1.4万 件 ・ 関連度順
概要と使いどころ
Fact lookup: Apple Human Interface Guidelines corpus (greppable markdown). Query on demand for accessibility and ergonomic minimums (hit-target sizes, Dynamic Type), platform terminology, system component capabilities, and App-Review-relevant conventions. Not design 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。
Start here for any iOS or SwiftUI task. Coordinates best-practice guides, correctness checks, and full Apple API references. Use before navigating to other Apple skills — for building, reviewing, refactoring, or debugging iOS apps.
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。
Swift concurrency patterns — actors, structured concurrency, cancellation, async streams, GCD migration, strict-concurrency diagnostics, common bug patterns. Use when writing, reviewing, or debugging concurrent Swift code.
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Refactor and review SwiftUI view files with strong defaults for small dedicated subviews, MV-over-MVVM data flow, stable view trees, explicit dependency injection, and correct Observation usage. Use when cleaning up a SwiftUI view, splitting long bodies, removing inline actions or side effects, reducing computed `some View` helpers, or standardizing `@Observable` and view model initialization patterns.
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。
SwiftData patterns — autosave, relationships, dangerous predicates, CloudKit constraints, indexing, class inheritance. Use when writing, reviewing, or debugging SwiftData code.
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。
Swift Testing patterns — structs over classes, async confirmations, parameterized tests, exit tests, attachments, common agent mistakes. Use when writing, reviewing, or migrating Swift Testing code.
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Use when drafting a medical research manuscript or any IMRAD section. Runs an 8-phase pipeline from outline to submission-ready draft for original articles, AI validation studies, case reports, meta-analyses, technical notes and more. Checking a draft is /self-review.
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Use when a manuscript comes back with reviewer or editor comments. Numbers every comment, classifies it MAJOR/MINOR/REBUTTAL, drafts a point-by-point response with tracked manuscript changes, routes new analyses to /analyze-stats and writes the cover letter.
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Use when turning a personal agent skill into an open-source, distributable one. Runs a PII audit, generalizes personal details, checks licence compatibility, reviews cross-platform adapters and walks through packaging.
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Use when checking a radiology or medical AI study design before drafting or submission. Reviews the analysis unit, cohort logic, leakage risks, comparator, validation strategy and reporting-guideline fit. AI-vs-expert benchmarks are /design-ai-benchmarking.
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Use when a manuscript or response-to-reviewers letter reads as AI-written. Scans for 27 AI writing patterns and rewrites flagged passages, preserving technical accuracy and bounding how much text changes. Not general copy-editing; that is /polish-language.
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 running a systematic review and meta-analysis, DTA or intervention. Covers PROSPERO protocol, search, screening, extraction, risk of bias (QUADAS-3, RoB 2, ROBINS-I), bivariate/HSROC or random-effects pooling, forest plots, heterogeneity and PRISMA reporting. Topic scouting is /ma-scout.
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Use when clinical data may contain PHI and must be de-identified before any LLM-assisted analysis. A local Python script (no network or AI calls) detects identifiers with regex and heuristics in 12 country locale packs, with interactive terminal review.
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Use when designing a study that benchmarks AI systems against a human-expert panel, before data collection. Plans the arms, decoupled rubrics with anchors, planted calibration probes, reviewer panel, inter-rater reliability targets and LLM-as-judge versus human adjudication.
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Use when looking for a meta-analysis topic before any protocol exists. Starts from a professor's publication profile or from a clinical question, finds gaps, assesses feasibility and returns a ranked topic list. Running the review itself is /meta-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。
Searches academic literature via arXiv, Semantic Scholar, and open-access PDFs. Use when building literature reviews or finding formal research on a topic.
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。
Detects AI-generated writing patterns in prose. Use when reviewing docs for slop, vague language, or identity leaks before publishing.
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。
Implements GitHub or GitLab issues via parallel subagents with review gates between task batches. Use when resolving multi-step issues end-to-end.
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。
Assesses architecture decisions, ADR compliance, and coupling. Use when evaluating design changes or validating structural decisions before merging.
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。
Evaluates test suites for coverage gaps, TDD/BDD compliance, and anti-patterns. Use when auditing test quality or before a major release.
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Detects shared stack membership and iterates a command across all PRs in base-to-tip order. Use when a command supports --stack flag for multi-PR iteration.
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Detects time and space complexity hotspots via AST scan. Use when code feels slow, before performance-sensitive merges, or to find O(n²) regressions.
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Verifies workspace state and staged changes as a read-only preflight. Use before commits or PRs to confirm staged set is clean and correct.
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 code change impact with risk scoring and affected-node mapping. Use before merging to understand what a change touches and what lacks test coverage.
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。