「chat run」の検索結果

ベンガル語の検索やAI向けに、文字表現の統一、単語分割、数字変換、結合文字を壊さない表示処理を整理し、学習データの整備や保存にも役立つスキル。

• ベンガル語の検索や入力を実装したいとき
• ベンガル語の学習データ整備
• ベンガル語と英語の会話対応

初期設定で選んだアプリや利用目的を引き継ぎ、最初の依頼を具体的な成果物にします。仕事の要約、HTMLページ、自動化スクリプト、アプリ導入などの小さな作業を進めます。

• 接続したアプリで最初の仕事要約
• 仕事用リンクのスタートページ作成
• Downloads整理スクリプトの作成

After you give a substantive answer or draft that the user may act on — advice or recommendations, drafted artifacts such as goals, plans, pitches, proposals, or emails, estimates or projections, analysis or interpretation of data, factual claims they may rely on, or a multi-step argument — invoke this skill BEFORE finalizing your reply and then, if it applies, append 2-3 short follow-up questions, each tied to something specific in what you just produced, that help the user check key facts, probe the reasoning or assumptions, and notice missing context. Do this at most once per conversation. Skip it when the user asked a trivial how-to or simple lookup, wants a purely educational explanation, asked you only to format, convert, or assemble a file from content they provided, is writing code they will run, is doing creative writing or casual chat, or already asked you to double-check, cite, or review — the skill file explains these boundaries and the exact output format.

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-native tutor and onboarding workflow for the four independent Claude certification tracks in AI Engineering from Scratch. Use when a learner wants to choose a Claude certification, prepare for CCAO-F, CCDV-F, CCAR-F, or CCAR-P, resume a certification path, learn the next lesson interactively, run and verify practical labs, build scored artifacts, take a diagnostic or mock exam, or remediate weak exam domains from GitHub with Claude Code, Codex, ChatGPT, Cursor, or another agent.

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-native tutor and onboarding workflow for the MCPA (Model Context Protocol Associate) certification in AI Engineering from Scratch. Use when a learner wants to prepare for the MCPA, resume their certification path, learn the next lesson interactively, run and verify practical labs, take the diagnostic or a full mock, or remediate weak exam domains from GitHub with Claude Code, Codex, ChatGPT, Cursor, or another agent.

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。

Design a coherent assessment-and-practice lesson using zone-of-proximal-development principles: independent diagnosis, targeted review, supported practice, fresh independent checks, and an evidence-based next step. Use for 习题课（最近发展区）, 最近发展区, 测验习题课, diagnostic review lessons, or guided-to-independent practice. Can also run one practice cycle in chat; do not turn a request for a single exercise or theory explanation into a full lesson.

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。

Prepare, format, and validate datasets for supervised fine-tuning and preference training. Use when converting raw data into training format, applying chat templates, configuring sequence packing, generating synthetic training data, or writing a dataset card before a run.

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。

Retrieve and apply OpenViking Experience memories through the Agent runtime's generic OpenViking search and read tools. Use before or during executable, multi-step, or tool-based work such as coding, file or data changes, configuration, deployment, workflow execution, and failure recovery when prior operational guidance could improve reliability. Do not use for casual chat or simple factual questions.

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 for any CopilotKit question — adding it to an app, chat UI, frontend or server tools, generative UI, shared state, human-in-the-loop, agent frameworks (LangGraph, CrewAI, Mastra, ADK, PydanticAI, and others), the runtime, Intelligence, threads, voice, or diagnosing something that is not working. Do not answer from memory: this skill exists to point you at the current documentation and source, both of which you can read.

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 driving a WeKnora RAG server through the `weknora` CLI as an agent — authenticating, managing knowledge bases / documents / sessions / agents, running search or chat, or interpreting the CLI's JSON envelopes and exit codes. Read this before any other weknora-* 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。

Everything In Its Right Place. The universal post-work organizer. After any significant work session, EIIRP runs a 7-phase audit: (1) inventory every output, (2) walk taxonomy to decide where each lands, (3) check schema-pack consistency against the brain's actual shape, (4) file enriched brain pages, (5) audit the skill graph for DRY+MECE, (6) verify resolvability, (7) report. Named after the Radiohead song. Nothing produced during significant work lives only in chat — knowledge becomes permanent, patterns become reusable. Also carries the always-on auto-fire gate: when >=500 words of structured analysis on a user-shared document is about to be delivered, file the brain page first, then deliver the analysis with the link in that same reply.

