Data Visualization

Data visualization with chart selection, color theory, and annotation best practices. Covers chart types (bar, line, scatter, heatmap), axes rules, and story...

Security Scan

The skill's content matches a data-visualization guide, but its runtime instructions push users to download and run a remote CLI and to execute code remotely without any privacy or safety guidance — a disproportionate install/execution pattern that merits caution.

Purpose & Capability

The name, description, and recipes align with a data visualization guide. However, the SKILL.md consistently steers users toward using the inference.sh CLI and its remote python-executor rather than local tooling; that dependence on a specific remote executor is not strictly necessary for a visualization guide and should be justified to users.

Instruction Scope

Instructions include a curl | sh install pipeline and multiple examples that run code via infsh app run (remote execution). The doc does not warn that your code, and any data you include, will be transmitted to inference.sh or explain how data is handled, retained, or protected. Recommending remote execution without privacy/consent details is scope creep for a guide that could equally show local commands.

Install Mechanism

Although the registry lists no install spec, the SKILL.md explicitly recommends piping https://cli.inference.sh to sh which downloads a binary from dist.inference.sh. curl | sh is a high-risk pattern (runs remote code immediately). The doc asserts checksum verification is available, but that requires users to perform manual checks; the skill provides no automated, verifiable install spec in the registry.

Credentials

The skill requests no environment variables, credentials, or config paths — that's proportionate to a documentation/recipe skill. Note: the use of a remote executor implies network transmission of code/data even though no credentials are required; the skill does not explain this.

Persistence & Privilege

The skill does not request always-on presence and has no install artifacts in the registry. It is instruction-only and does not request elevated privileges or modification of other skills.

What to consider before installing

This is a legitimate-looking visualization guide, but it repeatedly instructs you to install and use a third-party CLI (inference.sh) via curl | sh and to run code remotely. Before installing or sending any data to that service: 1) prefer running the provided Python code locally (pip install matplotlib) if you want to avoid sending data off-host; 2) if you consider using inference.sh, manually inspect their install script and verify the SHA-256 checksums from a trusted channel (do not blindly pipe to sh); 3) avoid sending sensitive or proprietary datasets to the remote executor unless you have reviewed their privacy/storage policy and trust the provider; 4) ask the publisher for an explicit privacy/data-handling statement and for an official install spec (e.g., a package in a known repo or a documented release on GitHub). These issues make the skill suspicious rather than clearly benign.

Create clear, effective data visualizations via inference.sh CLI.

Quick Start

curl -fsSL https://cli.inference.sh | sh && infsh login

# Generate a chart with Python
infsh app run infsh/python-executor --input '{
  "code": "import matplotlib.pyplot as plt\nimport matplotlib\nmatplotlib.use(\"Agg\")\n\nmonths = [\"Jan\", \"Feb\", \"Mar\", \"Apr\", \"May\", \"Jun\"]\nrevenue = [42, 48, 55, 61, 72, 89]\n\nfig, ax = plt.subplots(figsize=(10, 6))\nax.bar(months, revenue, color=\"#3b82f6\", width=0.6)\nax.set_ylabel(\"Revenue ($K)\")\nax.set_title(\"Monthly Revenue Growth\", fontweight=\"bold\")\nfor i, v in enumerate(revenue):\n    ax.text(i, v + 1, f\"${v}K\", ha=\"center\", fontweight=\"bold\")\nplt.tight_layout()\nplt.savefig(\"revenue.png\", dpi=150)\nprint(\"Saved\")"
}'

Install note: The install script only detects your OS/architecture, downloads the matching binary from dist.inference.sh, and verifies its SHA-256 checksum. No elevated permissions or background processes. Manual install & verification available.

Chart Selection Guide

Which Chart for Which Data?

