'polling' 검색 결과

xAI Grok Imagine API integration for image generation, text-to-video, image-to-video, and editing via natural language. Use when you need to generate images or videos from text prompts, edit existing images, animate static images into videos, or edit existing videos with natural language instructions. Supports conversational generation across messaging platforms with async polling, progress updates, and automatic delivery.

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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'.

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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.

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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.

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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.

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Validate bugs and features that depend on local hosts, localhost, filesystem home state, auth, polling, installed apps, real conversations, or signed native helpers.

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Generate Atlas Cloud images and videos through its asynchronous media API with schema-first model selection and credential-safe polling.

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Use when the user asks to "monitor brand mentions", "set up social listening", "did anything spike about us this week", or "watch these accounts for buying triggers"; runs always-on keyless listening — a versioned listening-query architecture (brand variants incl. misspellings and 中文 names, exclusion terms, per-source syntax for HN Algolia / Bluesky / GDELT / Tavily), a mention sweep with six-class triage (crisis / bug / lead / praise / question / spam) each routed with an SLA, a 7-day rolling baseline with spike flags (the crisis trigger input), a B2B trigger watchlist (funding / hiring / launch signals), and an explicit coverage disclosure where X/IG/TikTok/LinkedIn/小红书 numbers are proxy-labeled, never Measured. Not for launch-window telemetry (T-0→T+30 rank and review polling) — use launch-monitor. 舆情监听/品牌提及监测/基线突刺/触发信号

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Use when the user asks to "monitor my launch", "track our Product Hunt / Hacker News ranking", or "watch the launch window"; runs the T-0 to T+30 window watch — pre-launch instrumentation verification (UTM/event checks, the upstream of RAMP P1), HN rank/points/comments polling with a comments-over-points flamewar early-warning (Estimated heuristic), PH votes/featured status, store charts and reviews, news echo, D0/W1/M1 KPI snapshots vs targets, spike-vs-sustain and owned-capture reads, and alert thresholds against the launch-tier KPI targets. Not for launch-day go/rollback calls — use launch-day-conductor; not for metric deep-dives — use performance-analyzer; not for SEO rank tracking — use rank-tracker. 发布监控/排名轮询/火焰战比/spike-sustain

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Replace arbitrary timeouts with condition polling for reliable async tests

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Identify and avoid Adobe-specific anti-patterns: using deprecated JWT auth, not caching IMS tokens, ignoring Firefly content policy, missing async job polling, and leaking p8_ secrets. Real code examples with fixes. Trigger with phrases like "adobe mistakes", "adobe anti-patterns", "adobe pitfalls", "adobe what not to do", "adobe code review".

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ALWAYS use this skill whenever you would otherwise ask the user a question in free-form chat -- for clarifications, confirmations (especially destructive actions), missing parameters, multiple-choice decisions, or structured form input. Elicitations are routed through the Unique AI Platform UI via `unique-cli elicit create` + `elicit wait` (the default polling pattern) or a single supervised `unique-cli elicit ask` call (only when your agent instructions say your environment supervises the wait) so the user gets a proper structured prompt and you get a structured answer back. Do NOT ask the user in plain chat when you can use this skill instead.

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Push browser tab and download lifecycle events plus manager-owned background tabs. Read this when an agent needs lifecycle notifications or a temporary tab without polling.

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Generate 3D models, textures, images, rig characters, animate them, and prepare for 3D printing using the Meshy AI API. Handles API key detection, task creation, polling, downloading, and full 3D print pipeline with slicer integration. Use when the user asks to create 3D models, convert text/images to 3D, texture models, rig or animate characters, 3D print a model, or interact with the Meshy API.

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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.

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Export PostHog events, persons, or sessions on demand and download the resulting files. Use when the user asks to download/export raw PostHog data, create a one-off file export, fetch a Parquet or JSONLines export, or use the file_download_batch_exports API. Covers starting the export with MCP, polling completion, and downloading via the existing REST redirect endpoint.

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Generate Skills from HTTP MCP servers with async job patterns (submit/status/result). Use when converting MCP specifications (.mcp.json) into reusable Skills using mcp_tool_catalog.yaml. Supports --lazy mode for context-saving generation. Also use for calling async MCP tools via JSON-RPC 2.0 with session-based polling.

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Implement real-time features using Convex reactive queries that automatically update when data changes, enabling live collaboration, instant updates, and reactive UIs without manual polling. Use when building live dashboards, implementing collaborative editing, creating chat applications, showing real-time notifications, building activity feeds, implementing presence indicators, creating reactive search, or any feature requiring instant data synchronization across clients.

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Babysit a GitHub pull request during a user-approved monitoring window by polling review comments, CI checks/workflow runs, and mergeability state. Diagnose failures, retry likely flaky failures up to 3 times, auto-fix/push branch-related issues when appropriate, and report fresh review feedback. 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.

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Doctrine for all Supabase realtime: postgres_changes, broadcast, presence. Use before writing a `.channel(`/useChannel subscription, echo suppression, reconnect logic, an autosave loop beside realtime, or replacing polling with live updates, or when the tab freezes, messages duplicate, or a subscription delivers nothing.

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Outbox Pattern knowledge base. Provides patterns, antipatterns, and PHP-specific guidelines for transactional outbox, polling publisher, and reliable messaging audits.

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Poll RSS, JSON APIs, and GitHub with watermark dedup.

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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.

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