1,528 件 ・ 関連度順
概要と使いどころ
Deep multi-source technical research with citations — tech evaluation, ecosystem/standards comparison. NOT for API docs (mk:docs-finder), codebase discovery (mk:scout), or project Q&A (mk:ask-me).
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 publish-grade SVG+PNG technical diagrams (architecture, data flow, sequence, ER, state-machine, etc.) in 8 styles. Writes to tasks/visuals/. Needs rsvg-convert for PNG; else SVG-only.
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。
Ticket/PRD intake: product-area classification, completeness scoring, RCA, technical assessment. Works with Jira, Linear, GitHub, or manual paste. Use to analyze or triage a ticket.
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 or compare multiple technical approaches for one validated decision. NOT for product discovery or refining an existing 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。
Generate publish-grade SVG+PNG technical diagrams — architecture, data flow, flowchart, sequence, agent-memory, class, ER, state-machine, comparison, timeline, and more — across 8 visual styles. Writes output to tasks/visuals/ or an active plan's visuals/ directory. Requires rsvg-convert (librsvg) for PNG export; degrades gracefully to SVG-only when absent.
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 a decision needs deep multi-source technical research with cited evidence — technology evaluation, ecosystem comparison, standards/spec fact-finding, "how do others solve X". Triggers on "research X", "deep dive on X", "evaluate X vs Y", "find best practices for X". NOT for library/API doc lookup (see mk:docs-finder); NOT for one-shot URL fetch (see mk:web-to-markdown); NOT for internal codebase discovery (see mk:scout); NOT for project-only Q&A (see mk:ask-me); NOT for comparing solution designs (see mk:brainstorming); NOT for root-cause debugging (see mk:investigate); NOT for wiki ingestion of sources (see mk:wiki-research).
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 or compare multiple technical approaches for one validated decision. Do not use for product discovery or an existing 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。
Ticket/PRD intake: product area classification, completeness scoring, RCA, technical assessment. Works with Jira (via mk:jira-issue / jira-as), Linear (via MCP), GitHub (via gh CLI), or manual paste. Triggers: 'analyze ticket', 'intake PRD', 'triage issue', 'classify ticket', 'check ticket'.
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 diagrams and visualizations using Mermaid.js v11 syntax. Use when generating flowcharts, sequence diagrams, class diagrams, state diagrams, ER diagrams, Gantt charts, user journeys, timelines, architecture diagrams, or any of 24+ diagram types. Supports JavaScript API integration, CLI rendering to SVG/PNG/PDF, theming, configuration, and accessibility features. Essential for documentation, technical diagrams, project planning, system architecture, and visual communication.
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。
Develop and extend the RulCode technical interview preparation platform built with React, Vite, TypeScript, and Supabase. Use when adding new algorithm problems, building visualization components, implementing DSA topic pages, updating the problem editor, or modifying the interview preparation UI.
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。
Vehicle data through Openapi Automotive - look up cars, motorcycles, insurance and MOT status by license plate across Italy, UK, Germany, France, Spain, Portugal. Use for vehicle technical data and insurance validity checks by plate.
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 rapport-based elicitation to resistant or semi-cooperative humans and agents. Use when coercive prompting or brittle handoffs damage cooperation. NOT for routine cooperative tasks, pure technical debugging, or already reliable exchanges.
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。
Draft professional emails for various contexts including business, technical, and customer communication. Use when the user needs help writing emails or composing professional messages.
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。
Long-running design anthropologist that builds comprehensive visual databases from 500-1000 real-world examples, extracting color palettes, typography patterns, layout systems, and interaction design across any domain (portfolios, e-commerce, SaaS, adult content, technical showcases). This skill should be used when users need exhaustive design research, pattern recognition across large example sets, or systematic visual analysis for competitive positioning.
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。
Identify and counteract cognitive biases in medical decision-making through systematic error analysis and contextual algorithm application. For diagnostic reasoning, treatment decisions, and clinical judgment improvement. NOT for basic medical knowledge, technical procedures, or non-clinical healthcare domains.
