xuzhougeng

在你投入一个研究方向之前，先看看有谁已经在做同样的事。把 idea 拆成多个不同角度分别去检索，返回一张"邻近程度地图"，重点标出最危险的撞车候选。底层走真实检索（scimaster-cli），不做主观新颖性判断。 **以下情况请主动触发本技能**：用户说"这个选题有没有人做过"、"我会不会被抢发"、"这个方向还有空间吗"、"我的 idea 新不新"、"要不要查一下有没有撞车"、"这个想法是不是已经有人发了"——即使用户没有说"bear-scoop"，只要意图是**在提交或动笔前确认 idea 的独特性**，就使用本技能。加 `-r` flag 只看近两年和预印本，适合担心被抢发的场景。 不适用于：为已有观点找支持文献（用 bear-support）、找反对文献（用 bear-counter）、概念地图（用 bear-map）、溯源演化史（用 bear-trace）。

给一个主题或一篇种子论文，向前挖掘它建立在哪些前驱工作上（逐层回溯），再向后补充最新进展，讲清楚这个问题从源头到现在是怎么演化的。底层走真实检索（scimaster-cli），每个节点都锚定真实检索到的论文。 **以下情况请主动触发本技能**：用户问"这个问题是怎么来的"、"帮我溯源这个方向"、"这个领域怎么发展的"、"这篇论文是建立在谁的基础上的"、"从头讲一下这个方向的历史"、"入门一个新领域想先了解演化脉络"——即使用户没有说"bear-trace"，只要意图是**理解一个研究问题或领域的来龙去脉**，就使用本技能。加 `-d N` 控制向前溯源的层数（默认 3 层）。 不适用于：为观点找支持文献（用 bear-support）、找反对文献（用 bear-counter）、选题查重（用 bear-scoop）、概念地图（用 bear-map）。

Create a Wisp specialist or author a project-local skill using the tools Wisp actually exposes. Use when the user wants a custom Agent persona, a restricted specialist loadout, a new skill, or changes to an existing project-local skill.

给一个概念，从真实检索到的论文摘要里挖出它的邻近概念，画出以该概念为中心的知识地图——每个节点都锚定真实文献，不从记忆里补节点。同时输出 Mermaid 概念图（可在 Claude Code / GitHub / Obsidian 渲染）和可截图的独立 HTML 地图，再给 3–6 篇入门推荐。 **以下情况请主动触发本技能**：用户说"帮我画一张这个概念的知识地图"、"这个词周围有哪些相关概念"、"梳理一下这个方向的概念网络"、"给我一张概念图"、"这个领域的核心概念有哪些"、"知识地图"、"概念地图"、"map out this concept"、"draw a knowledge map of X"、"what concepts surround X"、"show me the concept network around this term"、"concept map for X"——即使用户没有说"bear-map"，只要意图是**以一个概念为中心，了解它周围的知识版图**，就使用本技能。 不适用于：为观点找支持文献（用 bear-support）、找反对文献（用 bear-counter）、选题查重（用 bear-scoop）、溯源演化史（用 bear-trace）。

Use when creating, migrating, or debugging pixi environments, especially for scientific Python, bioinformatics, single-cell analysis, CUDA/PyTorch, Jupyter/VS Code kernels, conda-to-pixi migration, or conda + PyPI mixed dependency issues.

Audit scientific figures for duplicated, reused, transformed, or uninformative image panels. Use for 图片查重, 论文图像重复, PDF figure 审核, when the user attaches a PDF, asks to review selected PDF pages, or tags a directory containing manuscript input images. For PDFs, extract large embedded figure images before splitting them into panels; for directories, preserve originals and split every composite image directly. Produces a reviewed panel manifest, all-pairs candidate table, visual evidence, coverage accounting, and a cautious integrity report.

将概念、理论或分析方法类图书蒸馏为证据可追溯、经人工门禁审核且不暴露书名、作者、出版社等来源身份的任务型 Skill 候选。用于新建或恢复图书蒸馏、以本地 Tesseract 扫描 DOCX 全部内嵌图像或 Poppler 渲染的扫描 PDF 全页、建立 source map 与 evidence/claim/relation/capability rule、用第一性原理重构可迁移方法，以及按权威 task contract 防止上下文压缩后的产品任务漂移；仅在本元 Skill 自身的 owning distillation 中路由其 Gate 4 评测。不用于单纯摘要、事实问答、人物思维模仿、实验或临床 SOP、临床决策，也不作为其他 Skill 的通用物化、评测、Git、发布或部署工具。

Build, audit, authorize, recover, or finalize dynamic Zotero citations and bibliographies in Microsoft Word DOCX files with a protected-source, digest-bound workflow. Use for Word–Zotero citation conversion, static OOXML citation audits, mocked/offline validation, Refresh authorization/report review, UI-evidence contracts, run recovery, or rollback proposals; never perform live Word/Zotero integration without separate explicit authorization.

