1.4万 件 ・ 関連度順
概要と使いどころ
Diff a live page's accessibility violations against a baseline — by default compares uncommitted changes (stash-based), or pass --branch [<name>] to diff against a branch. Reports only new violations introduced, violations fixed, and pre-existing count. Use `scan` for a full audit with no diffing.
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。
AIとの壁打ちで整理したプラン、または指定ファイルの内容を GitHub Issue として作成する。セッション内のプランの Issue 化、既存プランファイルからの Issue 作成、config によるデフォルト値設定が必要なときに使う。
5〜10 分のプレゼン資料を Marp（Markdown）で作成し、PDF / PPTX / HTML に書き出す。 会話やリポジトリの実状況からスライドを起こし、clean テーマの雛形で下書き → docs/slides/YYYY-MM-DD_slug.md に保存 → 文字量の lint → PNG で目視確認 → 描画する。 「プレゼン資料を作りたい」「スライド」「発表資料」「Marp」「Google スライド用の pptx」 などに言及されたときに使用する。
AIとの壁打ちで整理したプラン、または別ファイルの設計メモや実装メモを、読みやすいMarkdown形式の実装プランに整形して `docs/plan/YYYY-MM-DD_<plan-name>.md` に保存する作業で使う。会話中に提案したプランの書き出し、テキストメモの構造化、見出し整理、表への変換、チェックリスト化、保存が必要なときに使う。
Forces the laziest solution that actually works, simplest, shortest, most minimal. Channels a senior dev who has been paged at 3am for a factory class that wrapped one function: question whether the task needs to exist at all, reach for the standard library before custom code, native platform features before dependencies, one line before fifty. Supports intensity levels: lite, full, ultra. The default is full. Use on ANY coding task: writing, adding, refactoring, fixing, reviewing, or designing code, and choosing libraries or dependencies. Also use whenever the user says "ponytail", "be lazy", "lazy mode", "simplest solution", "minimal solution", "yagni", "do less", or "shortest path", or complains about over-engineering, bloat, boilerplate, or unnecessary dependencies. Do NOT use for non-coding requests such as general knowledge, prose, translation, summaries, recipes.
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。
Targeted deep research to fill a specific gap in an existing thesis. Use when user says "deepen", "flesh out", "expand on", "fill in", or specifies a thesis section to improve. Also supports --sync-metrics mode — detects stale financial metrics across every in-scope analytical section of a thesis (not just Key Metrics or Summary) and coherently updates every sentence that depends on them, using a Key-Metrics-table / FMP / web-search waterfall. Use when user says "sync metrics", "update the numbers throughout the note", or "fix stale figures across the whole thesis". The batch form `--sync-metrics --all-flagged` syncs every thesis flagged by the latest `/numbers --all-open` in one operation — detection fanned out to capped READ-ONLY agents, one consolidated confirmation gate, direct sequential writes (agents never write).
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。
Systematically evaluate weak, stale, or low-conviction theses for upgrade, monitoring, or closure. Use when user says "prune", "kill list", "clean up theses", "what should I close", or "reduce portfolio".
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。
Targeted deep research to fill a specific gap in an existing thesis. Use when user says "deepen", "flesh out", "expand on", "fill in", or specifies a thesis section to improve. Also supports --sync-metrics mode — detects stale financial metrics across every in-scope analytical section of a thesis (not just Key Metrics or Summary) and coherently updates every sentence that depends on them, using a Key-Metrics-table / FMP / web-search waterfall. Use when user says "sync metrics", "update the numbers throughout the note", or "fix stale figures across the whole thesis". The batch form `--sync-metrics --all-flagged` syncs every thesis flagged by the latest `$numbers --all-open` in one operation — detection fanned out to capped READ-ONLY agents, one consolidated confirmation gate, direct sequential writes (agents never write).
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。
会話で直近に調べた・生成した内容 (調査結果、比較、設計案、手順など) を Markdown や HTML ファイルに書き出すスキル。ユーザーが「md で出力して」「html にまとめて」「ファイルに書き出して」など会話内容のファイル化の意図を示したら必ず使用する。
Serves LLMs with high throughput using vLLM's PagedAttention and continuous batching. Use when deploying production LLM APIs, optimizing inference latency/throughput, or serving models with limited GPU memory. Supports OpenAI-compatible endpoints, quantization (GPTQ/AWQ/FP8), and tensor parallelism.
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。
OpenViking long-term memory plugin guide. Once installed, the plugin automatically remembers important facts from conversations and recalls relevant context before responding.
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 LLM inference on CPU, Apple Silicon, and consumer GPUs without NVIDIA hardware. Use for edge deployment, M1/M2/M3 Macs, AMD/Intel GPUs, or when CUDA is unavailable. Supports GGUF quantization (1.5-8 bit) for reduced memory and 4-10× speedup vs PyTorch on CPU.
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。
OpenAI's general-purpose speech recognition model. Supports 99 languages, transcription, translation to English, and language identification. Six model sizes from tiny (39M params) to large (1550M params). Use for speech-to-text, podcast transcription, or multilingual audio processing. Best for robust, multilingual ASR.
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。
Guarantee valid JSON/XML/code structure during generation, use Pydantic models for type-safe outputs, support local models (Transformers, vLLM), and maximize inference speed with Outlines - dottxt.ai's structured generation library
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。
Expert guidance for fine-tuning LLMs with Axolotl - YAML configs, 100+ models, LoRA/QLoRA, DPO/KTO/ORPO/GRPO, multimodal support
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 this skill when the user requests to generate, create, write, or draft a newsletter, email digest, weekly roundup, industry briefing, or curated content summary. Supports topic-based research, content curation from multiple sources, and professional formatting for email or web distribution. Trigger on requests like "create a newsletter about X", "write a weekly digest", "generate a tech roundup", or "curate news about Y".
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 this skill when the user requests to generate, create, imagine, or visualize images including characters, scenes, products, or any visual content. Supports structured prompts and reference images for guided generation.
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 this skill when the user requests to generate, create, or imagine videos. Supports structured prompts and reference image for guided generation.
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 this skill when the user requests to generate, create, or improve documentation for code, APIs, libraries, repositories, or software projects. Supports README generation, API reference documentation, inline code comments, architecture documentation, changelog generation, and developer guides. Trigger on requests like "document this code", "create a README", "generate API docs", "write developer guide", or when analyzing codebases for documentation purposes.
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。
Conduct multi-round deep research on any GitHub Repo. Use when users request comprehensive analysis, timeline reconstruction, competitive analysis, or in-depth investigation of GitHub. Produces structured markdown reports with executive summaries, chronological timelines, metrics analysis, and Mermaid diagrams. Triggers on Github repository URL or open source projects.
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 this skill when the user requests to review, analyze, critique, or summarize academic papers, research articles, preprints, or scientific publications. Supports comprehensive structured reviews covering methodology assessment, contribution evaluation, literature positioning, and constructive feedback generation. Trigger on queries involving paper URLs, uploaded PDFs, arXiv links, or requests like "review this paper", "analyze this research", "summarize this study", or "write a peer 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。
Set up and run HeartMuLa, the open-source music generation model family (Suno-like). Generates full songs from lyrics + tags with multilingual support.
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。
CLI to manage emails via IMAP/SMTP. Use himalaya to list, read, write, reply, forward, search, and organize emails from the terminal. Supports multiple accounts and message composition with MML (MIME Meta 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。
Search personal knowledge bases, notes, docs, and meeting transcripts locally using qmd — a hybrid retrieval engine with BM25, vector search, and LLM reranking. Supports CLI and MCP integration.
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。