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Generates publication-quality figures for ML papers from research context. Given a paper section or description, extracts system components and relationships to generate architecture diagrams via Gemini. Given experiment results or data, auto-selects chart type and generates data-driven figures via matplotlib/seaborn.
Passed 19 of 22 test cases with the skill, and 15 of 22 without it.
and 4 more in the full report
4 fixed15 passed either way3 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 4 test cases the skill fixed. Same model, same prompt, with and without the skill.
Generate a python script `figures/gen_fig_overview.py` that calls Gemini API to create an informal overview diagram for an AI system using a hand-drawn whiteboard sketch style.
| Checked | Without skill | With skill |
|---|---|---|
| Script prompt specifies background color `#FAFAF7` | Missed | Passed |
| Script prompt specifies dark charcoal line borders | Missed | Passed |
The instructions your model reads when the skill loads.
Generate publication-quality figures for ML/AI conference papers. Two distinct workflows:
| Figure Type | Tool | Why |
|---|---|---|
| Architecture / system diagram | Gemini (Workflow 1) | Complex spatial layouts with boxes, arrows, labels |
| Workflow / pipeline / lifecycle | Gemini (Workflow 1) | Multi-step processes with connections |
| Bar chart, line plot, scatter | matplotlib (Workflow 2) | Precise numerical data, reproducible |
| Heatmap, confusion matrix | matplotlib/seaborn (Workflow 2) | Structured grid data |
| Ablation table as chart | matplotlib (Workflow 2) | Grouped bars or line comparisons |
| Pie / donut chart | matplotlib (Workflow 2) | Proportional data (use sparingly in ML papers) |
| Training curves | matplotlib (Workflow 2) | Loss/accuracy over steps/epochs |
Rule of thumb: If the figure has numerical axes, use matplotlib. If the figure has boxes and arrows, use Gemini.
The user will typically provide one of these inputs — not a ready-made specification:
| Input Type | Example | What to Extract |
|---|---|---|
| Full paper / section draft | "Here's our method section..." | System components, their relationships, data flow |
| Description paragraph | "Our system has three layers that..." | Key entities, hierarchy, connections |
| Raw results / data table | "MMLU: 85.2, HumanEval: 72.1..." | Metrics, methods, comparison structure |
| CSV / JSON data | Experiment log files | Variables, trends, grouping dimensions |
| Vague request | "Make a figure for the overview" | Read surrounding paper context to infer content |
For diagrams (research context → architecture figure):
test cases passed, vs 15 / 22 without
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