Turn CSV, JSON, or Excel data into publication-ready charts with Python: load it with pandas and render bar, line, scatter, and statistical plots in matplotlib (with seaborn styling), then export high-DPI PNG or SVG.
Open the source and read safety notes before installing.
Source-backed facts for citing this resource, derived directly from the registry — also available as plain text for AI assistants.
Decision playbook
Signals are comparatively strong, but you should still validate source, privacy posture, and package provenance for your environment.
0
96
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No baseline selected
No major trust-signal divergence detected in the current selection.
Confirm ownership and provenance before trusting install instructions.
Source link availableRequired
Open the canonical repository and verify ownership.
Source provenance statusRequired
Marked as first-party.
Metadata reviewed
Registry metadata indicates a reviewed listing.
Validate risk disclosures before installation or API wiring.
Safety notes presentRequired
Review the listed safety guidance before running commands.
Privacy notes presentRequired
Review data handling notes before connecting accounts or secrets.
Trust level risk gateRequired
Trust level does not block evaluation.
Check package metadata and artifact integrity signals.
Install payload available
Install or copy payload is available for review.
Package verification flag
Package marked verified.
Checksum metadata
SHA-256 hash is present.
Use compare context to validate trade-offs before adoption.
Compare tray has multiple entries
Add at least one more entry to compare trust differences.
Baseline comparison available
No baseline peer selected yet.
Diverging trust signals identified
No major trust-signal divergence found.
Setup at a glance
Copy-ready — paste the snippet to get started.
Adoption plan
Current risk score 0/100. Use staged verification before broader rollout.
Validate source and review signals before any execution.
Confirm source provenanceRequired
Source URL/provenance metadata is present.
Confirm metadata review state
Listing has review metadata.
Verify install payload
Install/config payload exists and can be inspected.
Confirm safety, privacy, and package integrity signals.
Review safety notesRequired
Safety notes are present.
Review privacy notesRequired
Privacy notes are present.
Verify package integrity metadata
Package verification/checksum metadata is available.
Adopt in controlled steps based on the selected plan.
Run in isolated sandbox firstRequired
Use a constrained sandbox and observe behavior across multiple tasks.
Roll out graduallyRequired
Roll out to a small cohort before wider usage.
Set monitoring and fallback
Define rollback path and monitor errors after adoption.
Evidence readiness
Required evidence gates are covered (6/6 signals complete).
Source repository/provenance is listed.
Required in this preset
Review metadata is present.
Required in this preset
Safety notes are present.
Required in this preset
Privacy notes are present.
Optional in this preset
Package integrity metadata is present.
Optional in this preset
Install payload is available.
Required in this preset
Required evidence gates are covered for this preset.
Decision timeline
6/6 steps complete with no blocking gaps for this preset.
triage
Source/provenance metadata is available.
triage
Review metadata is available.
verify
Safety notes are available.
verify
Privacy notes are available.
verify
Package integrity metadata is available.
rollout
Install payload is available.
No required blockers for this timeline preset.
Prerequisite readiness
6 prerequisites to line up before setup.
Safety & privacy surface
1 safety and 1 privacy notes across 2 risk areas. Review closely: network access.
| Platform | Support | Install path |
|---|---|---|
| claude-code | Native | .claude/skills/<skill-name>/SKILL.md |
| codex | Native | .agents/skills/<skill-name>/SKILL.md |
| windsurf | Native | .windsurf/skills/<skill-name>/SKILL.md |
| gemini | Native | .gemini/skills/<skill-name>/SKILL.md or .agents/skills/<skill-name>/SKILL.md |
| cursor | Adapter | .cursor/rules/<skill-name>.mdc |
| cli | Manual | AGENTS.md or tool-specific context file |
import pandas as pd
import matplotlib.pyplot as plt
df = pd.read_csv('data.csv')
df.groupby('category')['value'].sum().plot(kind='bar')
plt.tight_layout(); plt.savefig('chart.png', dpi=200)Claude can create charts and visualizations from your data (CSV, JSON, Excel) using matplotlib, seaborn, plotly, or other visualization libraries. Generate publication-ready charts, dashboards, and data visualizations with custom styling.
SKILL.md.SKILL.md content as reusable workflow instructions..gemini/skills/<skill-name>/SKILL.md or .agents/skills/<skill-name>/SKILL.md where supported..cursor/rules/*.mdc adapter for project rules.Required:
What Claude handles:
Prompt: "Create a bar chart from this CSV showing sales by category. Make it professional-looking with labels and save as chart.png"
Claude will:
Prompt: "Plot this time series data: dates on x-axis, values on y-axis. Show trend line and save as SVG."