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。
Designs genomics experiments so technical nuisance variation (batch, lane, plate, flow cell, operator, reagent lot, processing day) is balanced against the biological variable of interest and therefore estimable rather than confounded, using constrained sample-to-batch assignment (designit, OSAT), the confounder/mediator/collider distinction, and the principle that no post-hoc correction recovers a fully confounded design. Covers detecting hidden batches with surrogate variable analysis, a decision table for downstream correction (ComBat-seq, RUVSeq, SVA) whose execution is deferred to differential-expression/batch-correction, and reproducibility metadata. Use when assigning samples to sequencing batches/lanes/plates, avoiding batch-condition confounding, deciding whether a design is salvageable by correction, choosing a correction method, or estimating the number of hidden batches. For the experimental unit, randomization, and blocking concepts see experimental-design/randomization-blocking.
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。
Identify differentially bound regions across CLIP-seq conditions (knockdown vs control, treatment vs vehicle, disease vs healthy) using DEWSeq (sliding-window DESeq2), Flipper (Skipper-downstream), ASpeak, edgeR, or limma-voom. Use when computing condition-level changes in RBP binding intensity, choosing peak-level vs window-level vs crosslink-level testing, designing replicate experiments, or distinguishing biological binding shifts from technical confounders.
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。
Classify constitutional (germline) copy number variants for clinical reporting using the 2019 ACMG/ClinGen technical standards points-based framework, with ClassifyCNV and AnnotSV for semi-automated scoring. Covers the separate copy-number-loss and copy-number-gain rubrics, the five-tier classification, ClinGen haploinsufficiency/triplosensitivity and dosage-sensitive regions, de novo and segregation evidence, and population-frequency benign evidence. Use when assigning pathogenic/likely-pathogenic/VUS/likely-benign/benign to a constitutional CNV, scoring a CNV against ACMG/ClinGen criteria, or distinguishing the automatable evidence from the case-specific evidence requiring manual input.
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。
Provides a checklist of five evidence-based measures to enhance the technical success of adrenal venous sampling procedures. Triggers include when preparing for AVS and asking 'How can I maximize success rate?' or troubleshooting failed attempts.
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。
Determines successful catheterization in adrenal venous sampling by applying selectivity index cut‑offs with and without ACTH stimulation. Triggered during AVS when measuring cortisol levels and asking “Is the catheter properly positioned?” or when reviewing results for technical adequacy.
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。
Draft or revise English prose for any brief — documentation body, intake problem statements, spec context, RCA summaries, marketing copy, README sections, PR descriptions. Mandatorily invokes `Skill(humanizer)` as the final pass on every draft. Conditionally invokes `copywriting` for persuasive register, `documentation` for reference docs, `technical-tutorials` for tutorials. Used when any phase needs human-readable prose written or rewritten. Composition only — research and register-picking happen in the caller's context.
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 wants to design, redesign, shape, critique, audit, polish, clarify, distill, harden, optimize, adapt, animate, colorize, extract, or otherwise improve a frontend interface. Covers websites, landing pages, dashboards, product UI, app shells, components, forms, settings, onboarding, and empty states. Handles UX review, visual hierarchy, information architecture, cognitive load, accessibility, performance, responsive behavior, theming, anti-patterns, typography, fonts, spacing, layout, alignment, color, motion, micro-interactions, UX copy, error states, edge cases, i18n, and reusable design systems or tokens. Also use for bland designs that need to become bolder or more delightful, loud designs that should become quieter, live browser iteration on UI elements, or ambitious visual effects that should feel technically extraordinary. Not for backend-only or non-UI tasks.
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。
Draft a Workflow Phase 4 technical spec from an intake (and optionally a BRD + scout + research memo). The spec defines how the system will change: design (C4 + UML + dependency graph in PlantUML), data, APIs, tests, rollout, rollback. Output lives at `docs/specs/<slug>.md`. Never self-approves — approval happens via `/approve-direction`.
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。
Comprehensive patterns for creating, maintaining, and managing Architecture Decision Records (ADRs) that capture the context and rationale behind significant technical decisions.
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。