human-draw
SolidRenders information as a picture a person reads at a glance. Seven shapes — bar, spine, tree, lane, fork, matrix, small-multiple — drawn in printable ASCII on a monospace grid. Works on any subject: a budget, a harvest, a rota, a roof, a decision. Trigger phrases: "/human-draw", "draw this", "show me this visually", "make a diagram of this", "I can't hold all this in my head". Not /human-output (governs the prose around the figure, and runs alongside this skill rather than before it). Not /human-rewrite (repairs existing text, and hands material here when it turns out relational). This skill builds the text figure that survives copy-paste into any terminal.
Install
Quality Score: 86/100
Skill Content
Details
- Author
- allemaar
- Repository
- allemaar/open-skills
- Created
- 2 months ago
- Last Updated
- yesterday
- Language
- JavaScript
- License
- Apache-2.0
Integrates with
Similar Skills
Semantically similar based on skill content — not just same category
human-output
The contract for writing anything a person will read and decide from — a report, a finding, an answer, a request for a decision. Works on any subject: research, legal, finance, operations, planning, code. Referenced by other skills and invocable directly before a long piece of work. Trigger phrases: "/human-output", "remember I am a person not a machine", "write this for a human", "keep the reader in mind". Not /human-rewrite (repairs text that already exists) or /human-draw (builds a picture) — human-output governs writing as it happens.
cvpr-figure
Create, revise and audit publication-grade pipeline, framework, teaser and module figures for CVPR, ICCV, ECCV, NeurIPS, ICLR, ICML, AAAI and ACL papers, and export them as Visio-editable .vsdx, PowerPoint-editable .pptx, plus SVG/PDF/PNG/EMF for LaTeX. Use for architecture diagrams, method overview figures, framework figures, pipeline diagrams, teaser/paradigm-comparison figures, attention and token diagrams, module zoom-ins, and 顶会论文配图、框架图、流程图、pipeline图、方法图、teaser图、 网络结构图、模型架构图、科研绘图、论文示意图、Visio可编辑图. Drives a declarative spec through a deterministic layout engine whose palette, typography and geometry were measured out of 349 figure PDFs from published CVPR/ICCV/ECCV/AAAI papers, then audits the result against a checklist of the things that make a diagram look machine-generated. Works from a paper section, an abstract, a method description, a PyTorch nn.Module source tree, or an mmengine/mmdet `model = dict(...)` config — 根据代码出图、根据论文内容出图. Do not use for data plots — bar charts, curves, heatmaps, scatter and
draw-the-system-not-your-study
Use at study design when allocating figure slots, and again at analysis and writing, on tasks where the source's own rendered figures are not available to copy. Covers the four slots reserved for the system before any hypothesis claims one, drawing the loaded arrays instead of their counts, why a panel that reports a shortfall is not the panel carrying the result, and why a deliverable marked covered_by an artifact path is not covered.