need to check which tools are connected, see CONNECTORS.md.

Create publication-quality data visualizations using Python. Generates charts from data with best practices for clarity, accuracy, and design.

Usage

/create-viz <data source> [chart type] [additional instructions]

Workflow

1. Understand the Request

Determine:

  • Data source: Query results, pasted data, CSV/Excel file, or data to be queried
  • Chart type: Explicitly requested or needs to be recommended
  • Purpose: Exploration, presentation, report, dashboard component
  • Audience: Technical team, executives, external stakeholders

2. Get the Data

If data warehouse is connected and data needs querying:

  1. Write and execute the query
  2. Load results into a pandas DataFrame

If data is pasted or uploaded:

  1. Parse the data into a pandas DataFrame
  2. Clean and prepare as needed (type conversions, null handling)

If data is from a previous analysis in the conversation:

  1. Reference the existing data

3. Select Chart Type

If the user didn't specify a chart type, recommend one based on the data and question:

Data RelationshipRecommended Chart
Trend over timeLine chart
Comparison across categoriesBar chart (horizontal if many categories)
Part-to-whole compositionStacked bar or area chart (avoid pie charts unless <6 categories)
Distribution of valuesHistogram or box plot
Correlation between two variablesScatter plot
Two-variable comparison over timeDual-axis line or grouped bar
Geographic dataChoropleth map
RankingHorizontal bar chart
Flow or processSankey diagram
Matrix of relationshipsHeatmap

Explain the recommendation briefly if the user didn't specify.

4. Generate the Visualization

Write Python code using one of these libraries based on the need:

  • matplotlib + seaborn: Best for static, publication-quality charts. Default choice.
  • plotly: Best for interactive charts or when the user requests interactivity.

Code requirements:

python
import matplotlib.pyplot as pltimport seaborn as snsimport pandas as pd
# Set professional styleplt.style.use('seaborn-v0_8-whitegrid')sns.set_palette("husl")
# Create figure with appropriate sizefig, ax = plt.subplots(figsize=(10, 6))
# [chart-specific code]
# Always include:ax.set_title('Clear, Descriptive Title', fontsize=14, fontweight='bold')ax.set_xlabel('X-Axis Label', fontsize=11)ax.set_ylabel('Y-Axis Label', fontsize=11)
# Format numbers appropriately# - Percentages: '45.2%' not '0.452'# - Currency: '$1.2M' not '1200000'# - Large numbers: '2.3K' or '1.5M' not '2300' or '1500000'
# Remove chart junkax.spines['top'].set_visible(False)ax.spines['right'].set_visible(False)
plt.tight_layout()plt.savefig('chart_name.png', dpi=150, bbox_inches='tight')plt.show()

5. Apply Design Best Practices

Color:

  • Use a consistent, colorblind-friendly palette
  • Use color meaningfully (not decoratively)
  • Highlight the key data point or trend with a contrasting color
  • Grey out less important reference data

Typography:

  • Descriptive title that states the insight, not just the metric (e.g., "Revenue grew 23% YoY" not "Revenue by Month")
  • Readable axis labels (not rotated 90 degrees if avoidable)
  • Data labels on key points when they add clarity

Layout:

  • Appropriate whitespace and margins
  • Legend placement that doesn't obscure data
  • Sorted categories by value (not alphabetically) unless there's a natural order

Accuracy:

  • Y-axis starts at zero for bar charts
  • No misleading axis breaks without clear notation
  • Consistent scales when comparing panels
  • Appropriate precision (don't show 10 decimal places)

6. Save and Present

  1. Save the chart as a PNG file with descriptive name
  2. Display the chart to the user
  3. Provide the code used so they can modify it
  4. Suggest variations (different chart type, different grouping, zoomed time range)

Examples

/create-viz Show monthly revenue for the last 12 months as a line chart with the trend highlighted
/create-viz Here's our NPS data by product: [pastes data]. Create a horizontal bar chart ranking products by score.
/create-viz Query the orders table and create a heatmap of order volume by day-of-week and hour

Tips

  • If you want interactive charts (hover, zoom, filter), mention "interactive" and Claude will use plotly
  • Specify "presentation" if you need larger fonts and higher contrast
  • You can request multiple charts at once (e.g., "create a 2x2 grid of charts showing...")
  • Charts are saved to your current directory as PNG files

Source and attribution

Source:anthropics/knowledge-work-pluginsindata/skills/create-vizat commitae1513e

License: No license

Content belongs to its original authors. SourceWeft indexes it from a public repository.

