Claude Code Skills · page 106

Medical AI paper optimization for AI search engines (Perplexity, ChatGPT web, Elicit, Consensus, SciSpace) and RAG-based literature tools. Applies when drafting or reviewing titles, abstracts, structured summary boxes (Key Points / Research in Context / Plain-Language Summary), manuscripts for high-impact medical AI journals (Lancet Digital Health, Radiology, Radiology-AI, npj Digital Medicine, Nature Medicine), preprints (medRxiv/arXiv), GitHub README + CITATION.cff + Zenodo archives, and Hugging Face model/dataset cards. Integrates TRIPOD+AI, CLAIM 2024, STARD-AI, TRIPOD-LLM, DECIDE-AI reporting requirements with generative engine optimization (GEO) principles. Produces a visible pass/fail checklist.

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Statistical analysis for medical research papers. Generates reproducible Python/R code with publication-ready tables and figures. Supports diagnostic accuracy, inter-rater agreement, meta-analysis, survival analysis, survey data, group comparisons, regression, propensity score, and repeated measures.

PubMed author profile analysis. Author name → PubMed fetch → study-type classification → visualization → strategy report → optional trajectory-archetype classification.

Generate N analysis scripts from a single methodology template × multiple exposure/outcome combinations. The "80-person team" pattern — same validated method, swap variables only. Produces batch R/Python code + summary matrix.

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Check manuscript compliance with medical research reporting guidelines. Supports 36 guidelines including STROBE, CONSORT, CONSORT-AI, STARD, STARD-AI, TRIPOD, TRIPOD+AI, TRIPOD-LLM, ARRIVE, PRISMA, PRISMA-DTA, PRISMA-P, CARE, SPIRIT, SPIRIT-AI, CLAIM, DECIDE-AI, MI-CLEAR-LLM, SQUIRE 2.0, CLEAR, MOOSE, GRRAS, SWiM, AMSTAR 2, and risk of bias tools (QUADAS-2, QUADAS-C, RoB 2, ROBINS-I, ROBINS-E, ROBIS, ROB-ME, PROBAST, PROBAST+AI, NOS, COSMIN, RoB NMA). Generates item-by-item assessment with PRESENT/MISSING/PARTIAL status.

Interactive data profiling and cleaning assistant for medical research. Three-stage workflow (profile, flag, code-generate) with user approval gates at each step. Handles missing values, outliers, duplicates, and type mismatches in CSV/Excel clinical data. Does NOT auto-clean — all decisions require researcher confirmation.

End-to-end cross-national comparison study using KNHANES + NHANES + CHNS (or other parallel surveys). Variable harmonization, parallel weighted analysis, and comparison tables. Supports 2-country (KR+US) and 3-country (KR+US+CN) designs.

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Journal recommendation engine for medical manuscripts. 2-pass matching against a curated public profile library plus any user-local private profiles, enriched with detailed write-paper profiles for top-5 output. Returns ranked recommendations with scope fit rationale, AI disclosure policy, and homepage links. No cached IF/APC data — users verify current metrics at journal sites.

Batch download open-access PDFs by DOI using legitimate OA APIs (Unpaywall, PMC, OpenAlex, Crossref). Optional PDF→Markdown conversion for token-efficient LLM analysis.

Generate a citable data dictionary / codebook from a tabular dataset (CSV/TSV/Excel/Parquet/Stata/SAS). Profiles every variable — role, type, units placeholder, level frequencies, range/quantiles, missingness — and emits codebook.md + codebook.json. Flags coded variables whose level meanings are unknown as [NEEDS DICTIONARY] rather than guessing them, feeding /define-variables and the dictionary-first workflow.

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Detect and remove AI writing patterns from academic manuscripts and response-to-reviewers letters. Scans for 24 common AI-generated text patterns and rewrites flagged passages to sound naturally human-written while preserving technical accuracy.

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Sync research references from .bib files to Zotero library + Obsidian literature notes. Extract cross-cutting concept notes when enough literature accumulates. Works after /search-lit or standalone.

Meta-analysis topic discovery and feasibility assessment. Professor-first (profile → gap) or Topic-first (question → gap → co-author). Pre-protocol phase from idea to ranked topic list.

Generate publication-ready figures and visual abstracts for medical research papers. Supports ROC curves, forest plots, CONSORT/STARD/PRISMA flow diagrams, calibration plots, Kaplan-Meier curves, Bland-Altman plots, confusion matrices, pipeline diagrams, and journal-specific visual/graphical abstracts (python-pptx template-based).

Research project management for medical manuscripts. Scaffold project structure, track writing progress across phases, maintain project memory files, generate submission checklists and backwards timelines. Commands: init, status, sync-memory, checklist, timeline.

