1,811 件 ・ 関連度順
概要と使いどころ
Facilitate sprint planning by organizing backlog items, estimating effort, balancing capacity, identifying dependencies, and producing a sprint commitment summary. Use when teams need structured sprint planning output. Do not use for strategic roadmap planning, annual budgeting, or product vision work.
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Identify and exploit misconfigured Spring Boot Actuator endpoints. This skill covers how to extract sensitive configuration details, heap dumps, environment variables, and ultimately escalating to Remote Code Execution (RCE) via `spring-cloud-starter` vulnerabilities.
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。
Spring Security 7 implementation for Spring Boot 4. Use when configuring authentication, authorization, OAuth2/JWT resource servers, method security, or CORS/CSRF. Covers the mandatory Lambda DSL migration, SecurityFilterChain patterns, @PreAuthorize, and password encoding. For testing secured endpoints, see spring-boot-testing 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。
Spring Boot 4 data layer implementation for Domain-Driven Design. Use when implementing JPA or JDBC aggregates, Spring Data repositories, transactional services, projections, or entity auditing. Covers aggregate roots with AbstractAggregateRoot, value object mapping, EntityGraph for N+1 prevention, and Spring Boot 4 specifics (JSpecify null-safety, AOT repositories). For DDD concepts and design decisions, see the domain-driven-design 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。
Create, read, edit, fix, or clean spreadsheet files (.xlsx, .xlsm, .csv, .tsv). Use whenever a build task's artifact kind is spreadsheet, or the task names a spreadsheet file and wants something done to it or produced from it, including restructuring messy tabular data into a proper workbook. Covers openpyxl generation, formulas and recalculation, editing existing workbooks, and the validation gates. Not for tasks whose deliverable is a document, report, or web page that merely contains a table.
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。
Turn one approved master still into a character's whole animated sprite set - one image-to-video clip per action (idle, walk, run, attack, jump, hurt, cast and so on), all from that still, generated through generate2dmedia route_media.py (the xAI API when a key is configured, else the local Grok CLI) - then gate each take, key it with a soft matte, register it to the master, pick loops or retime actions, finish (HD by default, pixel on request) and package fixed-canvas frames, alpha WebM, packed MP4 for iPhone and a PNG fallback with engine metadata and a JS runtime. Also processes a supplied clip. Use for character and creature animation, fluid motion and any video-to-sprite work. Not for FX, icon or prop sheets (generate2dsprite), maps or plate loops (generate2dmap), code-drawn animation (codeart2d, only on request) or calling a video API by itself (generate2dmedia).
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。
Analyzes ticket complexity and maps dependencies against an existing codebase before sprint planning. Triggers on 'how complex is this ticket', 'what should we work on first', 'can we fit this in the sprint', 'tech review before planning', 'plan the sprint'. NOT for writing implementation plans (see mk:plan-creator); NOT for scope/ambition review (see mk:plan-ceo-review).
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。
JIRA agile via the jira-as wrapper: epics, sprints, backlog, ranking, story points, subtasks, velocity. Triggers: 'create sprint', 'add KEY to sprint', 'set story points on KEY', 'rank KEY before KEY', 'velocity for board', 'epic add'. Board ID ≠ project key. NOT for issue CRUD (mk:jira-issue); NOT for time tracking (mk:jira-time).
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。
Analyzes pooled CRISPR screens with MAGeCK (Li et al 2014), covering count generation (mageck count), the RRA two-condition workflow (mageck test using alpha-RRA over per-sgRNA negative-binomial p-values), the MLE multi-condition workflow (mageck mle with explicit design matrix and beta-score output), normalization choice (median vs total vs control-sgRNA vs spike-in), sgRNA efficiency injection, paired-sample testing, time-course design, drug-screen versus dropout-screen design matrices, MAGeCKFlute and MAGeCK-VISPR downstream visualization, and decision logic for when to use MAGeCK vs JACKS / BAGEL2 / drugZ / Chronos. Use when running a fresh CRISPR screen analysis, picking RRA vs MLE for the experimental design, choosing a normalization method from QC signatures, debugging MLE convergence failure or NaN beta scores, comparing MAGeCK output across tools, or building a batch-aware multi-cell-line / multi-condition MLE design matrix.