Claude will:
Prompt: "Create a 2x2 grid of charts from this data:
Claude will:
Prompt: "Create an interactive plotly chart with hover tooltips and zoom. Save as HTML."
Claude will:
"Create a sales dashboard from this data:
1. Line chart: monthly revenue trend
2. Bar chart: top 10 products by sales
3. Pie chart: sales by region
4. Table: key metrics summary
Use a professional color scheme and save as sales_dashboard.png"
"Visualize this dataset statistically:
1. Histogram with distribution curve
2. Box plot showing quartiles
3. Scatter plot with correlation
4. Heatmap of correlations between variables
Add statistical annotations and save as analysis.png"
"Compare Year 2024 vs 2025 data:
1. Side-by-side bar charts
2. Percentage change annotations
3. Highlight positive/negative changes with colors
4. Add summary statistics
Make it presentation-ready"
"Create a chart matching our brand:
- Primary color: #FF6B35
- Font: Arial
- Style: minimalist, no grid lines
- Background: white
- High DPI for print (300 dpi)
Show monthly data as area chart"
This skill leans on the Python plotting stack; pick the library by output type:
| Library | Type | Notable for |
|---|---|---|
| Matplotlib | Foundational plotting | Maximum control and publication-quality static charts |
| Seaborn | Statistical viz on top of Matplotlib | Concise statistical plots with good defaults |
| Plotly | Interactive visualization | Interactive HTML charts and dashboards |
Use Matplotlib for precise static figures, Seaborn for quick statistical charts, or Plotly when you need interactivity in a browser.
Issue: Charts look cluttered Solution: "Simplify the chart: remove grid, use fewer colors, increase spacing"
Issue: Text too small or overlapping Solution: "Increase font size to 12pt and rotate x-axis labels 45 degrees"
Issue: Colors don't match brand Solution: Provide hex codes: "Use #FF6B35 for primary, #4ECDC4 for secondary"
Issue: Export quality is poor Solution: "Export at 300 DPI for print quality" or "Use vector format (SVG/PDF)"
Issue: Legend blocks data Solution: "Move legend outside plot area to the right" or "Use smaller legend with abbreviations"
Issue: Date axis formatting is wrong Solution: "Format x-axis dates as 'MMM YYYY' with one tick per month"
Show that CLI Data Visualization Quickstart Skill is listed on HeyClaude. Paste this Markdown into your README — it renders the badge and links back to this page.
[](https://heyclau.de/entry/skills/cli-data-viz-quickstart)CLI Data Visualization Quickstart Skill side by side with 3 alternatives on trust, install, platform support, and disclosed safety notes — all from reviewed registry metadata.
2 trust signals differ across this comparison (Package trust, Source provenance).
| Field | Turn CSV, JSON, or Excel data into publication-ready charts with Python: load it with pandas and render bar, line, scatter, and statistical plots in matplotlib (with seaborn styling), then export high-DPI PNG or SVG. Open dossier | Clean, filter, join, pivot, and export CSV/XLSX data reliably with reproducible steps. Transform messy spreadsheets into production-ready datasets with pandas. Handle encoding issues, data type conversion, missing values, duplicates, and complex merges. Open dossier | Microsoft-maintained Fabric skill bundles for AI coding assistants working with Warehouses, Lakehouses, Spark, Power BI semantic models, Eventhouse/KQL, Eventstreams, Dataflows Gen2, migrations, and medallion architectures. Open dossier | MIT-licensed BrowserAct Agent Skill pack for installing and operating the `browser-act` browser automation CLI from Claude Code, Codex, OpenClaw, Cursor, OpenCode, Windsurf, Gemini CLI, and other skills-compatible agents. Open dossier |
|---|---|---|---|---|
| Next steps | ||||
| Trust | ||||
| Review status | ReviewedMaintainer reviewed | ReviewedMaintainer reviewed | ReviewedMaintainer reviewed | ReviewedMaintainer reviewed |
| Package trustDiffers | Package verified2025-10-15 | Package verified2025-10-15 | Package not verified | Package not verified |
| Source provenanceDiffers | Source-backed | No submission link | Source-backed | Source-backed |
| Submitter | — | — | — | — |
| Install risk | Review first | Low risk | Review first | Review first |
| Notes | Safety ✓ Privacy ✓ | Safety ✓ Privacy ✓ | Safety ✓ Privacy ✓ | Safety ✓ Privacy ✓ |
| Brand | — | — | ||
| Category | skills | skills | skills | skills |
| Source | First-party | First-party | Source-backed | Source-backed |
| Author | JSONbored | JSONbored | Microsoft | BrowserAct |
| Added | 2025-10-15 | 2025-10-15 | 2026-06-18 | 2026-06-18 |
| Platforms | ||||
| Harness | ||||
| Source repo | — | — | — | — |
