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概要と使いどころ
Run nf-core bioinformatics pipelines (rnaseq, sarek, atacseq) on sequencing data. Use when analyzing RNA-seq, WGS/WES, or ATAC-seq data—either local FASTQs or public datasets from GEO/SRA. Triggers on nf-core, Nextflow, FASTQ analysis, variant calling, gene expression, differential expression, GEO reanalysis, GSE/GSM/SRR accessions, or samplesheet creation.
日本語の概要は準備中です。原文の説明を表示しています。
Generate clinical trial protocols for medical devices or drugs. This skill should be used when users say "Create a clinical trial protocol", "Generate protocol for [device/drug]", "Help me design a clinical study", "Research similar trials for [intervention]", or when developing FDA submission documentation for investigational products.
日本語の概要は準備中です。原文の説明を表示しています。
Performs quality control on single-cell RNA-seq data (.h5ad or .h5 files) using scverse best practices with MAD-based filtering and comprehensive visualizations. Use when users request QC analysis, filtering low-quality cells, assessing data quality, or following scverse/scanpy best practices for single-cell analysis.
日本語の概要は準備中です。原文の説明を表示しています。
Deep learning for single-cell analysis using scvi-tools. This skill should be used when users need (1) data integration and batch correction with scVI/scANVI, (2) ATAC-seq analysis with PeakVI, (3) CITE-seq multi-modal analysis with totalVI, (4) multiome RNA+ATAC analysis with MultiVI, (5) spatial transcriptomics deconvolution with DestVI, (6) label transfer and reference mapping with scANVI/scArches, (7) RNA velocity with veloVI, or (8) any deep learning-based single-cell method. Triggers include mentions of scVI, scANVI, totalVI, PeakVI, MultiVI, DestVI, veloVI, sysVI, scArches, variational autoencoder, VAE, batch correction, data integration, multi-modal, CITE-seq, multiome, reference mapping, latent space.
日本語の概要は準備中です。原文の説明を表示しています。
Design mandates for acceptance tests - hexagonal boundary, business language abstraction, user journey completeness, pure function extraction, 3 Pillars (domain language / chained narrative / production composition), and the layered ATD discipline (Universe-bound assertion, layer-dependent PBT mode, two-tier acceptance, example-based sad paths)
日本語の概要は準備中です。原文の説明を表示しています。
How the nWave buddy agent reads a project to answer questions — detection, order of inspection, and citation discipline.
日本語の概要は準備中です。原文の説明を表示しています。
Use when users want XiaoBei skill / xiaobei-skill / 小北在读研 style academic image-to-VBA reconstruction: convert academic figures, scientific diagrams, slides, screenshots, or other images into VBA, Office drawing code, PowerPoint/Excel/Word shapes, editable shape reconstruction, 1:1 recreation, pixel-like approximation, or hybrid editable Office shape reconstruction.
日本語の概要は準備中です。原文の説明を表示しています。
Disciplined diagnosis loop for hard bugs and performance regressions. Reproduce → minimise → hypothesise → instrument → fix → regression-test. Use when user says "diagnose this" / "debug this", reports a bug, says something is broken/throwing/failing, or describes a performance regression.
日本語の概要は準備中です。原文の説明を表示しています。
Clean-code engineering standards for writing, refactoring, and reviewing code in any programming language. Use this whenever the user asks to write clean code, follow clean-code principles, refactor for clarity, improve naming, reduce complexity or duplication, separate concerns, tighten error handling, work test-first or do TDD, or otherwise raise code quality, readability, and maintainability. Also use when writing or reviewing Operately APIs or Ecto queries. Apply these rules by default when producing or changing code for a quality-conscious user.
日本語の概要は準備中です。原文の説明を表示しています。
Modern JavaScript (ES2022+) patterns for clean, maintainable code. Use when working with modern JavaScript features like optional chaining, nullish coalescing, or ESM.
日本語の概要は準備中です。原文の説明を表示しています。
Troubleshoot systematically using the Scientific Method. Use when debugging crashes, tracing errors, diagnosing unexpected behavior, or investigating exceptions.
日本語の概要は準備中です。原文の説明を表示しています。
Apply motion design principles for mobile apps covering timing curves, transitions, gestures, and performance-conscious animations. Use when implementing screen transitions, gesture-driven interactions, shared-element animations, or optimizing animation frame rates on iOS, Android, or Flutter.
日本語の概要は準備中です。原文の説明を表示しています。
Right-size, ground, and gate SDLC decisions with SNC-sized depth, said-vs-assumed write-back, an evidence ledger, honest option cards, recorded approval, and self-review. Use when running brainstorm-feature, plan-feature, design-solution, or system-design-session.
日本語の概要は準備中です。原文の説明を表示しています。
Draws architecture diagrams as editable draw.io files with a fixed house style, C4 levels, evidence-tagged shapes, and optional multi-view identity checks. Use when producing a system context, container, component, deployment, data flow, sequence, state, or ERD, or redrawing an ASCII or Mermaid one.
日本語の概要は準備中です。原文の説明を表示しています。
Helps a teacher prepare to teach an existing K-12 lesson (math / ELA / science / social studies) — a prep partner that thinks through the lesson's key student task with the teacher and leaves a short teacher-only prep note. Triggers on asks to internalize, prepare for, get ready to teach, or understand a lesson, including implicit ones ("help me get ready for tomorrow's lesson"), and right after another skill built a lesson, which it can follow. Not for building or adapting a lesson, grading, rubrics, assessment feedback, or generating quizzes or student-facing material, and not for single-fact lookups a sentence would answer ("what standard is this?", "how long is this lesson?").