Build a sourced research dossier for one therapeutic indication — patient population, epidemiology, disease biology, standard of care, regulatory path, and landmark trials. Use when the user asks for an indication overview, disease landscape, or trial-design background.

Plan, validate, and document public-bioinformatics data acquisition for GEO/GSE/GSM/GPL/GDS, SRA/ENA, TCGA/GDC, GTEx, and DepMap. Covers expression matrices, raw reads, download manifests, caches, and optional geokit SOFT/Series Matrix acquisition for R workflows.

Submit recoverable SSH-direct research Runs with live progress cards and model-free monitoring.

Organize multi-step scientific analyses into reproducible, self-contained modules. Use for workflows such as QC→PCA→DEG→GSEA that produce scripts, inputs, figures, tables, and methods. Creates a stable module layout, records exact inputs/parameters/package and database versions in each module README, keeps large data as references instead of copies, and verifies outputs before completion.

Set up and validate a reproducible Python or R environment on a Wisp execution context. Use for a selected local, WSL, or direct SSH context when installing scientific packages, configuring caches, recording interpreter activation, or producing an environment smoke test. Do not use for scheduler clusters or managed cloud providers that Wisp cannot track yet.

Retrieve, verify, and synthesize scientific literature. Use for seminal-paper lookups, evidence summaries, method comparisons, and gap analyses. Every citation must come from a live lookup, never from memory; retractions are checked; the deliverable is argued prose with resolvable DOI links.

Use this skill whenever the user provides a scientific paper PDF and asks for a group-meeting literature report, journal-club slides, 文献汇报PPT, 组会PPT, paper presentation, article walkthrough, or to explain a paper with PowerPoint. The skill first reconstructs the paper's scientific logic, then builds author/background sections, chooses an evidence-driven slide outline, crops only main-text figure panels from the PDF, and creates an academic PPT with 10–30 slides including title and conclusion/discussion. Always use this skill for '把这篇论文做成PPT', '文献汇报', 'journal club', or '组会汇报', even if the user only says they need slides.

Compose or improve a publication-grade multi-panel scientific figure from a claim, concrete data paths, or an existing image. Use for figure outlining, parallel panel rendering, exact-grid composition, visual inspection, and adversarial figure review. Use figure-style for one standalone plot and paper-narrative for whole-paper figure ordering.

Prepare optional local Python/R runtimes, user-configured Python MCP servers, Node/scimaster-cli, and pixi for the user’s task. Reuse detected paths, configure Wisp interpreters, and apply mirrors when needed. Use for 配置环境, missing runtime dependencies, or requested local installations. For remote SSH compute use compute-env-setup.

Judge and reshape the story told by a manuscript and its figure deck. Use when revising paper structure, testing whether Figure 1 is a hook, ordering figures, moving panels, identifying missing analyses, or defining the claim passed to figure-composer.

Wisp-science's actual agent tool surface and runtime boundaries. Load this when deciding which Wisp tool can perform a task, checking whether Python can reach agent or desktop capabilities, choosing between interactive analysis and persisted Runs, or answering questions about delegation, images, skills, memory, artifacts, lineage, credentials, session history, and other self-introspection capabilities.

Use when designing, reviewing, or implementing single-cell RNA-seq QC in Python or R with a human-in-the-loop, data-driven approach. Trigger for scRNA QC metrics, per-sample diagnosis, threshold discussion, mitochondrial/ambient/doublet assessment, MAD vs fixed cutoffs, or refactoring automated merge-first QC. The analyst confirms key decisions at each step—agents must inspect data, propose options, and wait for approval before filtering, doublet removal, or merging. Not a turnkey pipeline skill.

Use the InfiniSynapse CLI (`agent_infini`) for multi-turn AI data-analysis tasks, database/RAG context, and task workspace files. Use when the user mentions InfiniSynapse, agent_infini, database or RAG analysis, or asks to delegate analysis through InfiniSynapse.

给一句话或一段话，找出真实学术文献来反对它——相反结论、边界条件、替代解释、方法批评、复制失败。按威胁程度排序，每篇附一行"它如何威胁这个观点"和"怎么回应它"。底层走真实检索（scimaster-cli），绝不编造反例。 **以下情况请主动触发本技能**：用户想"提前挡住审稿人"、"找这个结论的反例"、"这个方向有没有争议"、"有没有跟这个相反的研究"、"帮我攻击这个论点"、"这个结论稳不稳"、"想知道有哪些反对意见"——即使用户没有说"bear-counter"，只要意图是**为一个观点寻找学术反对证据**，就使用本技能。bear-support 和 bear-counter 配对使用效果最好：对同一段话各跑一遍，就能同时看到正反两面。 不适用于：找支持文献（用 bear-support）、选题查重（用 bear-scoop）、概念地图（用 bear-map）、溯源演化史（用 bear-trace）。