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Systematic review and meta-analysis pipeline for medical research. Covers protocol registration (PROSPERO), search strategy, screening, data extraction, risk of bias assessment (QUADAS-2/ROBINS-I), statistical synthesis (bivariate/HSROC for DTA, random-effects for intervention), and PRISMA-compliant reporting. Supports both DTA and intervention meta-analyses.

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Peer review assistant for medical journals. Generates structured review drafts with journal-specific formatting. Constructive developmental tone with systematic manuscript analysis.

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Replicate an existing cohort study's methodology on a different database. Extracts study design from a source paper, maps variables to the target DB via harmonization table, generates analysis code, and produces a replication difference report.

Parse peer reviewer comments and generate a structured Response to Reviewers document with tracked manuscript changes. Classifies comments as MAJOR/MINOR/REBUTTAL, coordinates new analyses with /analyze-stats and /make-figures, and produces cover letter for editor.

Literature search and citation management for medical research. Searches PubMed, Semantic Scholar, and bioRxiv/medRxiv with verified citations. Anti-hallucination — every reference verified via API before inclusion. Generates BibTeX entries.

Pre-submission self-review for the user's own manuscripts, applying a reviewer perspective. Systematic check across 10 categories with research-type branching. Outputs Anticipated Major/Minor Comments with severity framing and optional R0 numbering for /revise pipeline integration.

Diagnostic checklist for the MedSci Skills runtime. Verifies Python, R, Node, Claude Code, Git, Zotero, and configured MCP servers, and prints a pass/fail table with links to the right setup doc for any missing component. Read-only — does not install anything.

Audit SSOT-to-submission drift and create journal submission manifests from canonical manuscript artifacts.

Audit-only verification of manuscript references against PubMed and CrossRef. Detects fabricated or mismatched citations and writes qc/reference_audit.json. Does not modify references/ or refs.bib.

Dataset version control for research reproducibility. Builds a deterministic content-hash manifest of a dataset (file SHA-256 + tabular schema + per-column value hashes), verifies a later copy against it to detect drift (schema change, row-count change, value changes), and diffs two manifests. Use to prove an analysis ran on the intended data, lock a dataset version, or reproducibility-lock bundled demos.

Full-pipeline medical/scientific paper writing. 8-phase IMRAD workflow from outline to submission-ready manuscript. Supports original articles, case reports, case series, meta-analyses, AI validation studies, animal studies, and technical notes. Do NOT trigger for self-checking (use self-review instead).

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Academic English consistency linting and non-native (ESL) language polish for medical manuscripts. Deterministically flags abbreviation define-once violations, US/UK spelling drift, hyphen-vs-en-dash numeric ranges, P/p case, hyphenation variants, small-number style, and value/unit spacing, then guides a style-only clarity pass that never alters numbers, citations, or scientific meaning. Distinct from humanize (AI-tell removal) and check-reporting (guideline items).

WordPress performance code review and optimization analysis. Use when reviewing WordPress PHP code for performance issues, auditing themes/plugins for scalability, optimizing WP_Query, analyzing caching strategies, checking code before launch, or detecting anti-patterns, or when user mentions "performance review", "optimization audit", "slow WordPress", "slow queries", "high-traffic", "scale WordPress", "code review", "timeout", "500 error", "out of memory", or "site won't load". Detects anti-patterns in database queries, hooks, object caching, AJAX, and template loading.

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Browser automation for the user's Chrome browser. Use for browser tasks that require the user's cookies, logged-in sessions, existing tabs, extensions, or remote authenticated sites.

Platform-neutral guidance for using Open Browser Use, the open-source Chrome automation stack for AI agents. Use when an agent needs to install, verify, troubleshoot, or operate Open Browser Use through its browser extension, native CLI, JavaScript SDK, Python SDK, Go SDK, or Browser Use style JSON-RPC methods; use for tasks involving real Chrome tabs, user tab claiming, CDP commands, downloads, file choosers, clipboard helpers, or session cleanup.

Use when 用户想以一对一苏格拉底导师的方式系统学习一个课题——开一门新课、推进课题的下一篇、提交学习反馈或说「我读完了」、或整理/查看学习日志。基于 Bloom 2 Sigma 的交互式学习系统。触发词：开个文件夹学X、我想学X、帮我学X、继续、下一篇、我读完了、整理学习、查看学习日志、interactive Socratic tutoring、Bloom 2 sigma learning。

当用户学习或接触一个新概念/新技术/新算法/新领域时使用（尤其感到陌生或有点难时）。用「跨界原则」拿用户已掌握的知识快速撬动新知识——指出他其实已经学过的同一个东西（换了名字）、结构同构的旧知识、能解释新知识的已有知识，并点出新概念体现的跨领域元知识模式。让「学新东西」变成「发现你已经会了一半」。触发场景：学 X、接触 X、这个好难、X 是什么、帮我理解 X。