日本語の概要は準備中です。原文の説明を表示しています。
Adapts an existing K-12 lesson (math, ELA, science, or social studies) for students at different proficiency levels (below / at / above grade level). Load this skill BEFORE asking the teacher any clarifying question about the lesson, tiers, or student levels. Triggers on explicit asks to differentiate, tier, or scaffold a lesson, and on implicit signals like "my students are at different levels". Produces 1 teacher-facing differentiation plan + 3 student-ready tier documents as editable, printable documents in a single output turn (shared content is written once, so tiers cannot drift). Uses the Learning Commons Knowledge Graph when connected; works without it. This skill adapts a lesson the teacher brings or names. Not for creating a new lesson from scratch — a new-lesson request that asks for differentiated or leveled materials is k12-lesson-plan-creation's job, one package. Not for grading, rubrics, assessment feedback, or quizzes.
日本語の概要は準備中です。原文の説明を表示しています。
Creates a lesson plan, student-facing materials, and observation template. Load this skill BEFORE asking the teacher any clarifying question about grade, subject, topic, standard, or timing. Use when a K-12 teacher needs a math, ELA, science, or social studies lesson built from scratch — even if grade, subject, or topic isn't yet stated. Do NOT load for grading, a rubric, assessment feedback, a quiz, or a standards lookup — answer those directly. Triggers on explicit requests (lesson plan, mini-lesson, unit plan, daily plan) and implicit teacher intent: "I'm teaching long division to 5th graders," "need to teach photosynthesis tomorrow." Core signal: teacher needs new instructional content created. A new lesson that asks for differentiated, tiered, or leveled materials is still ONE planning request — this skill produces those materials inside the lesson package; do not also invoke k12-lesson-differentiation. Not for differentiating an existing lesson (use k12-lesson-differentiation) or passage rewrites.
日本語の概要は準備中です。原文の説明を表示しています。
Generate X/Twitter release promotion posts with ASCII tables and CodeSnap rendering. Use when writing release posts, promotion tweets, plugin announcements, or preparing social media content for new versions.
日本語の概要は準備中です。原文の説明を表示しています。
Produces human-sounding text that avoids detectable AI writing patterns. Activates on any writing task — tweets, emails, articles, bios, captions, reports, copy, messages, LinkedIn posts, cover letters, README files, or any content where the output must not read as AI-generated. Enforces banned vocabulary, structural variety, punctuation discipline, accuracy rules, and voice calibration. Use when the user says "write," "draft," "rewrite," "make this sound human," "anti-slop," "not AI," or any variation of wanting authentic-sounding output.
日本語の概要は準備中です。原文の説明を表示しています。
智能引号/排版字符规范化工具。将弯引号、长破折号、省略号等排版字符转为标准 ASCII 形式， 适配 Word/PDF/网页文本的后处理清洗。当用户提到智能引号、弯引号转直引号、排版字符规范化、 长破折号等需求时使用此skill。
日本語の概要は準備中です。原文の説明を表示しています。
全角/半角字符规范化工具。读取结构化数据文件，将文本中的全角ASCII字符（字母、数字、标点）转换为半角形式， 并可按需执行指定 Unicode 正规化。适用于中日韩混排科研文本的统一预处理，不负责空白行删除、拼写纠错或重复标点合并。 当用户提到全角转半角、半角规范化、全半角统一、全角字母数字转换等需求时使用此skill。
日本語の概要は準備中です。原文の説明を表示しています。
括号类型统一工具。读取结构化数据文件，执行中文/全角括号到 ASCII 半角括号的映射，输出括号已统一的结构化数据文件。 当用户提到括号统一、全角括号转半角、中文括号转换、括号规范化等需求时使用此skill。 即使用户没有明确说出"bracket_normalizer"，只要任务涉及括号类符号统一，就应该使用此skill。 不负责全角字母数字转换、Unicode正规化或其他通用全半角处理。
日本語の概要は準備中です。原文の説明を表示しています。
Use when the user needs to turn a real software engineering / computer science project and an existing thesis draft into a submission-ready undergraduate thesis manuscript. Trigger for requests such as "根据项目把论文改成定稿", "按学校模板排版成最终版", "复制初稿后生成定稿 Word", "为定稿降查重", "根据PaperPass报告降AIGC", "继续在手改初稿上改", "恢复原来的图表和数据库说明", or when academic-paper-strategist has already produced an evidence-backed rewrite plan. Outputs a cleaned manuscript, final DOCX workflow, and a separate rework report.
日本語の概要は準備中です。原文の説明を表示しています。
Use when the user needs to plan, de-risk, or ground a software engineering / computer science undergraduate thesis from a real codebase before final writing. Trigger for requests such as "根据项目写毕业论文", "先做论文大纲", "用真实项目材料规划论文", "检查论文是否脱离代码", "根据检测报告降查重", "根据AIGC报告改写定稿", "继续在手改初稿上改", or when an existing draft thesis must be reworked against a school format sample. Produces an evidence-backed outline, chapter rewrite plan, figure plan, and handoff package for academic-paper-composer.
日本語の概要は準備中です。原文の説明を表示しています。