106 skills · sorted by relevance
Systematic methodology for eliciting, representing, and operationalizing expert cognitive skills—pattern recognition, situation assessment, mental simulation—from practitioners without requiring research training. Bridges academic rigor and practical usability.
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Systematic methodology for eliciting, representing, and operationalizing expert cognitive skills—pattern recognition, situation assessment, mental simulation—from practitioners without requiring research training. Bridges academic rigor and practical usability.
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Apply AlphaGo-style patterns: learned priors plus search, mixed evaluators, self-play, and asynchronous fast/slow loops. Use for intractable search and compute allocation. NOT for basic supervised learning, tractable optimization, or domains without search bottlenecks.
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Plan, draft, and revise academic papers with support for literature reviews, bilingual abstracts, citation checks, and responses to reviewers. The skill also formats manuscripts for publication requirements and produces Markdown, LaTeX, DOCX, or PDF outputs.
Assess a new or existing AI project by examining its code, goals, and constraints. Recommend skills confirmed to exist in the available registry, then outline tasks and implementation phases with dependencies, completion criteria, and checks.
Turn a repeatable task into an Andru.ia skill with clear inputs, outputs, examples, and limits. Draft SKILL.md, check the folder structure and required validations, and prepare a pull request describing the changes for review.
Set up rules that allow or block Claude Code tool calls, and record executed tool names in digitally signed receipts. The skill also explains offline verification and the limits of what those records prove.
Monitor requests to an AI prediction API for signs of model copying or training-data exposure. Test an authorized model with simulated attacks, measure the risk, and evaluate defenses such as request limits and reduced prediction detail.
Implement and debug Google OAuth 2.0 refresh tokens bound to a client key. Covers key generation, signed DPoP proofs that demonstrate key possession, and nonce challenge retries in WebCrypto and JavaScript environments.
Build Java applications with cooperating AI agents using AgentScope Java. Guides asynchronous processing, where waiting tasks do not block execution, plus language model connections, external tools, workflow coordination, error handling, and tests.
Golang CLI command tree library using spf13/cobra — cobra.Command, RunE vs Run, PersistentPreRunE hook chain, Args validators (NoArgs, ExactArgs, MatchAll, custom), persistent vs local flags, command groups, ValidArgsFunction, RegisterFlagCompletionFunc, ShellCompDirective, usage/help template customization, man-page and markdown doc generation, and testing with SetArgs/SetOut/SetErr. Apply when using or adopting spf13/cobra, or when the codebase imports `github.com/spf13/cobra`. For configuration layering alongside cobra, see the `samber/cc-skills-golang@golang-spf13-viper` skill. For general CLI architecture (project layout, exit codes, signal handling, I/O patterns), see `samber/cc-skills-golang@golang-cli`.
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Query Open Targets Platform for target-disease associations, drug target discovery, tractability/safety data, genetics/omics evidence, known drugs, for therapeutic target identification.
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Organizes the evidence and competitive landscape around a drug, target, or pathway by separating disease relevance, tractability, preclinical evidence, clinical evidence, modality fit, and crowding. Always map what is biologically supported, what is druggable, what has actually advanced, and what remains strategically open. Never confuse target relevance with druggability, preclinical activity with clinical promise, or narrative excitement with validated development maturity. Never fabricate references, trial status, approval status, company activity, or asset metadata.
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This skill should be used when the user asks to "find skills in the wild", "assimilate popular workflows", "discover SKILL.md files in repos", "research external skills", "find workflow patterns", "survey the skill landscape", "what skills exist out there", or wants to investigate public repositories for extractable processes, babysitter plugins, and reusable procedural insights. Searches GitHub for SKILL.md files, classifies repos by archetype, and maintains structured research under docs/reference-repos/.
