Search, 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.
Use 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.
Polish 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.
Self-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.
Research 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.
Use 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.
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.
Audit, 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.