Skill: okaris/data-visualization-2 (version 0.1.5)

--- name: data-visualization description: "Data visualization with chart selection, color theory, and annotation best practices. Covers chart types (bar, line, scatter, heatmap), axes rules, and storytelling with data. Use for: charts, graphs, dashboards, reports, presentations, infographics, data stories. Triggers: data visualization, chart, graph, data chart, bar chart, line chart, scatter plot, data viz, visualization, dashboard chart, infographic data, data presentation, chart design, plot, heatmap, pie chart alternative" allowed-tools: Bash(infsh *) --- # Data Visualization Create clear, effective data visualizations via [inference.sh](https://inference.sh) CLI. ## Quick Start ```bash 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](https://cli.inference.sh) 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](https://dist.inference.sh/cli/checksums.txt) 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 ```python # 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) ```bash 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) ```bash 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 ```bash 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 ```bash 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 ```bash 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 ```bash 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`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: suspicious | SkillSpector: clean (LOW)