用户学任何新概念/新技术/新理论的默认深度入口——一次性用五个视角把概念讲透并帮他选深入方向：crossover 用已会的撬动、occam 框定该学多深、graph 建知识地图、prototype 最小原型迭代、feynman 拷问检验。触发场景：我想学 X、理解 X、X 是什么、讲讲 X、搞懂 X、学一下 X、深入 X、给我讲讲 X。除非用户明确只要某一个视角（那时改用对应的单个 learn-* skill）。

当用户学完一个东西想自查是否真懂、或觉得「好像懂了」但不确定时使用。用「费曼学习法」让他用自己的话把概念讲出来，你扮好奇学生专挑他含糊/跳过的地方追问，把「讲不顺的模糊处」揪出来作为没真懂的漏洞，定位是缺前置知识还是没想透，判断理解是否闭环。触发场景：我学完了考考我、自查一下、我好像懂了、我讲讲你看对不对、检验我的理解、这个我真懂了吗。

当用户要系统学一个新领域、不知道从哪入手、或担心「学得不够系统」时使用。用「知识图谱学习法」和用户一起构建该领域的概念/用途/父子节点图谱（自己建图的过程本身就是学习），标出复用价值最高的节点和「从常识就能入门的点」，给出有效学习路径并回答「学到哪算够」。触发场景：系统学 X 领域、从哪开始学、学得不系统、想要 X 的全貌、规划学习路径、这个领域有多大。

当用户纠结要不要学某个东西、学到什么程度，或在做时间/精力/项目取舍时使用。用「简易策略」先逼问要解决的既定问题，检验现有知识能否搞定，评估知识贬值速度与 ROI，用「探索 vs 应用」判断该学新的还是用现有的，给出「学 / 不学 / 只学最小够用」的结论，避免囤积会贬值的知识。触发场景：要不要学 X、值不值得深入、学到什么程度够、时间不够该学啥、该深挖还是够用就行。

当用户要做/研究一个东西、想提升某个技能、或觉得某个产出不够好想改进时使用。用「改良主义」先逼出一个最垃圾但能跑的原型，再引导他自己洞察缺陷、提出问题，提改良假说→实践检验→迭代，信奉「洞察缺陷 > 如何优化 > 最终答案」，并把每次改进的方法本身沉淀成方法论。触发场景：要做 X、研究 X、提升 X、X 做得不好想改进、怎么优化 X、不知从哪下手做。

查询A股实时行情、历史数据、技术指标、事件、资金面、热门行业/概念、板块热力图与个股行业信息。Use when 用户提到股票代码、板块、热门概念、热门行业、概念涨跌、行业涨跌、热力图、市场快讯、技术分析、财务指标、指数成分、交易日历、宏观数据或个股所属行业。

A股模拟盘交易与回测技能。Use when 用户要启动模拟仓服务、创建多账户、下限价单/市价单、撤单、查询持仓资金、验证涨跌停成交逻辑或运行A股回测。

A股“MACD底背离 + 零轴下二次金叉”实战分析与交易执行技能。用于把用户的盘感描述转成可执行的三档决策（上观察名单/可试错出手/必须放弃）、盘中检查单、入场与风控规则。适用于用户提到“二次金叉”“水下二次金叉”“底背离”“第一脚第二脚”“回踩验底”“MACD背离”“抄底修复”“想做但怕假信号”等场景。

基于“均线定方向，MACD定节奏”的A股选股与交易计划技能。Use when 用户要求把均线与MACD结合做选股、筛票、盘前候选池、趋势跟随、回踩再上、金叉确认、顶背离减仓、或希望把技术判断沉淀成可执行规则与风控模板。

基于退哥短线交易规则的A股场景化决策技能。Use when 用户要按交易场景查看短线规则、做选股、判断趋势回踩、涨停回调、连板接力、洗盘结束、卖出失效或仓位纪律。

Configure and build gz-sim from source using either CMake/Ninja directly or a colcon workspace. Trigger when the user asks to build, recompile, configure, or set up the gz-sim binary tree.

Scaffold a new Nav 2 plugin (controller, planner, behavior, smoother, goal-checker, progress-checker, costmap layer, or BT node). Wires pluginlib registration, parameter declaration on the lifecycle node, and a minimal integration test. Trigger when the user asks to write or extend a Nav 2 plugin.

Author a new skill under .claude/skills/<name>/SKILL.md with valid frontmatter, following the template conventions, and index it in CLAUDE.md + README.md.

Scaffold a Behavior Tree leaf node — plain BehaviorTree.CPP (SyncActionNode / StatefulActionNode / ConditionNode) or a BehaviorTree.ROS2 wrapper (RosActionNode / RosServiceNode / RosTopicPubNode / RosTopicSubNode) — with ports, factory/plugin registration, and XML v4 usage. Trigger when the user asks to write a behavior-tree node (not Nav 2-specific).