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。

Audits and fixes both halves of being found — classic search and AI answers. Search side: rankings, site structure, page speed, titles and descriptions, and content that answers what customers search for. AI side: whether assistant crawlers can reach the site, pages render without JavaScript, an llms.txt file, schema.org data, and presence in the directories assistants read. For online stores it also runs a bulk catalog cleanup, rewriting product titles, descriptions, alt text, and SEO fields in approved batches. Works on any public website with no connector. Use this whenever the owner talks about being found online, including "nobody finds us on Google," "how do I show up in ChatGPT," "make my website AI friendly," "SEO audit," "GEO," "AEO," "AI search visibility," "my competitor ranks above me," "fix my product listings," or "I asked an AI about my industry and we weren't in the answer."

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 the owner's Monday morning briefing as a two-link chain — business-pulse for the cross-connector snapshot of cash, sales, and pipeline, and report-builder for any saved KPI reports the owner has already defined — then reads the week ahead straight from Calendar and mail and merges all three parts into a single one-page Monday document, delivered per the owner's stored output preference. Requires no connectors and degrades gracefully, building from whatever of the ledger (MYOB, NetSuite, QuickBooks, Xero, or Zoho Books), Calendar, DocuSign, Expensify, Gmail or M365, HubSpot, PayPal, Ramp, RingEx Chat, Shopify, Slack, Stripe, and TikTok Ads is connected, or from pasted and uploaded data when none are. Trigger on "Monday brief," "start my week," "what do I need to know this week," "weekly briefing," "catch me up before Monday," or when the owner schedules a recurring start-of-week update.

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。

Turns a plain-English description of a recurring report into a real, repeatable report — defines the metrics, pulls them from whatever data sources are connected, delivers a chat summary plus an XLSX workbook, and saves the definition so the same report reruns on demand or on a schedule. Handles anything from "sales by location versus last year" to AR aging to labor as a percentage of revenue. Works fully from an uploaded CSV or XLSX when no connector is available. Use this whenever the owner asks for a report, dashboard, KPI pack, metrics summary, or recurring numbers — including phrasings like "can you track this every week," "I need to see these numbers monthly," "build me a report on," "same report as last time," or "put together a KPI dashboard." Reach for it even when the owner names the metrics without using the word report.

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。

Sync tasks and refresh memory from your current activity. Use when pulling new assignments from your project tracker into TASKS.md, triaging stale or overdue tasks, filling memory gaps for unknown people or projects, or running a comprehensive scan to catch todos buried in chat and email.

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。

Google Workspace administration via the gws CLI (github.com/googleworkspace/cli). Install, authenticate, and automate Gmail, Drive, Sheets, Calendar, Docs, Chat, and Tasks. Run security audits and use local recipe templates and persona bundles. Use for Google Workspace admin, gws CLI setup, Gmail automation, Drive management, or Calendar scheduling.

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 the user wants to plan, promote, run, or improve a webinar or virtual event to generate and convert demand. Use when the user mentions 'webinar,' 'virtual event,' 'online event,' 'live demo,' 'virtual summit,' 'workshop,' 'masterclass,' 'fireside chat,' 'roundtable,' 'registration funnel,' 'show-up rate,' 'attendance rate,' 'webinar promotion,' 'webinar follow-up,' or 'on-demand webinar.' Also use when they have a webinar that isn't converting — low registrations, low show-up, or attendees who don't buy — and want to diagnose and fix it. Covers the full funnel: registration, promotion, show-up, live engagement, live-to-close, and post-event nurture. Distinct from launch-strategy (full product launches) and email-sequence (lifecycle nurture) — this is the end-to-end webinar/event motion. NOT for in-person field events logistics, and NOT for generic lifecycle email (use email-sequence).

