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/display-quantitative-information

@3323bc9
by Tristan Manchestertristanmanchester/agent-skills3 stars
2
Run it yourself

Design, critique, or implement quantitative graphics, scientific figures, dashboards, and evidence tables. Use for display choice, scale/encoding integrity, uncertainty, and accessible chart handoff. Do not use for decorative illustration or general data cleaning without a quantitative display.

Use this Skill: https://skilld.dev/gh/tristanmanchester/agent-skills/display-quantitative-information

Nothing lands on disk. Nothing to clean up.

Fork this Skill

Edit a local copy. It keeps the author and licence.

SKILL.md

≈82 tokens for metadata: the name and description. ≈1.3k when used: this file. ≈8.1k more on demand in 18 files.

Description uses 4.1% of example budget

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  • A shorter description leaves more room for other Skills. This entry exceeds our 1% size suggestion.

In our Claude Code example, all Skill names and descriptions share 8,000 characters. This Skill uses ≈330 characters, or 4.1%.

The 1% threshold is a size suggestion. Longer descriptions can still fit.

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Example settings and source

The example uses a 200k-token context and default Claude Code settings. The count includes the name, description, separators, and when_to_use when present. Codex also counts local file paths.

Skit's source and limits: Codex 0.160.1, Claude Code 2.1.292.

Display quantitative information

Help the viewer reason from evidence. Prioritise truthful encoding and useful comparisons over cosmetic minimalism. Preserve the package's Tufte-informed methods without turning data-ink or any other maxim into an automatic verdict.

Work from the analytical task

  1. Identify the comparison or decision: lookup, trend, relationship, distribution, composition, geography, uncertainty, or monitoring.
  2. Inspect grain, units, denominator, groups, missingness, time spacing, transformations, sample size, and uncertainty before plotting. Separate observations from summaries.
  3. Choose a display for that task using display selection when needed. Establish intentional aggregation and ordering explicitly.
  4. Audit scales and encodings before aesthetics. Check actual data bounds, baseline, transformations, temporal spacing, omitted values, and uncertainty against source data.
  5. Deliver the requested chart, code, specification, or critique. Verify the output itself rather than assuming a successful plotting command preserved its meaning.

For a critique, lead with a demonstrated integrity or comparison problem. A sound chart may need no change; do not manufacture an improvement to satisfy a template. For scientific work, retain conditions, uncertainty, calibration, and raw observations where they affect interpretation. For dashboards, align time windows and denominators with the actual decision, not decorative KPI symmetry.

Use helpers deliberately

Resolve SKILL_DIR to this installed directory. Bundled scripts do not live in the target project's scripts/ directory. Read a helper's help before invoking it. The spec audit, display suggestions, lie-factor calculation, and text-fingerprint checks are heuristics or scoped calculations, not a substitute for inspecting data and the rendered graphic. A repetition score does not establish poor writing or AI authorship. Preserve clear consistent terminology instead of varying it randomly.

python "$SKILL_DIR/scripts/suggest_display.py" --csv data.csv --goal auto --format markdown
python "$SKILL_DIR/scripts/audit_visual_display.py" --spec chart.json --format markdown
python "$SKILL_DIR/scripts/render_chart_svg.py" --csv observations.csv \
  --x date --y defect_rate --chart line --x-type date --output new-chart.svg

SVG renderer contract

Choose bar, dot, line, or scatter explicitly. The renderer does not guess a chart type or silently average duplicate observations. It reads at most 10,000 rows from a CSV of at most 16 MiB. New output paths are required; no output overwrite.

  • Scatter plots retain all observations and compute both axis limits from those observations, not group means.
  • Lines use numeric x values, or --x-type date for unambiguous YYYY-MM-DD dates, with proportional spacing. A blank y value breaks the path; omitted rows cannot establish an unrecorded missing interval. Duplicate x values within a line series are rejected until the caller selects an appropriate aggregation/representation.
  • Bars require unique category/group pairs and include zero. Zero values have zero bar height. Dots retain observations; both preserve category encounter order.
  • Missing/non-finite/ambiguous numeric values are rejected outside the explicitly supported line-gap case. Locale numbers need deliberate normalisation first.

Optional --group, --title, --metadata, --width, and --height configure the first-pass output. Metadata records input/point counts, domains, gaps, and lack of aggregation. SVG title/description and distinct markers help, but do not certify accessibility. Inspect crowded labels, overplotting, group identification, contrast, and the source/uncertainty annotations needed for the deliverable. Use a full plotting library for intervals, complex dates, dense categories, or publication layout. SVG and optional metadata are separate writes, not an atomic two-file transaction.

References and assets

Load only the relevant material: principles, integrity, redesign, specification, accessibility, examples, and review rubric. Rubric scores organise judgement; they are not empirical accuracy measures. The language/fingerprint material is a review prompt, not an instruction to distort a good existing voice.

Keep the existing chart-spec, critique-note, and handoff templates as optional scaffolds. Preserve units, sources, denominators, uncertainty, and exact numerical meaning through revisions. A cleaner misleading chart is still misleading.

Maintenance

Run python -m unittest discover -s "$SKILL_DIR/tests" -v for the renderer regressions. These test numerical geometry and output handling, not human readability, all retained helpers, or a complete accessibility audit. The existing proprietary licence declaration is retained without a dangling LICENSE.txt link; no new redistribution permission or unavailable historical licence text is invented.

Source: SKILL.md on GitHub

Skill behaviors

skilld matched fixed text patterns in SKILL.md and file names. Patterns miss obfuscated code.

skilld run checks every file with the same patterns. It asks for approval before it loads a Skill with a behavior marked Needs approval.

Third-party checks

No alerts1mo3 checks · Risk SAFE
  • Gen Agent Trust Hub1mo

    The skill is safe to use and adheres to best practices for data visualization auditing. It provides analytical scripts to evaluate chart integrity based on Tufte's principles. All scripts use only the Python standard library, do not perform network requests, and do not access sensitive system files. A minor risk is documented regarding the attack surface for indirect prompt injection when processing untrusted user data.

  • Socket1mo

    No alerts

  • Snyk1mo

    Risk: LOW · No issues

Provenance

Signed by skilld at 3323bc9. This ties the file your Agent reads to that commit on GitHub. It does not review the instructions.

Last checked against GitHub 2 weeks ago.

Steadyupdated last month

Capability

Other metadata
compatibility
Bundled helpers require Python 3.10+ and use the standard library. The SVG renderer is a first-pass tool, not a full plotting or accessibility-validation system.
metadata
{
  "version": "2.1.0",
  "reviewed": "2026-09-13",
  "source-note": "Tufte-informed methods adapted from previously supplied study material; source book text and images are not bundled. No missing licence terms have been reconstructed."
}

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