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在为《电子学报》(Acta Electronica Sinica, AES) 稿件撰写相关工作与文献综述时调用。覆盖如何界定领域范围、系统检索中外文献(CNKI/万方/IEEE/dblp)、按技术脉络而非流水账组织综述、突出与本文的区别与本文创新点、恰当引用本刊及英文姊妹刊 Chinese Journal of Electronics 的相关论文、GB/T 7714 引用规范、以及双盲阶段自引匿名化。帮助电子/信息/信号处理方向作者写出既显示学科视野、又清晰定位本文贡献的相关工作章节，避免因综述薄弱或漏引关键文献被外审质疑创新性。
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当你为《电子学报》(Acta Electronica Sinica, AES) 投稿准备代码、数据与硬件设计等研究制品(artifact)的可用性说明时使用。中文学报暂无独立的 artifact evaluation 徽章委员会，本技能说明本刊现状、如何自愿开放代码/数据以增强稿件可信度、如何在正文与附录中规范披露制品、如何在双盲阶段匿名托管仓库、以及电子/信号处理类工作(算法、电路、FPGA、数据集)的制品打包要点。帮助作者在没有强制徽章的前提下最大化《电子学报》稿件的可复现说服力。
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在为《电子学报》(Acta Electronica Sinica, AES) 撰写审稿意见答复与修回说明时调用。适用于收到编辑部"修改后再审/退修"通知后，逐条回应外审专家意见、撰写"修改说明"对照表、在正文中标注修改痕迹、处理相互矛盾或要求补做实验的意见、把握修回时限与再审轮次。帮助电子/信息/通信/信号处理方向的中文稿件在《电子学报》多级审理中稳步走向录用，避免因答复不规范或情绪化措辞被再次退修甚至退稿。
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当你为《电子学报》(Acta Electronica Sinica, AES) 稿件强化可复现性与实验可重复时使用。覆盖电子/信息/信号处理/通信类工作的实验环境记录、随机性控制、数据与预处理透明、算法伪代码与超参数完整披露、硬件与仿真工具链版本、统计显著性与多次运行、以及在中文学报正文与附录里如何组织可复现信息。帮助作者用可复现性作为通过《电子学报》外审、抵御"结果无法重复"质疑的核心竞争力，而非事后补救。
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当稿件被《电子学报》(Acta Electronica Sinica, AES) 录用后进入定稿、校样与清样阶段时使用。覆盖录用通知后的版权协议签署、按最终排版规范核对中英文题名/摘要/关键词/中图分类号、补全双盲阶段隐去的作者信息与基金编号、GB/T 7714 参考文献终校、图表与公式的印刷级质量、校样(清样)一校二校流程、版面费缴纳、DOI 与出版时序确认。帮助电子/信息/通信方向作者把已录用稿件零差错地送到《电子学报》正式出版。
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当你需要理解《电子学报》(Acta Electronica Sinica, AES) 审稿流程、稿件状态与时限时使用。覆盖投稿受理→编辑初审→双盲外审(同行评议)→复审→主编/编委会终审→退修与再审轮次→录用的多级审理链路，各阶段可能的决定(录用/退修/退稿)、6 个月未答复红线、如何解读稿件状态、如何礼貌催稿、以及本刊与英文姊妹刊 Chinese Journal of Electronics 的分流关系。帮助电子/信息/通信方向作者对《电子学报》的审理节奏与决策点建立准确预期，合理规划投稿与修回。
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当你为《电子学报》(Acta Electronica Sinica, AES) 判断选题是否对口、定位到合适栏目/学科方向时使用。覆盖本刊电子科学与技术、信息与通信工程、信号与信息处理、电路与系统、电磁场与微波、集成电路等学科侧重，如何评估选题的创新性与本刊契合度、区别于计算机类与控制类兄弟刊、判断投中文《电子学报》还是英文姊妹刊 Chinese Journal of Electronics、以及原创论文与综述的定位差异。帮助作者在动笔前就把选题放进《电子学报》的正确坐标，避免因不对口在初审被退。
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当你按《电子学报》(Acta Electronica Sinica, AES) 规范打磨中文科技论文写作时使用。覆盖中英文题名、结构式中文摘要与英文 Abstract、中英文关键词、中图分类号(TN 大类)、量和单位与法定计量、公式与变量排版、图表规范、GB/T 7714 顺序编码制参考文献中英文著录、以及电子/信息/信号处理领域术语与数学表达的一致性。帮助作者写出符合《电子学报》体例、语言严谨、可被外审专家顺畅评阅的中文旗舰学报稿件，避免因写作不规范在初审或外审被扣分。
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当你在向《自动化学报》(Acta Automatica Sinica, AAS) 正式提交稿件前做投稿就绪审计时调用。覆盖官网 aas.net.cn 的 ScholarOne 投审稿系统(aas-cn)、双盲匿名化(删除页眉页脚/标题下方/尾注/文件名中的作者单位基金号)、全部作者签名加盖第一作者单位公章的《作者承诺》前置提交、只收原创非翻译稿与不接收 AI 署名、学术不端查重、栏目(综述/论文/短文)归属与退稿风险分诊。适用于把一份控制/自动化/模式识别方向的中文稿打磨到符合本刊双盲三审制门槛的场景。
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在向《电子学报》(Acta Electronica Sinica, AES) 正式提交稿件前做投稿就绪审计时调用。覆盖官网 ejournal.org.cn 在线投稿系统、注册与稿件同步、官方 Word/LaTeX 模板、中英文摘要与关键词、中图分类号(TN 为主)、GB/T 7714 参考文献、双盲评审的匿名化要求、稿件评审费(中文约 199 元/英文约 260 元 待核实以系统为准)、涉密脱敏、栏目归属与稿件类型选择，并在上传前做退稿风险分诊。适用于把一份电子/信息/通信/信号处理方向的中文原创论文打磨到符合本刊四级审理门槛的场景。
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