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。
Analyzes single-cell pooled CRISPR screens (Perturb-seq, CROP-seq, Perturb-CITE-seq, ECCITE-seq, multiome) where each cell carries an sgRNA and a scRNA-seq / surface-protein / chromatin readout. Covers experimental design (direct-capture Perturb-seq Dixit 2016 vs CROP-seq 3'UTR-barcoded Datlinger 2017 vs ECCITE-seq vs Multiome), MOI for sgRNA assignment, escaper-cell filtering (Mixscape, Papalexi 2021), SCEPTRE NB GLM + permutation for low-MOI (Barry 2024 Genome Biol 25:124), the Pertpy framework, factor decomposition, genome-scale Perturb-seq (Replogle 2022 Cell, 2.5M cells), and per-perturbation single-cell DE. Use when running a single-cell CRISPR screen, choosing direct-capture vs CROP-seq architecture, filtering escaper cells, performing single-cell DE, integrating Perturb-seq with pathway analysis, scaling to GW CRISPRi via Replogle protocol, or analyzing multi-omics screens.
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。
Propose the next dependency-ready sprint from the ALREADY-DECOMPOSED roadmap — standup's active sibling. Reads docs/roadmap-execution-plan.md + the memory backlog, computes per-task readiness from the roadmap's machine-readable status, orders with roadmap-planner's graph engine, and emits a proposed task-set (sprint-plan manifest shape) with per-feature done-criteria, excluding unready tasks and naming their unmet prerequisite. PROPOSES ONLY — the human confirms/edits before /triage routes it (typically to the `power` track). Distinct from `sprint-plan`, which decomposes a fresh vision.
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。
Summarize sprint status and surface risks. Use when the user says "check sprint status" or "show sprint status"
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 sprint status tracking from epics. Use when the user says "run sprint planning" or "generate sprint plan"
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。
Periodically inspect a sprint manager and worker sessions against a sprint spec, classify progress, nudge only for real stalls, and always append a checkpoint log beside the sprint file.
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。
Prepare a defense-contractor environment for CMMC Level 2 certification: scope CUI and FCI, implement the 110 NIST SP 800-171 Rev 2 security requirements across 14 families, compute the SPRS score with the DoD Assessment Methodology, manage a compliant POA&M, and ready the organization for a C3PAO assessment. Use when an organization handles Controlled Unclassified Information (CUI) under a DoD contract, when a contract carries DFARS clause 252.204-7012/7019/7020/7021, when preparing for or responding to a CMMC assessment, when computing or improving an SPRS score, when building a System Security Plan or POA&M for 800-171, or when scoping which systems are in the CUI boundary. Keywords: CMMC, CMMC Level 2, NIST 800-171, SP 800-171 Rev 2, CUI, FCI, SPRS, DFARS 7012, C3PAO, POA&M, System Security Plan, DoD Assessment Methodology, 110 controls, defense industrial base, DIB, FedRAMP equivalency.
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。
Erzeugt aus einem Bestand offener und gefallener Entscheidungen einen strukturierten ASCII-Abhängigkeitsgraphen (maximal 100 Spalten) für das Terminal. Bewertet Knoten nach fünf festen Kriterien: sinnvoll, empfohlen, usertypisch (Richtung und decision-avatar-Konfidenz getrennt), zusammenpassend und widersprechend; Kanten tragen Beziehungstyp und Belegstatus. Deckt Blocker, Freischaltungen, Reihenfolgen und widersprüchliche Empfehlungen gnadenlos auf. Nutze diesen Skill bei "welche Entscheidungen hängen voneinander ab", "Abhängigkeitsgraph der Entscheidungen", "Entscheidungspfad visualisieren", "Widersprüche zwischen Entscheidungen prüfen", "Reihenfolge der Beschlüsse", /decision-path. NICHT nutzen, um Entscheidungen erst zu inventarisieren -- das macht decision-briefing; NICHT nutzen, um das empfohlene Gesamtkonzept zur Abnahme zu bündeln -- das macht decision-draw; NICHT für kompakte Fünfteiler- Einzelblöcke mit Optionen und Pro/Contra -- das macht decision-shot; NICHT für die Einzelfall-Erwägung mit Frameworks -- das macht decide.
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。
スプリント計画を実行する。GitHub Issues からバックログ整理→タスク分解→マイルストーン設定→sprint-state.md生成を自動化する
Choose animation values that feel native on Apple platforms instead of guessing springs. Use when: picking a spring/duration/easing for any transition; an animation "feels off", sluggish, floaty, wobbly, or cheap; tuning a sheet, drawer, panel, selection lens, button press, or drag release; wiring gesture-driven motion; or someone asks "what spring should I use", "why does this feel wrong", "make it feel native/premium", "match Apple's feel". Also fires on hand-picked magic numbers in .spring(), on animations that jump when interrupted, and on motion that only starts after a gesture ends.
