「polling」の検索結果

Maintain optional cross-device portal login, its directive parser, QR and polling UI, approving-browser binding, single-use transfers, MFA and provider evidence, logout invalidation, and TLS/browser tests.

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 AWS data pipelines using @aws-sdk/client-s3 and @aws-sdk/client-sqs. Manages S3 object lifecycle with PutObjectCommand/GetObjectCommand, processes SQS message queues via ReceiveMessageCommand with long polling, and configures S3 event notifications to SQS for event-driven processing.

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。

Send, read, and respond to Telegram messages as Claudicle via the @claudicle_bot Bot API. Use this skill when sending Telegram messages (text, Markdown, inline keyboards), building approval gates with tappable buttons, checking the shared inbox for unhandled Telegram messages, answering callback queries, managing Telegram memory context, or checking bot and listener status. Five purpose-built scripts (send, check, status, callback, memory) wrap the python-telegram-bot v22 polling adapter at ~/.claudicle/adapters/telegram/ with REST-based senders (no asyncio). Pairs with /telegram-respond for the cognitive pipeline and with mazkir-callback-watcher.py for the Mazkir approval gate.

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。

Monitor, cap, and recover from context accumulation in agentic systems. Covers per-cycle cost tracking, context window auditing, budget caps with enforcement policies, emergency pruning when approaching limits, and progressive disclosure integration to minimize token spend on routing. Use when running long-lived agent loops (heartbeats, polling, autonomous workflows), when context windows are growing unpredictably between cycles, when API costs spike beyond expected baselines, when designing new agentic workflows that need cost guardrails from the start, or when post-mortem analysis reveals a cost incident caused by context accumulation.

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。

Select a `delaySeconds` value when scheduling a loop wakeup via the `ScheduleWakeup` tool or the `/loop` slash command. Covers the three-tier cache-aware decision (cache-warm under 5 minutes, cache-miss 5 minutes to 1 hour, idle default 20 to 30 minutes), the 300-second anti-pattern, the [60, 3600] runtime clamp, the minute-boundary rounding quirk, and writing a telemetry-worthy `reason` field. Use when designing an autonomous loop, when a heartbeat cadence is being planned, when polling cadence is being tuned, or when post-hoc review of loop costs reveals interval mis-sizing.

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 implementing delays that must survive process restarts (e.g., 24-hour cart abandonment, scheduled follow-ups), waiting for human approval or external events with timeouts (review gates, webhook callbacks, async API completion), polling external services without losing state on crashes, calling other functions and awaiting their results, memoizing expensive operations so they don't re-run on retry, or running async work in parallel inside a workflow. Covers Inngest step methods: step.run, step.sleep, step.waitForEvent, step.waitForSignal, step.sendEvent, step.invoke, step.ai, plus patterns for loops and parallel execution.

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 analyzing an existing TypeScript or JavaScript codebase to decide where and how to introduce Inngest. Covers repository discovery, framework and package detection, finding durability gaps in HTTP handlers, webhooks, cron jobs, queues, long-running jobs, AI agents, Agent Evals, polling loops, eval loops, and side-effect-heavy code, then producing and implementing an incremental integration plan.

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。

Optimizes Atlassian Forge apps to reduce platform consumption and avoid unnecessary costs using Atlassian's "Optimise Forge platform costs" guidance. Use when the user asks to optimize Forge app costs, reduce Forge invocations, lower GB-seconds, reduce storage or log usage, tune memory, replace polling, improve scheduled triggers, reduce KVS writes, move work to the frontend, use bridge APIs, batch API calls, add caching, or evaluate Forge Remote trade-offs. By default, perform an audit first and offer to make the recommended changes after presenting the audit. Only modify files immediately when the user explicitly asks the agent to implement or apply optimizations.

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。

Generate AI video ads and image ads through the Novoads REST API (api.novoads.ai). Use when the user wants a UGC video, a product video, a talking-head ad, an AI actor holding a product, a TikTok or Reels or Shorts ad, a static image ad, or asks to "make me an ad", "generate a video", or names a model (Seedance, Seedance 2.5, Seedance Mini, Omni Flash, Veo 3.1, Sora 2, GPT Image 2, Nano Banana Pro, Reve). Handles upload, dialogue approval, cost confirmation, generation, polling, QA, and download. A product photo lands here when it SEEDS a new scene; when the picture itself should move, unchanged, that is image-to-motion. Not for editing an existing video file and not for publishing to an ad platform.

