academic-figure

$npx mdskill add joshua-zyy/academic-paper-writer/academic-figure

Generates and audits academic data/result figures for CS/AI/ML papers using Python-generated editable SVG

  • Identifies user needs for generating or auditing academic figures from experiment results or numeric data.
  • Deploys Python-based tools to create, revise, or audit figures with specific design rules for benchmarks and ablations.
  • Applies CS/AI/ML-specific guidelines for various types of charts such as confusion matrices, training curves, and efficiency tradeoffs.
  • Delivers generated or audited figures directly to the user in editable SVG format.

SKILL.md

.github/skills/academic-figureView on GitHub ↗
---
name: academic-figure
description: "Create, revise, or audit academic data/result figures for CS/AI/ML papers. Data/result plots default to Python-generated editable SVG with CS/AI/ML-specific design rules for benchmarks, ablations, training dynamics, robustness, diagnostics, distributions, confusion matrices, and efficiency tradeoffs. Use when: generating plots from experiment results or numeric data, auditing publication figures, suggesting data-driven figure types, revising chart colors/layouts/labels, or preparing figure QA reports. Model framework diagrams, architecture diagrams, overview diagrams, and complex mechanism schematics are outside this skill's automatic drawing scope; provide only manual figure requirements or caption/blueprint notes when needed. Triggers on: 绘图, figure, chart, 画图, 实验图, 训练曲线, 消融实验, 对比图, 混淆矩阵, 结果图, 性能图, 鲁棒性图, 效率图, plot, publication figure, 数据可视化, generate plot, figure blueprint, 建议图表类型, figure audit, 审查图表, figure revision, 修改图表."
---

# Academic Figure

CS/AI/ML academic-figure router. 实验数据图默认交付 Python/matplotlib 可编辑 SVG,并执行 CS/AI/ML 图表设计 gate。模型框架图、架构图、overview 图和复杂机制图不属于本 skill 的自动绘制范围;如论文需要此类图,只能输出人工绘制需求、证据清单、caption 草案或 figure blueprint notes。

## Router Protocol

1. Read `manifest.yaml`. It declares `always_load` files, `axes`, and `references.on_demand`.
2. Read every file listed under `always_load`. These are the skill's binding rules — not reference material.
3. Apply the loaded material as constraints:
   - `stance.md` defines Python-only plotting, figure contract, visual policy, and scope.
   - `red-lines.md` defines absolute prohibitions. Do not negotiate these.
   - `output-contract.md` defines deliverables per mode.
   - `anti-patterns.md` defines known failure modes and their correct alternatives.
4. Select exactly one `mode` from the manifest. If ambiguous, ask one concise clarification only when data source or target use is missing. Requests for model framework, architecture, overview, or mechanism diagrams use `figure-blueprint` only to produce `manual_figure_needed` notes; never render images, SVG, or prompts.
5. Echo the selected mode to the user before executing.
6. Reach for `references/` only when the manifest's `references.on_demand` condition is satisfied.

## Modes

| Mode | Use when |
|---|---|
| `chart-from-data` | Data files or numeric results, needs publication plot with CS/AI/ML chart design gate |
| `figure-blueprint` | Wants figure suggestions for a paper section |
| `figure-audit` | Existing figure reviewed for publication readiness |
| `figure-revision` | Existing figure needs revision |

## Agent Dispatch

`agents/figure_agent.md` is dispatched by the orchestrator at Step 6.4. The agent returns figure artifacts, scripts, SVG paths, and reports; it must not independently edit project source code or experimental data.

## Completion Criteria

- `chart-from-data`: Figure Contract, CS/AI/ML chart design gate, Python script, source data, editable SVG, QA report — all pass.
- `figure-audit`: Every QA item has pass/fail status and concrete remediation.
- `figure-blueprint`: Every suggested figure maps to a paper claim and data/evidence source.
- `figure-revision`: Revised artifact or instructions, QA report, unchanged evidence traceability.

More from joshua-zyy/academic-paper-writer

SkillDescription
academic-citationSearch, verify, and map citations for CS/AI/ML papers. Produces VERIFIED/UNVERIFIED reference lists with Citation-to-Claim maps and Exemplar Sets. Use when: finding references for a paper section, verifying citation accuracy, building exemplar sets for introduction/related work learning, checking if existing citations are real and accurate, supplementing local literature library. Triggers on: 找引用, 文献检索, citation pass, find references, reference check, 补文献, citation verification, Exemplar Set, search papers, verify citation, 核验文献, 查引用, literature search, reference verification, citation verification with reading, 引用确认, 全文阅读验证.
academic-experimentsAudit, run, or verify experimental evidence for CS/AI/ML papers. Produces Evidence Inventory with evidence_type annotations (newly_run/preexisting_artifact/user_claim) and Protocol Risk assessments. Use when: checking if experiment results are reproducible, auditing existing experiment artifacts, running minimal reproducible commands, evaluating checkpoints without full retraining, documenting protocol risks like data leakage or missing baselines. Triggers on: 复核实验, run experiments, 实验结果, experiment evidence, verify results, 实验验证, evidence inventory, protocol risk, 跑实验, check results, reproduce experiments, 实验审计.
academic-latex-layoutUse when working on academic paper LaTeX layout, figure/table float placement, draft-to-template LaTeX generation, existing .tex project layout repair, figures piling up, figures far from text, large blank areas, two-column figure/table placement, page-limit layout cleanup, or compiled PDF layout review.
academic-paper-writerUse when writing or revising CS/AI/ML papers from research notes, code repositories, local literature libraries, target venue requirements, or section-level drafting requests. Triggers include 写论文, 初稿, paper draft, write introduction, write method, full paper outline, section-by-section drafting, 证据闭环, 论文起草, 从零写论文, 逐节写作.
academic-polishingPolish academic prose, de-AI-ify text, control claim strength, or rewrite method sections for CS/AI/ML papers. Executes Prose Quality Gate, Claim Strength Audit, and de-AI pass. Use when: removing AI writing patterns from paper text, adjusting claim strength to match evidence level, rewriting method sections with proper narrative flow, improving academic writing quality, checking for overclaiming. Triggers on: 润色, polish, improve writing, 去AI, de-AI, claim strength, 改写, rewrite method, prose quality, 降级表述, remove AI patterns, academic writing polish, 学术润色, 去AI化, 降级结论, improve prose.
academic-reviserSelf-review, audit, or verify CS/AI/ML paper drafts as a critical peer reviewer. Three-round review (evidence→argument→style) with Verification Status and debt tracking. Use when: reviewing a paper draft before submission, checking evidence compliance of claims, simulating peer reviewer feedback, verifying citation closure and evidence debts, performing cross-section consistency checks. Triggers on: self review, 自查, verification, 审稿, evidence compliance, peer review, 论文审查, draft audit, 验证论文, 检查引用, cross-section review, 审修, draft verification.
academic-venue-researchResearch target venue requirements and writing style for CS/AI/ML papers. Produces venue-brief.md with submission requirements and detailed writing style analysis. Use when: researching target journal/conference requirements, analyzing writing style of target venue, generating venue brief for paper writing, checking submission guidelines. Triggers on: venue research, 期刊调研, 目标期刊, writing style analysis, 写作风格分析, venue requirements, submission guidelines, 投稿要求, journal style, conference format.