How do I install the Top Cs Figure skill?

Run npx skillmds@latest add zhiming33416/top-cs-figure in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Top Cs Figure safe to use?

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Top Cs Figure?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Top Cs Figure free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Top Cs Figure?

zhiming33416 (@zhiming33416) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Top Cs Figure by zhiming33416 — Agent Skill

Name: Top Cs Figure
Author: zhiming33416

Top Cs Figure

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, and visual QA for WWW, ICLR, ICML, NeurIPS, CVPR, ACL, or generic top-CS submissions. Do not use for interactive dashboards, Plotly/Altair/web apps, Illustrator/Figma-first layout, AI-generated graphical abstracts, or result-table writing.

zhiming33416 Updated Sep 17, 2026

File contents

Top CS Figure

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

Route the request

Read manifest.yaml and every path under always_load.
Detect venue, paper_type, and visual_family; resolve figure_task, data_state, output_target, and figure_handoff as runtime parameters.
State the route, data boundary, and output target in one short line.
Load only the files mapped to the selected axis values.
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.
Use generic for unsupported venue/year combinations and tell the user to verify current official formatting rules.

Execute

Prefer an existing top-cs figure brief or render spec when provided; otherwise build a compact figure contract before plotting. Prefer the shared figure-brief contract when present. Preserve figure_id, claim_ids, research question, evidence status, panel jobs, caption draft, and manuscript callout.
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.
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.
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.
Use Python/matplotlib for all plotting, previewing, exporting, and visual QA. Do not add R, Plotly, Altair, OpenRouter, or image-generation routes.
Keep method colors, dataset labels, metric direction, baseline order, and terminology aligned with the manuscript.
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.
Export SVG as the primary editable artifact, plus PDF and PNG preview by default; add TIFF only for camera-ready or explicit submission bundles.
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.
Return the fixed output sections from static/core/output-format.md, including unresolved inputs and verification status.

---name: top-cs-figuredescription: 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, and visual QA for WWW, ICLR, ICML, NeurIPS, CVPR, ACL, or generic top-CS submissions. Do not use for interactive dashboards, Plotly/Altair/web apps, Illustrator/Figma-first layout, AI-generated graphical abstracts, or result-table writing.---# Top CS FigureUse the skill's static/dynamic layers; do not reconstruct visual grammar or export rules from memory.## Route the request1. 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.## Execute1. Prefer an existing top-cs figure brief or render spec when provided; otherwise build a compact figure contract before plotting. 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.

Frequently asked questions