Report or request removal

More from anthropics/knowledge-work-plugins

Ticket Deflector

anthropics

Featured

Reads a forwarded customer email or ticket, pulls order and refund status from a payments connector (PayPal, Square, or Stripe) or Shopify, account history from the CRM, and open tickets from a support desk (Zoho Desk), drafts a tone-matched reply in the owner's writing voice, and can issue a refund through the payments connector with explicit owner approval. With Shopify connected it also runs a proactive order-triage mode that surfaces orders needing attention — unfulfilled past the promised window, payment problems, pending refunds, stuck shipments — and drafts the next action for each before the customer has to ask. Use when the user says "draft a response," "answer this customer," "where's my order," "I want a refund," "check my orders," or "anything about to blow up."

Awaiting classification27Kupdated today

Tax Season Organizer

anthropics

Featured

Prepares tax-season materials for the owner's accountant, not tax advice. US federal tax; a non-US business gets its closed-books packet instead. Two modes: (1) quarterly estimated tax from YTD net income in the ledger (MYOB, NetSuite, QuickBooks, Xero, or Zoho Books); (2) year-end 1099 prep, scanning the ledger, PayPal, and Stripe for contractors paid over USD 600 into a 1099-NEC list with missing W-9 flags. Any tax request routes first to /tax-prep, which confirms the books are closed and reconciled before running this skill. Use this skill directly only when the owner says the period's books are already closed: "books are closed, now do the 1099s," "run the quarterly estimate off the closed numbers," or "just the contractor W-9 list."

Awaiting classification27Kupdated today

Tax Prep

anthropics

Featured

Prepares tax materials from closed books: a quarterly estimated payment breakdown or a year-end 1099-NEC list and accountant packet.

Business & Finance27Kupdated today

Smb Onboard

anthropics

Featured

Guides a small-business owner through first-time setup: connecting tools, running a value-proof recipe, capturing business context, and setting a weekly…

Productivity & Workflow27Kupdated today

Smb Router

anthropics

Featured

Routes a small-business owner's request to the right plugin skill or command and explains what is available.

Productivity & Workflow27Kupdated today

Month End Prep

anthropics

Featured

Reconciles the accounting ledger (MYOB, NetSuite, QuickBooks, Xero, or Zoho Books) against PayPal, Shopify, Square, and Stripe settlements, flags transactions that need attention, suspicious duplicates, and missing receipts, then writes a plain-English P&L narrative and exports a close packet (xlsx + one-page PDF). This is the first link of the /close-month command; a request to close the month or the books routes there, and the command runs this skill before refreshing the forecast and distributing the packet. Use this skill directly only when the owner wants the reconciliation alone, with no forecast refresh and no distribution: "just reconcile, no packet," "what's missing from the books," "flag the duplicates and missing receipts," or "write the P&L narrative for this month."

Awaiting classification27Kupdated today

More in Data & Analytics

Content Strategy

anthropics

Featured

Turns PayPal, QuickBooks or Square sales data into a prioritized 30-day content brief for small businesses.

Data & Analytics27Kupdated today

Cash Flow Snapshot

anthropics

Featured

Builds a 30/60/90-day cash flow forecast with confidence bands and named risk flags from ledger, payment or CSV data.

Data & Analytics27Kupdated today

Pipeline Review

anthropics

Featured

Reviews CRM sales pipeline health by stage, flagging aging, stale and at-risk deals and checking coverage.

Data & Analytics27Kupdated today

Metrics Review

anthropics

Featured

Reviews product metrics, analyzes trends against targets, and produces a scorecard with recommended actions.

Data & Analytics27Kupdated today

Performance Report

anthropics

Featured

Builds marketing performance reports with key metrics, trend analysis, wins and misses, and prioritized optimization recommendations.

Data & Analytics27Kupdated today

People Report

anthropics

Featured

Generates HR people-analytics reports on headcount, attrition, diversity, and org health from workforce data.

Data & Analytics27Kupdated today