1万 件 ・ 関連度順
概要と使いどころ
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。
Build premium, realistic soundtracks for code-made videos and animations from real licensed recordings - launch films, product and feature videos, UI walkthroughs, logo stings, explainers, trailers and animated shorts made with Remotion, HyperFrames, three.js, GSAP or any renderer. Use this whenever a video or animation needs sound design, SFX, foley, ambience, music, a soundtrack, a score, UI click / whoosh / riser / impact / logo-sting sounds, or a music edit to picture; whenever a synthesized or "random" sounding soundtrack must be replaced with real recordings; or when the user asks to source, license, credit, mix, master, critique or mux audio for a film. It covers cue lists from the picture, sourcing from Freesound / Mixkit / composer libraries with licence checks, a music edit at bar lines, a JSON sound design rendered by a deterministic mixer, measured QA (clicks, masking, harshness, sync, loudness) plus earshot listening, a multi-agent critique, mastering, credits and the mux.
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。
Listen with earshot whenever you must judge, choose, trim, verify or compare sound you cannot hear - picking a sound effect, music or ambience from candidates, finding the cleanest moment in a recording, checking that audio is real and not code-synthesized, reviewing a soundtrack or a video's audio (what happens when, sync to picture, loudness, mix), A/B-ing a mix change, or making a sound in code and improving it by listening (earshot forge). Use it before telling anyone how audio sounds.
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。
Runs parallel prose and craft review agents against a voice profile. Use when checking generated content for AI patterns and voice drift 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。
Updates documentation after code changes with quality gates, slop detection, and accuracy checks. Use when code changes require corresponding doc updates.
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。
Standardizes release approvals with GitHub-aware checklists and deployment gates. Use before releasing to production to verify all gates pass.
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。
Applies anti-sycophancy checklist to override agreement bias. Use when analyzing contested claims or avoiding socially convenient but inaccurate conclusions.
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。
Executes implementation plans with progress tracking, checkpoint validation, and quality gates. Use after planning is complete and tasks are ready to implement.
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。
Enforces token quota management at session start with conservation and compression checks. Use at the start of every session or before large context loads.
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。
Orchestrates the QUALITY pipeline stage for egregore work items, running code review, unbloat, and test updates. Use when running quality checks before a PR.
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。
Review plugin quality with tiered checks and dependency scoping. Use for PR and pre-release audits.
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。
Triage night-market failures by symptom (hooks, CI, tests). Use when a check fails unexpectedly. Do not use for routine gates; use night-market-operations.
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 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.
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 skill fails for a missing tool or the environment needs checking. Diagnoses Python, R, Node, Claude Code, Git, Zotero and configured MCP servers and prints a pass/fail table with links to the setup docs. Read-only; installs nothing.
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 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.
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 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.
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 medical AI paper should be found and cited by AI search engines and RAG tools. Optimizes the title, abstract, summary box (Key Points, Research in Context), keywords, preprint, GitHub README/CITATION.cff and Hugging Face card, returning a visible pass/fail checklist.
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 sending local changes back to the project (an added journal profile, a fixed checklist item, an adapted skill) or reporting a false positive or bug. Finds what changed, scans it for patient data, shows every line and sends nothing until you confirm.
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 references must be written, rendered or converted. Checks [@key] citation keys, renders the reference list with a journal CSL via pandoc, converts [N] markers, injects Zotero Word field codes and runs manuscript-DOCX cross-reference QC. Read-only auditing is /verify-refs.
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 auditing a manuscript item by item against a reporting guideline or risk-of-bias tool. Covers 49 reporting guidelines and risk-of-bias tools (STROBE, CONSORT, STARD, TRIPOD+AI, PRISMA, QUADAS and more), marking each item PRESENT, PARTIAL or MISSING. Not a reviewer critique (/self-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。
Use when choosing the model for a medical-imaging study. Picks a paper-grounded architecture family (CNN/ViT, U-Net/nnU-Net, detection, SAM/foundation, GNN), then vets the concrete repo or checkpoint for licence, version pin, weight provenance and benchmark overlap.
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 paper needs publication-ready figures or a visual abstract. Makes ROC, forest, calibration, Kaplan-Meier and Bland-Altman plots, CONSORT/STARD/PRISMA flow diagrams, confusion matrices, pipeline diagrams and journal visual abstracts, checking the journal's AI-image policy first.
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 Gaussian-SLAM for CUDA RGB-D dense SLAM, Gaussian-splatting mapping, dataset/configuration preflight, checkpoint evaluation, reconstruction metrics, and global-map refinement.
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。
Routes face-alignment installation, import checks, landmark-detection workflows, and detector-backend selection.
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。