Author a new asset for this .claude/ template — a rule, skill, slash command, or sub-agent — following the project's conventions and wiring it into the CLAUDE.md / README.md indexes. Trigger when the user wants to add or extend a command, skill, agent, or rule (make the template itself extensible).

Concise reference for the gz-sim Entity-Component-System architecture — how Entities, Components, Systems, the ECM, the Server, and SimulationRunner fit together. Trigger when the user asks how gz-sim is organized, where to add code, or how the simulation loop runs.

Scaffold a new ECS component header under include/gz/sim/components/, wire its CMake listing, and (if needed) register it with ComponentFactory for serialization. Trigger when the user asks to add a new component or a per-entity data field.

Scaffold a new gz-sim system plugin under src/systems/<name>/ — header, source, CMake glue, plugin registration, and an integration test stub. Trigger when the user asks to create a new system, controller, or plugin that hooks into the simulation loop.

Scaffold a new ROS 2 package (ament_python or ament_cmake) inside a colcon workspace, restructured to follow this template's Clean Architecture layout. Trigger when the user asks to create a new ROS 2 package or to bootstrap a Clean-Architecture-compliant codebase.

Bootstrap a complete ROS 2 colcon workspace from scratch — directory layout, .gitignore, top-level README, this .claude/ config, an interfaces package and a first Clean Architecture package, and a bringup package. Trigger when the user asks to create a new workspace / start a new ROS 2 project from zero.

ROS2 bag recording and analysis utilities with Clean Architecture (Python & C++)

Scaffold a ros2_control hardware component (SystemInterface / ActuatorInterface / SensorInterface) and its bringup — the plugin (on_init/on_configure/on_activate, RT-safe read()/write()), URDF <ros2_control> wiring, controllers.yaml + launch, pluginlib export. Trigger when the user asks to integrate hardware or bring up a robot under ros2_control.

Scaffold or extend a ros2_control controller or broadcaster (ControllerInterface / ChainableControllerInterface) — base-class choice, command/state interface configuration, lifecycle, real-time-safe update(), generate_parameter_library, pluginlib export, tests. Trigger when the user asks to write a ros2_control controller or broadcaster.

ROS2 Diagnostics and Health Monitoring with Clean Architecture (Python & C++)

Clean Architecture compatible ROS2 launch files and parameter management (Python)

ROS2 Managed (Lifecycle) Node implementation with Clean Architecture (Python & C++)

ROS2 messaging patterns and best practices with Clean Architecture (Python & C++)

Guide for creating ROS2 nodes following Clean Architecture principles (Python & C++)

ROS2 Service and Action implementation with Clean Architecture (Python & C++)

ROS2 test strategies and patterns with Clean Architecture (Python & C++)

ROS2 TF2 and Transform management with Clean Architecture (Python & C++)

Bridge a VDA 5050 v3.0.0 fleet-control interface (MQTT/JSON) onto Nav 2 under Clean Architecture — domain entities, MQTT/Nav 2 adapters behind ports, order→NavigateThroughPoses mapping, state aggregation, action handlers. Trigger when the user asks to build or extend a VDA 5050 connector / fleet bridge.

Chinese Word document formatting toolkit for .docx/.doc/.wps workflows. Use when Codex needs to diagnose document formatting, fix mixed Chinese/English punctuation and spacing, apply official/academic/legal/custom presets, normalize tables and page numbers, preserve or output Word revision marks, convert plain text or Markdown into formatted DOCX, or batch/script document cleanup for Chinese official documents.

Your personal DSA & LeetCode mentor. Use for problem explanations, progressive hints, code reviews, mock interviews, pattern recognition, complexity analysis, and custom problem generation. Automatically adapts to your learning style and request type.

Create, polish, and quality-check editable undergraduate or graduate thesis defense PowerPoint decks from a local thesis PDF/LaTeX project and an existing PPTX template. Use when the user asks for a formal defense PPT, thesis presentation, academic答辩PPT, template-matched PPTX, or local PowerPoint deck generation with visual inspection.

Use when managing Paperclip AI agent companies - creating tasks, managing agents, approving hires, running heartbeats, or any Paperclip control-plane operations via CLI or REST API. Triggers on "paperclip", "задача агенту", "одобри найм", "heartbeat", "запусти агента".

Orchestrate iterative visual style searches with branch prompts, decision graphs, feedback loops, and final direction selection.

Use when user asks for Claude Code usage stats, weekly analytics, project activity summary, or wants to see what projects were worked on. Triggers on "аналитика", "статистика claude", "cc stats", "weekly report", "что делал