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。
End-to-end pooled and single-cell CRISPR screen analysis from FASTQ to hit genes. Orchestrates library design QC, guide counting, six-stage screen QC (plasmid Gini, replicate Pearson, CEGv2 PR-AUC, copy-number artifact), method-appropriate hit calling across MAGeCK RRA/MLE, BAGEL2, drugZ, JACKS, and Chronos, cancer-cell-line copy-number correction (CRISPRcleanR / Chronos), batch correction for multi-batch screens, and the specialized branches for combinatorial paralog screens, single-cell Perturb-seq, base-editor variant-function screens, prime-editor screens, and in vivo bottleneck-aware screens. Use when analyzing any pooled CRISPR screen end-to-end, matching the hit-calling method to the experimental design, integrating copy-number correction into the pipeline, or branching the workflow for single-cell, combinatorial, base-editor, prime-editor, or in vivo variants.
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 you need to design, review, or improve REST APIs with Spring Boot — including HTTP methods, resource URIs, status codes, DTOs, versioning, deprecation and sunset headers, content negotiation (JSON and vendor media types), ISO-8601 instants in DTOs, pagination/sorting/filtering, Bean Validation at the boundary, idempotency, ETag concurrency, HTTP caching, error handling, security, contract-first OpenAPI (OpenAPI Generator), controller advice, and problem details for errors. This should trigger for requests such as Review Java code for Spring Boot REST API; Apply best practices for Spring Boot REST API in Java code. Part of cursor-rules-java project
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。
Motion アニメーションライブラリ (旧 Framer Motion) リファレンス。 React コンポーネント motion, AnimatePresence, AnimateView, LayoutGroup, LazyMotion, MotionConfig, Reorder。hooks useAnimate, useScroll, useSpring, useTransform, useMotionValue, useInView。 vanilla JS API animate, scroll, inView, hover, press, stagger, spring, timeline, motionValue。Vue 版 (motion-v) v-motion ディレクティブ, useScroll 等の composable, Motion+ 限定コンポーネント (Carousel, Cursor, Ticker, Typewriter, AnimateNumber)。transitions / layout / drag / gestures / SVG / scroll アニメーション、accessibility, performance, bundle 最適化, troubleshooting。
Apple グラフィックスフレームワークリファレンス。 Metal, Core Animation, Core Graphics, Core Image, SpriteKit, SceneKit。 MTLDevice, MTKView, MTLBuffer, MTLTexture, Metal 4 (MTL4CommandQueue, MTL4CommandBuffer, MTL4ArgumentTable), CALayer, CABasicAnimation, CADisplayLink, CAMetalDisplayLink, CAMetalDrawable, CGContext, CGPath, CGImage, CIFilter, CIContext, CIImage, CIRAWFilter, CIWarpKernel, CIBlendKernel, SKScene, SKSpriteNode, SKAction, SCNNode, SCNGeometry, SCNMaterial。
Analyzes single-cell pooled CRISPR screens (Perturb-seq, CROP-seq, Perturb-CITE-seq, ECCITE-seq, multiome) where each cell carries an sgRNA and a scRNA-seq / surface-protein / chromatin readout. Covers experimental design (direct-capture Perturb-seq Dixit 2016 vs CROP-seq 3'UTR-barcoded Datlinger 2017 vs ECCITE-seq vs Multiome), MOI for sgRNA assignment, escaper-cell filtering (Mixscape, Papalexi 2021), SCEPTRE NB GLM + permutation for low-MOI (Barry 2024 Genome Biol 25:124), the Pertpy framework, factor decomposition, genome-scale Perturb-seq (Replogle 2022 Cell, 2.5M cells), and per-perturbation single-cell DE. Use when running a single-cell CRISPR screen, choosing direct-capture vs CROP-seq architecture, filtering escaper cells, performing single-cell DE, integrating Perturb-seq with pathway analysis, scaling to GW CRISPRi via Replogle protocol, or analyzing multi-omics screens.
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。
Analyzes pooled CRISPR screens with MAGeCK (Li et al 2014), covering count generation (mageck count), the RRA two-condition workflow (mageck test using alpha-RRA over per-sgRNA negative-binomial p-values), the MLE multi-condition workflow (mageck mle with explicit design matrix and beta-score output), normalization choice (median vs total vs control-sgRNA vs spike-in), sgRNA efficiency injection, paired-sample testing, time-course design, drug-screen versus dropout-screen design matrices, MAGeCKFlute and MAGeCK-VISPR downstream visualization, and decision logic for when to use MAGeCK vs JACKS / BAGEL2 / drugZ / Chronos. Use when running a fresh CRISPR screen analysis, picking RRA vs MLE for the experimental design, choosing a normalization method from QC signatures, debugging MLE convergence failure or NaN beta scores, comparing MAGeCK output across tools, or building a batch-aware multi-cell-line / multi-condition MLE design matrix.
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。