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。

Export raw OpenTelemetry traces from an AI application to Confident AI's Observatory. TRIGGER when the user wants to send OpenTelemetry or OTLP traces/spans from an LLM app, agent, RAG pipeline, or chatbot to Confident AI; configure the Confident AI OTLP endpoint; set confident.span.* or confident.trace.* attributes; export AI-app traces without the deepeval Python package; wire an OTLPSpanExporter, OpenTelemetry Collector, or vendor-neutral OTel SDK to Confident AI; or pick the US vs EU OTLP endpoint. Language-agnostic: the mechanism is OTLP attribute keys plus an exporter endpoint. DO NOT TRIGGER for building DeepEval pytest eval suites, datasets, goldens, metrics, or deepeval test run (use the `deepeval` skill); for instrumenting with the DeepEval SDK's @observe decorator or framework integrations (use the `deepeval-tracing` skill); or for non-AI software such as web servers, CRUD backends, or infrastructure: the confident.* attributes describe AI components 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。

Run a source-grounded paid-advertising audit for one or more of Google, Meta, YouTube, LinkedIn, TikTok, Microsoft, Apple, Amazon, Reddit, Pinterest, Snapchat, and X. Use for full ad checks, account health reviews, paid-media diagnostics, partial audits after authentication or worker failure, missing-platform weighting, beta-feature eligibility and scoring, spend audits, tracking audits, or prioritized opportunities and risks.

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。

Reference for using Plannotator (its `plannotator` tool when you have one, otherwise the CLI): plan review, code review, annotating files, URLs, folders, and running local apps, annotating the last assistant message, browsing archived plan decisions, exporting or sharing Guided Reviews, and the Plannotator Inbox (messages and questions the person answers later, without blocking you). Invoke when asked to use Plannotator for anything not covered by a more specific plannotator-* skill, and before you leave the person a decision, a question or finished work while they are away from the chat (if another agent started you, report to it instead).

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。

Resize, crop, or export any image or video into platform-ready social media assets using Adobe Creative Cloud tools. Use this skill when a user wants to prepare a photo, image, or video for one or more social platforms — Instagram, TikTok, LinkedIn, Facebook, YouTube, Snapchat, Pinterest, Threads, or X/Twitter. Triggers on: "prepare my image for Instagram", "resize for TikTok", "get this ready to post", "make versions for all platforms", "social media sizes", "crop for stories", "export for LinkedIn", "resize my video for social", "make social media assets", or any request to adapt a photo or video for specific platforms. Handles subject-aware cropping, AI canvas expansion, test previews before full runs, and same-ratio video resizing.

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。

Add accessible animated AI loading and agent-status indicators with the React thinking-orbs library. Use when a chat, copilot, voice, search, generation, or tool-running interface needs a semantic working, searching, solving, listening, composing, or shaping state; when replacing a generic spinner with an AI activity orb; or when implementing the library's size, theme, speed, pause, reduced-motion, and canvas behavior.

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 asked to watch, babysit, audit, review, compare, or fix another agent's work from a Codex session ID, Claude Code session/transcript, chat/thread link, PR, branch, log, or pasted run summary. Monitor until the other agent is done or blocked, reconstruct what the user asked, independently investigate the same problem to form your own hypotheses and approach, inspect what the agent actually changed and verified, compare the two investigations, report gaps and add-on directions, and optionally make scoped fixes when the user authorizes repair.

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 building, deploying, evaluating, debugging, or measuring latency for the DeepStream SOP Inference Microservice — a GPU-accelerated FastAPI service that detects whether operators perform assembly-line steps in order via event boundary detection (GEBD) plus VLM classification. Trigger even if the user does not name it: verify operator step sequence, detect missing or out-of-order SOP steps, score factory/work-cell video for procedure compliance, run VLM-based SOP checking on industrial cameras, or call /v1/chat/completions with a file, RTSP, or Basler camera. Also trigger for its internals: SOPVideoProcessor, DeepStream GEBD model (e.g. DDM) via Triton CAPI, nvds_custom_postprocess, Cosmos Reason 1/2 vLLM, SSE streaming, Kafka NvProto/JSON output, Basler/Pylon camera + emulation, Docker compose, chunk-level latency. Do NOT trigger for generic DeepStream pipelines, object detection/tracking, NIM imports, or video summarization.

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。