427 件 ・ 関連度順
概要と使いどころ
Orchestrate AI/ML pipelines for data ingestion, model training, batch inference, and RAG indexing using Prefect, Airflow, or Dagster. Build reliable, observable, and retriable workflows for production AI systems.
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。
Audit hook delivery health from Agent Monitor data — balance PreToolUse vs PostToolUse (a gap means tools that started but never reported back), detect missing Stop/SubagentStop terminators (sessions/subagents that never closed), and check for stale ingestion (no recent events). Use when hooks look unreliable or events seem to be dropping.
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 Workflow-tool fleet runs from the Agent Monitor — these fleets emit no hooks and are ingested from on-disk run journals. List recent runs with status and agents-per-run, then drill into a single run's per-agent detail. Reconciles against the live run-state endpoints. Use when reviewing Workflow() fleets rather than hook-instrumented interactive sessions.
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。
Diagnose Claude Code hook installation, delivery, and ingestion issues. Checks hook configuration, connectivity, event flow, and identifies common problems. Use when events are not appearing in the dashboard.
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。
Answer a question from a second-brain vault by reading its own pages, citing the pages used, and saying plainly when the vault does not contain the answer. Use this skill whenever the user asks what they know about a topic, what they have read on something, what connects two ideas, what they have changed their mind about, or asks any question that should be answered from their own notes rather than general knowledge. Do NOT use for ingesting new material, for general questions the vault has nothing to do with, or when the user explicitly wants an answer from the open web.
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。
Audit a second-brain vault for structural problems and repair them: broken wikilinks, orphan pages, empty stubs, frontmatter that violates the schema, near-duplicate pages, and stale index entries. Use this skill whenever the user asks to lint, clean, audit or check the health of their vault, says pages feel messy or links are broken, after a large bulk import, or when a scheduled maintenance run fires. Do NOT use for ingesting new sources, for answering questions from the vault, or for rewriting page content that is structurally fine but reads badly.
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。
Produce a periodic review of a second-brain vault: what was added, which topics are growing, what contradicts what, which questions are still open, and what is worth reading next. Use this skill whenever the user asks for a weekly or monthly review, asks what changed in their vault, what they have been learning about lately, or what they should look at next, and when a scheduled review task fires. Do NOT use for structural linting, for ingesting sources, or for answering a specific factual question.
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。
Fetch requested source material and prepare sourced engagement updates. Use to catch up from external notes or messages; applying updates requires a bound record and confirmation.
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。
When you want to extract content from a video — YouTube, Loom, Vimeo, Riverside, Zoom recording, local MP4, X/IG video, anything yt-dlp supports. Three depth modes user picks per invocation — transcript (just words, fast/free), visual (transcript + ffmpeg frame extraction + Claude vision pass on key moments), multimodal (Gemini native video ingestion if $GEMINI_API_KEY set, else dense Claude vision). Uses local Whisper for transcription (MLX-Whisper on Apple Silicon, faster-whisper elsewhere), falls back to platform-provided transcripts when available (Loom, Riverside, YouTube auto-subs). Saves to ~/Documents/videos/<source>-<slug>-<date>/ and optionally captures summary to second-brain raw/ as call-/meeting-/note-. Triggers on "/watch-video <url>," "watch this video," "transcribe this loom," "analyze this video," "summarize this recording," "key moments from this," "what happened in this video." This skill replaces and broadens the prior youtube-transcript 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。
Best-practices reference for preparing and instrumenting applications on Temps. Covers the app runtime contract (`.temps.yaml` health, HOST/PORT, readiness, SIGTERM, replicas, migrations) and production observability (errors, traces, metrics, logs, analytics, privacy, sampling, cardinality, credential boundaries). Use whenever building or reviewing an app for Temps, configuring health checks, adding telemetry, diagnosing missing/noisy signals, or checking OTLP/Sentry/analytics ingestion. Triggers include "temps best practices", "prepare this app for Temps", ".temps.yaml health", "temps health check", "ignore health checks in otel", "temps observability", "wire up telemetry", and "instrument this app for temps". Prefer focused setup skills for a single SDK. Use temps-cli for executing deploy and resource-management commands.
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。
RAG-specific best practices for LlamaIndex, ChromaDB, and Celery workers. Covers ingestion, retrieval, embeddings, and performance.
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 knowledge for Microsoft Planetary Computer Pro development including troubleshooting, decision making, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when using GeoCatalog/STAC APIs, configuring collections/tiles, securing access, integrating QGIS/ArcGIS, or troubleshooting ingestion, and other Microsoft Planetary Computer Pro related development tasks. Not for Azure Maps (use azure-maps), Azure Open Datasets (use azure-open-datasets), Azure Data Explorer (use azure-data-explorer).
