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Top cs figure

Skill zhiming33416/top-cs-paper-skills/skills/top-cs-figure

Evidence-grounded Codex skills for writing, polishing, reviewing, reviewer responses, and figures for top CS conferences.

Install
npx -y skills add zhiming33416/top-cs-paper-skills --skill top-cs-figure

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

2 things to look at

  • 24 days oldThe repository was created 24 days ago. New is not bad, but a brand new repository carrying a familiar-sounding name is the shape a typosquat arrives in, and there has been no time for anyone else to find a problem with it.
  • 1 stars1 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

Copied from the file, not written here

Create, revise, audit, render, and export publication-ready computer-science paper figures using a Python-first, corpus-calibrated workflow. Use for manuscript figures, multi-panel experimental charts, comparison plots, scaling curves, heatmaps, embeddings, network diagrams, method schematics, executable YAML/CSV figure render specs, figure-brief handoffs, caption/callout alignment, SVG/PDF/PNG/TIFF export bundles, venue-style evidence checks, and visual QA for WWW, ICLR, ICML, or generic top-CS submissions. Prefer an existing top-cs figure brief or render spec when provided; otherwise build a compact figure contract before plotting. Do not use for interactive dashboards, Plotly/Altair/web apps, Illustrator/Figma-first layout, AI-generated graphical abstracts, or result-table writing.

SKILL.md

3.4 KB, as published. Nobody here has run it

Top CS Figure

Use the skill's static/dynamic layers; do not reconstruct visual grammar or export rules from memory.

Route the request

  1. Read manifest.yaml and every path under always_load.
  2. Detect venue, paper_type, and visual_family; resolve figure_task, data_state, output_target, and figure_handoff as runtime parameters.
  3. State the route, data boundary, and output target in one short line.
  4. Load only the files mapped to the selected axis values.
  5. Open references/ only when an on_demand condition applies, especially corpus visual style, statistics, archetypes, accessibility, provenance, revision audit, or spec rendering for figure production tasks.
  6. Use generic for unsupported venue/year combinations and tell the user to verify current official formatting rules.

Execute

  1. Prefer the shared figure-brief contract when present. Preserve figure_id, claim_ids, research question, evidence status, panel jobs, caption draft, and manuscript callout.
  2. If no brief exists, create a minimal figure contract before code: claim, data source, panel map, visual family, metric direction, final size, export formats, and unresolved inputs.
  3. Treat data availability as binding. Use supplied data for results; when data are missing, return missing-inputs or a clearly labeled layout mockup without invented values.
  4. Use scripts/render_from_figure_spec.py when a YAML render spec and CSV/image source can make the figure repeatable. Prefer strict v3 specs; migrate v1/v2 inputs explicitly and record the migration.
  5. Use Python/matplotlib for all plotting, previewing, exporting, and visual QA. Do not add R, Plotly, Altair, OpenRouter, or image-generation routes.
  6. Keep method colors, dataset labels, metric direction, baseline order, and terminology aligned with the manuscript.
  7. Resolve color through palette_profile: require promoted style evidence and a usable co-occurrence profile for venue-derived mode; otherwise retain generic semantic roles. Report observed anchors, constructed tokens, accessibility metrics, and fallback reason. Never present corpus defaults as official venue rules or acceptance advice.
  8. Export SVG as the primary editable artifact, plus PDF and PNG preview by default; add TIFF only for camera-ready or explicit submission bundles.
  9. Require plotted-data CSV, normalized spec, render manifest, SVG/PDF/PNG, and structured QA for produced result figures. Run scripts/audit_figure_spec.py for a reproducibility record or revision comparison.
  10. Return the fixed output sections from static/core/output-format.md, including unresolved inputs and verification status.

Keep looking

Skills are one crate of 328,083. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.