Data Relationship	Best Chart	Never Use
Change over time	Line chart	Pie chart
Comparing categories	Bar chart (horizontal for many categories)	Line chart
Part of a whole	Stacked bar, treemap	Pie chart (controversial but: bar is always clearer)
Distribution	Histogram, box plot	Bar chart
Correlation	Scatter plot	Bar chart
Ranking	Horizontal bar chart	Vertical bar, pie
Geographic	Choropleth map	Bar chart
Composition over time	Stacked area chart	Multiple pie charts
Single metric	Big number (KPI card)	Any chart (overkill)
Flow / process	Sankey diagram	Bar chart

The Pie Chart Problem

Pie charts are almost always the wrong choice:

❌ Pie chart problems:
   - Hard to compare similar-sized slices
   - Can't show more than 5-6 categories
   - 3D pie charts are always wrong
   - Impossible to read exact values

✅ Use instead:
   - Horizontal bar chart (easy comparison)
   - Stacked bar (part of whole)
   - Treemap (hierarchical parts)
   - Just a table (if precision matters)

Design Rules

Axes

Rule	Why
Always start Y-axis at 0 (bar charts)	Prevents misleading visual
Line charts CAN start above 0	When showing change, not absolute values
Label both axes	Reader shouldn't have to guess units
Remove unnecessary gridlines	Reduce visual noise
Use horizontal labels	Vertical text is hard to read
Sort bar charts by value	Don't use alphabetical order unless there's a reason

Color

Principle	Application
Max 5-7 colors per chart	More becomes unreadable
Highlight one thing	Grey everything else, color the focus
Sequential for magnitude	Light → dark for low → high
Diverging for positive/negative	Red ← neutral → blue
Categorical for groups	Distinct hues, similar brightness
Colorblind-safe	Avoid red/green only — add shapes or labels
Consistent meaning	If blue = revenue, keep it blue everywhere

Good Color Palettes

# Sequential (low to high)
sequential = ["#eff6ff", "#bfdbfe", "#60a5fa", "#2563eb", "#1d4ed8"]

# Diverging (negative to positive)
diverging = ["#ef4444", "#f87171", "#d1d5db", "#34d399", "#10b981"]

# Categorical (distinct groups)
categorical = ["#3b82f6", "#f59e0b", "#10b981", "#8b5cf6", "#ef4444"]

# Colorblind-safe
cb_safe = ["#0077BB", "#33BBEE", "#009988", "#EE7733", "#CC3311"]

Text and Labels

Element	Rule
Title	States the insight, not the data type. "Revenue doubled in Q2" not "Q2 Revenue Chart"
Annotations	Call out key data points directly on the chart
Legend	Avoid if possible — label directly on chart lines/bars
Font size	Minimum 12px, 14px+ for presentations
Number format	Use K, M, B for large numbers (42K not 42,000)
Data labels	Add to bars/points when exact values matter

Chart Recipes

Line Chart (Time Series)

infsh app run infsh/python-executor --input '{
  "code": "import matplotlib.pyplot as plt\nimport matplotlib\nmatplotlib.use(\"Agg\")\n\nfig, ax = plt.subplots(figsize=(12, 6))\nfig.patch.set_facecolor(\"white\")\n\nmonths = [\"Jan\", \"Feb\", \"Mar\", \"Apr\", \"May\", \"Jun\", \"Jul\", \"Aug\", \"Sep\", \"Oct\", \"Nov\", \"Dec\"]\nthis_year = [120, 135, 148, 162, 178, 195, 210, 228, 245, 268, 290, 320]\nlast_year = [95, 102, 108, 115, 122, 130, 138, 145, 155, 165, 178, 190]\n\nax.plot(months, this_year, color=\"#3b82f6\", linewidth=2.5, marker=\"o\", markersize=6, label=\"2024\")\nax.plot(months, last_year, color=\"#94a3b8\", linewidth=2, linestyle=\"--\", label=\"2023\")\nax.fill_between(range(len(months)), last_year, this_year, alpha=0.1, color=\"#3b82f6\")\n\nax.annotate(\"$320K\", xy=(11, 320), fontsize=14, fontweight=\"bold\", color=\"#3b82f6\")\nax.annotate(\"$190K\", xy=(11, 190), fontsize=12, color=\"#94a3b8\")\n\nax.set_ylabel(\"Revenue ($K)\", fontsize=12)\nax.set_title(\"Revenue grew 68% year-over-year\", fontsize=16, fontweight=\"bold\")\nax.legend(fontsize=12)\nax.spines[\"top\"].set_visible(False)\nax.spines[\"right\"].set_visible(False)\nax.grid(axis=\"y\", alpha=0.3)\nplt.tight_layout()\nplt.savefig(\"line-chart.png\", dpi=150)\nprint(\"Saved\")"
}'