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。

Optional Moonbase license-activation integration for Pulp — load-bearing compile settings, OpenSSL-at-configure caveat, the moonbase-pulp User-Agent contract, audio-thread gating + click-free fade, async start/pump, the interactive native (no-WebView) activation editor (frame-tick polling + the don't-rebuild-mid-event trap), loadable plugin/standalone formats, and headless screenshots.

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 Chanjing text-to-digital-person APIs for AI portraits, talking videos, optional LoRA training, polling, and explicit downloads when requested.

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 Chanjing video synthesis APIs to create digital human videos from text or audio, with optional background upload, polling, and explicit download.

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 designing, implementing, or reviewing frontend-to-backend API integration, typed API clients, REST/tRPC/OpenAPI client choices, auth refresh, API error mapping, upload flows, SSE/WebSocket/polling choices, CORS-facing frontend behavior, or cross-boundary loading/error states. Do not use for backend-only service architecture or TanStack Query cache policy alone; Chinese triggers include API integration, front-end and back-end joint debugging, typed API client, Interface error handling, SSE, WebSocket.

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。

Drive running Unity Editor from shell to verify compiles, playmode, scenes, screenshots. Use when editing C# scripts, scenes, or assets in a Unity project and needing to: (1) check compilation succeeds, (2) read console errors, (3) enter/exit Play mode, (4) capture screenshots or frame recordings, (5) select objects or aim the Scene camera, (6) query editor window layout. Injects a file-polling command bridge (UnityRemote.cs) — prefer this over batchmode (fights the Library lock) or GUI automation.

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 native Linux packages (.deb / .rpm / .apk / .pkg.tar.zst) from a PKGBUILD or yap.json project using the yap-mcp Model Context Protocol server. Use this skill whenever the user asks to "build a package", "make a deb/rpm", "cross-compile for arm64/aarch64", "sign a release", generate SBOMs, install a built artifact, or diagnose why a yap build failed. The yap MCP server exposes async builds with buildID polling, validation, dependency-graph inspection, and artifact discovery.

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。

Black Forest Labs FLUX image generation: flux-2-pro and flux-2-flex text-to-image with async polling. Trigger phrases: black forest labs, BFL, flux image, flux-2-pro, flux 2 generate.

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。

Diagnoses and prevents vbar_control_agent segfaults, out-of-memory (OOM) errors, and TPU device initialization failures on TPU v6e nodes in GKE caused by race conditions during TPU device resets or high-frequency metrics polling. Use when troubleshooting vbar_control_agent crashes, memory cgroup OOMs in serial console logs, tpu-device-plugin metrics checksum corruption errors, or custom TPU metrics collection conflicts on GKE TPU v6e nodes. Don't use for general non-TPU container OOM troubleshooting or standard GKE node lifecycle 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。

Babysit a GitHub pull request after creation by continuously polling review comments, CI checks/workflow runs, and mergeability state until the PR is merged/closed or user help is required. Diagnose failures, retry likely flaky failures up to 3 times, auto-fix/push branch-related issues when appropriate, and keep watching open PRs so fresh review feedback is surfaced promptly. Use when the user asks Codex to monitor a PR, watch CI, handle review comments, or keep an eye on failures and feedback on an open 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。

Add a text watermark to one or multiple PDFs by uploading them to the Solutions API, polling until completion, then returning download URL(s) for the watermarked PDF(s) (or a ZIP if multiple).

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 monitoring bots that poll APIs and send notifications on state changes (new listings, price alerts, status 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。

Babysit a GitHub pull request after creation by continuously polling review comments, CI checks/workflow runs, and mergeability state until the PR is merged/closed or user help is required. Diagnose failures, retry likely flaky failures up to 3 times, auto-fix/push branch-related issues when appropriate, and keep watching open PRs so fresh review feedback is surfaced promptly. Use when the user asks Codex to monitor a PR, watch CI, handle review comments, or keep an eye on failures and feedback on an open 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。

Use when polling external AIs (OpenAI, Anthropic) outside the host session for a neutral second opinion on a roadmap, diff, prompt, or file set — or 'cross-check with another model'.

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 reading, writing, or watching the macOS clipboard — NSPasteboard types and multiple representations, the changeCount polling pattern, the transient/concealed pasteboard conventions clipboard tools must respect, and the Sequoia paste-privacy alerts.

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。

This skill covers deploying anomaly detection systems for industrial control environments using machine learning models trained on OT network baselines, physics-based process models, and behavioral analysis of industrial protocol communications. It addresses building normal behavior profiles for SCADA polling patterns, detecting deviations in Modbus/DNP3/OPC UA traffic, identifying rogue devices, and correlating network anomalies with physical process data from historians.

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。