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 knowledge for Azure HPC Cache development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when configuring HPC Cache namespaces, NFS/Blob targets, client access, data ingest scripts, or cache failover, and other Azure HPC Cache related development tasks. Not for Azure Managed Lustre (use azure-managed-lustre), Azure NetApp Files (use azure-netapp-files), Azure Batch (use azure-batch), Azure Virtual Machines (use azure-virtual-machines).
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 knowledge for Azure Data Explorer development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when securing ADX (RBAC/MI), automating cluster/schema deploys, tuning ingestion/queries, or integrating via ODBC/JDBC/Power BI, and other Azure Data Explorer related development tasks. Not for Azure Synapse Analytics (use azure-synapse-analytics), Azure HDInsight (use azure-hdinsight), Azure Databricks (use azure-databricks), Azure Stream Analytics (use azure-stream-analytics).
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 knowledge for Azure Data Manager for Agriculture development including limits & quotas, security, configuration, and integrations & coding patterns. Use when setting up BYOL creds/Private Link, ag data ingestion/IoT, AI/nutrient APIs, throttling, or Event Grid logs, and other Azure Data Manager for Agriculture related development tasks. Not for Azure Data Explorer (use azure-data-explorer), Azure Data Factory (use azure-data-factory), Azure Synapse Analytics (use azure-synapse-analytics), Azure Databricks (use azure-databricks).
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。
Get a PDF into the model without blowing the context window or losing structure. Native PDF beats OCR-then-text for most cases; extract-then-summarize beats native for very long docs.
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。
公众号素材｜业务资料库｜预设包｜.aws 预设包｜主题包｜品牌包｜aiworkskills.cn — 用户业务资料库与预设包管理：业务资料按产品名组织在 `.aws-article/products/{产品名}/`（介绍 .md 直挂产品根 + 配图归 `images/` 子目录含同名说明 .md），AI 与用户对话产出业务介绍内容时引导用户保存；图片入库走 `product_image_ingest.py --product <产品名> --stem <中文名>`。导入 .aws ZIP 预设包（本地文件或 `https://aiworkskills.cn/**/*.aws` URL）合并主题/配色/字体配置到 `.aws-article/presets/`；`config.yaml` 仅本地不存在时从包内复制，已存在则 stdout 输出差异 JSON 不覆盖。面向内容运营、品牌团队、设计支持岗。触发词：「素材库入库」「stock images」「上传图到素材库」「.aws」「预设包」「导入预设」「主题包」「aiworkskills.cn 链接」「.aws 下载地址」。
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 sends or generates an image, screenshot, video, audio or file and wants it saved, or asks to find past media (that diagram, the mockup from last week). Ingests and searches with local ChromaDB embeddings.
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。
ZIP 压缩包解压工具。递归扫描目录内 .zip 文件及分卷（.z01/.z02 等），合并分卷后解压并输出解压清单。当用户需要批量解压 zip 或分卷 zip 时使用此 skill。不负责 7z、rar 等其他格式的解压。
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。
7z 压缩包解压工具。递归扫描目录内 .7z 文件及分卷（.7z.001 等），解压并输出解压清单。依赖 py7zr 库。不负责 zip、rar 等其他格式的解压。
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。
7z 压缩包内容预览工具。递归扫描目录内 .7z 文件，列出内部文件清单并按需输出文本片段预览，供后续采集流程判断是否需要解压处理。 当用户提到 7z 预览、7z 压缩包内容查看、7z 文件清单等需求时使用此 skill。 即使用户没有明确说出"7z 预览"，只要任务涉及在解压前先查看 7z 压缩包内部文件结构和内容片段，就应该使用此 skill。 不负责 zip、rar、tar 等其他压缩格式的预览（各有独立 skill），也不负责解压或归档打包。
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。
RAR 压缩包解压工具。递归扫描目录内 .rar 文件及分卷（.part1.rar/.part2.rar 等），解压并输出解压清单。依赖 rarfile 库及系统 unrar 工具。不负责 zip、7z 等其他格式的解压。
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。
TAR 压缩包内容预览工具。递归扫描目录内 .tar / .tar.gz / .tar.bz2 / .tar.xz 文件，列出内部文件清单并按需输出文本片段预览，供后续采集流程判断是否需要解压处理。 当用户提到 tar 预览、tar.gz 预览、tar 压缩包内容查看、tar 文件清单等需求时使用此 skill。 即使用户没有明确说出"tar 预览"，只要任务涉及在解压前先查看 tar 压缩包内部文件结构和内容片段，就应该使用此 skill。 不负责 zip、7z、rar 等其他压缩格式的预览（各有独立 skill），也不负责解压或归档打包。
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。
TAR 压缩包解压工具。递归扫描目录内 .tar / .tar.gz / .tar.bz2 / .tar.xz 文件，解压并输出解压清单。不负责 zip、7z、rar 等其他格式的解压。
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。