Horizontal Bar Chart (Comparison)

infsh app run infsh/python-executor --input '{
  "code": "import matplotlib.pyplot as plt\nimport matplotlib\nmatplotlib.use(\"Agg\")\n\nfig, ax = plt.subplots(figsize=(10, 6))\n\ncategories = [\"Email\", \"Social\", \"SEO\", \"Paid Ads\", \"Referral\", \"Direct\"]\nvalues = [12, 18, 35, 22, 8, 5]\ncolors = [\"#94a3b8\"] * len(values)\ncolors[2] = \"#3b82f6\"  # Highlight the winner\n\n# Sort by value\nsorted_pairs = sorted(zip(values, categories, colors))\nvalues, categories, colors = zip(*sorted_pairs)\n\nax.barh(categories, values, color=colors, height=0.6)\nfor i, v in enumerate(values):\n    ax.text(v + 0.5, i, f\"{v}%\", va=\"center\", fontsize=12, fontweight=\"bold\")\n\nax.set_xlabel(\"% of Total Traffic\", fontsize=12)\nax.set_title(\"SEO drives the most traffic\", fontsize=16, fontweight=\"bold\")\nax.spines[\"top\"].set_visible(False)\nax.spines[\"right\"].set_visible(False)\nplt.tight_layout()\nplt.savefig(\"bar-chart.png\", dpi=150)\nprint(\"Saved\")"
}'

KPI / Big Number Card

infsh app run infsh/html-to-image --input '{
  "html": "<div style=\"display:flex;gap:20px;padding:20px;background:white;font-family:system-ui\"><div style=\"background:#f8fafc;border:1px solid #e2e8f0;border-radius:12px;padding:24px;width:200px;text-align:center\"><p style=\"color:#64748b;font-size:14px;margin:0\">Monthly Revenue</p><p style=\"font-size:48px;font-weight:900;margin:8px 0;color:#1e293b\">$89K</p><p style=\"color:#22c55e;font-size:14px;margin:0\">↑ 23% vs last month</p></div><div style=\"background:#f8fafc;border:1px solid #e2e8f0;border-radius:12px;padding:24px;width:200px;text-align:center\"><p style=\"color:#64748b;font-size:14px;margin:0\">Active Users</p><p style=\"font-size:48px;font-weight:900;margin:8px 0;color:#1e293b\">12.4K</p><p style=\"color:#22c55e;font-size:14px;margin:0\">↑ 8% vs last month</p></div><div style=\"background:#f8fafc;border:1px solid #e2e8f0;border-radius:12px;padding:24px;width:200px;text-align:center\"><p style=\"color:#64748b;font-size:14px;margin:0\">Churn Rate</p><p style=\"font-size:48px;font-weight:900;margin:8px 0;color:#1e293b\">2.1%</p><p style=\"color:#ef4444;font-size:14px;margin:0\">↑ 0.3% vs last month</p></div></div>"
}'

Heatmap

infsh app run infsh/python-executor --input '{
  "code": "import matplotlib.pyplot as plt\nimport numpy as np\nimport matplotlib\nmatplotlib.use(\"Agg\")\n\nfig, ax = plt.subplots(figsize=(10, 6))\n\ndays = [\"Mon\", \"Tue\", \"Wed\", \"Thu\", \"Fri\", \"Sat\", \"Sun\"]\nhours = [\"9AM\", \"10AM\", \"11AM\", \"12PM\", \"1PM\", \"2PM\", \"3PM\", \"4PM\", \"5PM\"]\ndata = np.random.randint(10, 100, size=(len(hours), len(days)))\ndata[2][1] = 95  # Tuesday 11AM peak\ndata[2][3] = 88  # Thursday 11AM\n\nim = ax.imshow(data, cmap=\"Blues\", aspect=\"auto\")\nax.set_xticks(range(len(days)))\nax.set_yticks(range(len(hours)))\nax.set_xticklabels(days, fontsize=12)\nax.set_yticklabels(hours, fontsize=12)\n\nfor i in range(len(hours)):\n    for j in range(len(days)):\n        color = \"white\" if data[i][j] > 60 else \"black\"\n        ax.text(j, i, data[i][j], ha=\"center\", va=\"center\", fontsize=10, color=color)\n\nax.set_title(\"Website Traffic by Day & Hour\", fontsize=16, fontweight=\"bold\")\nplt.colorbar(im, label=\"Visitors\")\nplt.tight_layout()\nplt.savefig(\"heatmap.png\", dpi=150)\nprint(\"Saved\")"
}'