| Safety notes | ✓This skill installs Python plotting libraries (matplotlib, seaborn, pandas) or the maintainer-built package, runs visualization scripts, and writes image files (PNG/SVG) to disk, overwriting any existing file at the output path. | ✓Installs Python packages (pip install pandas openpyxl) and runs scripts that read and write data files, overwriting outputs at the target path; review transformations before running on important data. | ✓Fabric authoring skills can guide creation or modification of Fabric items, notebooks, schemas, ingestion pipelines, semantic models, reports, Eventstreams, Dataflows Gen2, and deployment automation. Consumption skills may query live Warehouses, Lakehouses, Power BI semantic models, Eventhouse/KQL databases, and catalog metadata; use read-only roles for exploration. Operations skills can investigate performance, health, and slow-query behavior using workspace and workload telemetry; validate before applying generated tuning or remediation steps. Migration and medallion architecture skills can propose broad data-platform changes; review storage paths, costs, retention, governance, and downstream BI impact before execution. The included MCP setup scripts register external Fabric MCP servers only; they do not create, host, or secure a Fabric MCP server for you. | ✓BrowserAct can open pages, click, type, upload files, inspect state, capture screenshots, read page text, handle dialogs, export cookies, capture network requests, and operate logged-in browser sessions. Use BrowserAct only on sites, accounts, and data sources where the user has authorization. Do not use it to evade access controls, violate site terms, scrape disallowed data, or bypass rate limits. The entry skill declares confirmation gates for browser creation, deletion, local Chrome profile import, proxy/security changes, logins, form submissions, file uploads, and other sensitive operations; preserve those gates in agent workflows. `solve-captcha` may send the challenge image to BrowserAct's verification-assistance service according to the skill metadata; do not use it with sensitive or unauthorized pages. `remote-assist` can generate a live handoff URL for a human to take over. Treat that URL as access to the active browser session. Skill Forge can generate reusable automation skills from explored sites. Review generated scripts, selectors, network assumptions, output schemas, and site authorization before reusing them at scale. |
| Privacy notes | ✓Your data files (CSV/JSON/Excel) are read and rendered locally; nothing leaves the machine beyond the initial package downloads, but generated charts can embed values from the source data. | ✓Claude Pro / Code Interpreter workflows require uploading CSV or Excel files to the Claude conversation for remote processing; local pandas runs read files on your machine. Generated outputs, logs, and conversation history can contain values from your source data — review before sharing. | ✓Fabric workspace names, tenant IDs, subscription IDs, item IDs, schemas, table names, query text, logs, semantic model metadata, report definitions, and sample data may enter prompts or tool outputs. Do not paste Azure access tokens, Fabric API tokens, connection strings, service principals, workspace secrets, customer data, or regulated datasets into prompts, public issues, screenshots, or committed configs. If you register an external Fabric MCP server, queries and metadata are sent to that server according to its own auth, logging, and retention behavior. Use least-privilege Fabric roles, development workspaces, sample data, or obfuscated datasets when testing assistant-generated Fabric workflows. | ✓BrowserAct workflows can expose page content, screenshots, URLs, credentials typed into forms, cookies, browser profiles, uploaded files, downloaded files, network requests, HAR data, session names, browser descriptions, and logs. The BrowserAct skill metadata states that cookies, login sessions, page content, credentials, and browser profile data stay local, except the CAPTCHA challenge image when `solve-captcha` is invoked. Chrome-direct and profile import workflows can connect agents to existing local browser state. Treat those modes as account access, not a blank test browser. Log reports, feedback, Discord support, generated Skill Forge packages, and shared screenshots can leak private browsing or account context if submitted without review. Managed proxy, stealth browser, and API-key features create additional BrowserAct service dependencies beyond local CLI execution. |
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| Config | — | — | — | — |
| Citations | ||||
| Claim | Unclaimed | Unclaimed | Unclaimed | Unclaimed |
Clean, filter, join, pivot, and export CSV/XLSX data reliably with reproducible steps.
Fabric skill bundles for Copilot CLI, Claude Code, Codex, Cursor, and Windsurf covering authoring, consumption, operations, migrations, and Fabric MCP setup.
Source-backed guides for putting this to work.
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