给一个概念或领域，同时画出它的概念版图（空间：现在这个领域长什么样）和演化脉络（时间：这个领域是怎么走到今天的），在一份综合报告里帮你快速建立对一个陌生领域的立体认知。底层走真实检索（scimaster-cli），绝不编造引用。 **以下情况请主动触发本技能**：用户想"快速入门一个领域"、"搞懂这个方向"、"这个领域的核心概念和发展历史"、"帮我建立对这个方向的认知框架"——即使没有说"bear-onboard"，只要意图是**对一个陌生领域同时理解概念版图和演化脉络**，就使用本技能。 不适用于：只画概念地图（用 bear-map）、只做溯源（用 bear-trace）、为观点找文献（用 bear-support / bear-counter）、选题查重（用 bear-scoop）。

给一个研究 idea，依次执行撞车检测、安静区支撑检索、潜在挑战检索，在一份综合报告里呈现这个 idea 的立项前证据格局——撞车风险在哪、安静区里有没有文献支撑、最需要提前想清楚的挑战是什么。底层走真实检索（scimaster-cli），绝不编造引用。 **以下情况请主动触发本技能**：用户想"开题前评估一下"、"这个 idea 值不值得做"、"立项前做个全面的文献调研"、"帮我评估这个研究方向"——即使没有说"bear-propose"，只要意图是**在正式投入一个研究方向之前做综合评估**，就使用本技能。 不适用于：只查撞车（用 bear-scoop）、只找支持（用 bear-support）、只找反对（用 bear-counter）、概念地图（用 bear-map）、溯源演化史（用 bear-trace）。

给一句话或一段话，找出真实学术文献来支持它，每篇文献附一行"它如何支撑这个观点"的说明。底层走真实检索（scimaster-cli），绝不从记忆编造引用——搜不到就如实说搜不到。 **以下情况请主动触发本技能**：用户想让某个观点"更有依据"、"更经得起质疑"、"加上文献支撑"、"找几篇论文"、"这个说法有没有研究"、"帮我查一下这个结论"、"审稿人会不会说没有引用"——即使用户没有说"bear-support"或"找文献"这几个字，只要意图是**为一个观点寻找学术支撑**，就使用本技能。 不适用于：找反对文献（用 bear-counter）、选题查重（用 bear-scoop）、概念地图（用 bear-map）、溯源演化史（用 bear-trace）。

Author a Wisp custom theme CSS file that uses documented tokens and import constraints. Use when the user wants a custom look, hide the paragraph lead bar, restyle chat Markdown, override colors, or import a stylesheet under Settings → Appearance.

Correctness and legibility checklist for publication figures, plus a matplotlib sidecar. Load before plotting anything and call `apply_figure_style()` (role-mapped font ladder, outward ticks, frameless legends, 300-dpi saves, CJK-safe fonts). Covers data fidelity, label budgets, axis/colour/type rules, chart choice by data shape, composition, and a mandatory render-then-inspect QA pass (bbox collisions + per-panel visual crops). Helpers: focal_palette, bar_with_points, strip_with_median, end_of_line_labels, panel_letter, set_frame, panel_crops. Multi-panel assembly lives in figure-composer; whole-paper figure ordering in paper-narrative.

给一个观点，同时检索支持它的文献和反对它的文献，在一份综合报告里呈现正反两侧的证据格局——支持侧最强证据是什么、反对侧最危险的挑战是什么、哪些角度有支持但无反对、哪些角度有反对但无支持。底层走真实检索（scimaster-cli），绝不编造引用。 **以下情况请主动触发本技能**：用户想同时看到"支持和反对"、"这个观点稳不稳"、"帮我做一个全面的论点评估"、"正反两面都找一找"——即使没有说"bear-review"，只要意图是**对一个观点做完整的证据格局分析**，就使用本技能。 不适用于：只找支持（用 bear-support）、只找反对（用 bear-counter）、选题查重（用 bear-scoop）、概念地图（用 bear-map）、溯源演化史（用 bear-trace）。

Use this skill when the user has attached a PDF, paper, report, or other document and the answer needs its content: summarize a section, compare sections, read specific pages, check the table of contents, or read a value off a figure. The `read` tool cannot parse PDF binary — python is the extraction path. Provides `pdf_pages` (pages as text or rendered PNGs, cached) and `pdf_outline` (embedded-bookmark TOC) in the persistent python kernel; load them once via the Runtime Sidecar exec line that `use_skill` appends. For PDF creation/manipulation, use reportlab/pypdf directly.

Turn a scientific conversation, paper takeaway, or figure story into paste-ready social copy. Use when the user asks for 小红书文案, a Xiaohongshu note, 微信/朋友圈, 公众号草稿, a tweet, Twitter/X copy, or share-to-social text from a Wisp session. Ask which platform unless the user already named one.