Storytelling with Data

The Narrative Arc

Step	What to Do	Example
1. Context	Set up what the reader needs to know	"We track customer acquisition cost monthly"
2. Tension	Show the problem or change	"CAC increased 40% in Q3"
3. Resolution	Show the insight or solution	"But LTV increased 80%, so unit economics improved"

Title as Insight

❌ Descriptive titles (what the chart shows):
   "Q3 Revenue by Product Line"
   "Monthly Active Users 2024"
   "Customer Satisfaction Survey Results"

✅ Insight titles (what the chart means):
   "Enterprise product drives 70% of revenue growth"
   "User growth accelerated after the free tier launch"
   "Support response time is the #1 satisfaction driver"

Annotation Techniques

Technique	When to Use
Call-out label	Highlight a specific data point ("Peak: 320K")
Reference line	Show target/benchmark ("Goal: 100K")
Shaded region	Mark a time period ("Product launch window")
Arrow + text	Draw attention to trend change
Before/after line	Show impact of an event

Dark Mode Charts

infsh app run infsh/python-executor --input '{
  "code": "import matplotlib.pyplot as plt\nimport matplotlib\nmatplotlib.use(\"Agg\")\n\n# Dark theme\nplt.rcParams.update({\n    \"figure.facecolor\": \"#0f172a\",\n    \"axes.facecolor\": \"#0f172a\",\n    \"axes.edgecolor\": \"#334155\",\n    \"axes.labelcolor\": \"white\",\n    \"text.color\": \"white\",\n    \"xtick.color\": \"white\",\n    \"ytick.color\": \"white\",\n    \"grid.color\": \"#1e293b\"\n})\n\nfig, ax = plt.subplots(figsize=(12, 6))\nmonths = [\"Jan\", \"Feb\", \"Mar\", \"Apr\", \"May\", \"Jun\"]\nvalues = [45, 52, 58, 72, 85, 98]\n\nax.plot(months, values, color=\"#818cf8\", linewidth=3, marker=\"o\", markersize=8)\nax.fill_between(range(len(months)), values, alpha=0.15, color=\"#818cf8\")\nax.set_title(\"MRR Growth: On track for $100K\", fontsize=18, fontweight=\"bold\")\nax.set_ylabel(\"MRR ($K)\", fontsize=13)\nax.spines[\"top\"].set_visible(False)\nax.spines[\"right\"].set_visible(False)\nax.grid(axis=\"y\", alpha=0.2)\n\nfor i, v in enumerate(values):\n    ax.annotate(f\"${v}K\", (i, v), textcoords=\"offset points\", xytext=(0, 12), ha=\"center\", fontsize=11, fontweight=\"bold\")\n\nplt.tight_layout()\nplt.savefig(\"dark-chart.png\", dpi=150, facecolor=\"#0f172a\")\nprint(\"Saved\")"
}'

Common Mistakes

Mistake	Problem	Fix
Pie charts	Hard to compare, always misleading	Use bar charts or treemaps
Y-axis not starting at 0 (bar charts)	Exaggerates differences	Start at 0 for bars, OK to truncate for lines
Too many colors	Visual noise, confusing	Max 5-7 colors, highlight only what matters
No title or generic title	Reader doesn't know the insight	Title = the takeaway, not the data type
3D charts	Distorts data, looks unprofessional	Always use 2D
Dual Y-axes	Misleading, hard to read	Use two separate charts
Alphabetical sort on bar charts	Hides the story	Sort by value (largest first)
No labels on axes	Reader can't interpret	Always label with units
Chartjunk (decorative elements)	Distracts from data	Remove everything that doesn't convey information
Red/green only for color coding	Colorblind users can't read	Use shapes, patterns, or colorblind-safe palettes

Related Skills

npx skills add inference-sh/skills@pitch-deck-visuals
npx skills add inference-sh/skills@technical-blog-writing
npx skills add inference-sh/skills@competitor-teardown

Browse all apps: infsh app list