「collection」の検索結果

Guide for multi-experiment batch operations: QC screening, batch download, comparison, and report generation across many ENCODE experiments simultaneously. Use when users need to process 5+ experiments together, create experiment comparison tables, perform batch quality checks, or generate summary reports. Trigger on: batch analysis, multiple experiments, bulk processing, experiment comparison, batch QC, multi-sample, batch download, experiment table, summary report, collection analysis.

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, validating, deploying, changing, or troubleshooting Volcengine TLS LogCollector collection on Linux hosts or Kubernetes, including Project, Topic, host group, collector rule, index, binding, heartbeat, parsing, and end-to-end log ingestion checks through volclog. 当用户需要设计、部署、 变更或排查火山引擎日志服务 LogCollector 采集（主机、Kubernetes DaemonSet/Controller、机器组、采集配置、 心跳、解析、日志未入库）时调用。

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。

Create and manage Volcengine cloud resources using the Volcengine CLI (`ve` command). Supports all Volcengine services including ECS, VPC, CLB, RDS, Redis, and more. Trigger this skill whenever the user asks to create, query, modify, or delete cloud resources on Volcengine, mentions the `ve` command, says "volcengine CLI", or describes infrastructure tasks such as "create an ECS instance", "set up a VPC", "list security groups", "allocate an EIP". Also trigger on Chinese prompts mentioning "火山引擎" or "火山" (e.g., "火山引擎上有哪些 ECS"、"查一下我火山的云服务器"、 "火山引擎创建一个 VPC"、"火山的 Redis 实例列一下"). Also trigger when the user encounters errors from `ve` commands and needs troubleshooting help. Also use for TOS bucket/object operations, tosutil (sometimes written tosutils), Ark CLI / arkcli model discovery, inference, generation, endpoints, fine-tuning, plans, or usage, and TLS (日志服务) / volclog log search, analysis, ingest, export, or LogCollector collection, through `ve tosutil`, `ve arkcli` and `ve volclog`.

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 ESG reporting software and sustainability metric accuracy. Analyzes Environmental (Scope 1/2/3 emissions, water, waste, biodiversity), Social (workforce diversity, pay equity, human rights due diligence), and Governance (board composition, ethics, risk) data collection against GRI.

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 game analytics and telemetry implementation -- event tracking completeness, FTUE and monetization funnel coverage, retention metric infrastructure, A/B testing framework, heatmap data collection, churn prediction signals, and LTV modeling support..

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Analyze an incident response program for playbook coverage, MTTR optimization opportunities, evidence collection readiness, root cause analysis quality, and post-incident review effectiveness. Triggers: building a SOC, assessing IR maturity, optimizing detection-to-recovery timelines.

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Analyze program impact measurement software for logic model completeness, indicator tracking rigor, data collection methodology, causal attribution modeling, cost-effectiveness analysis, beneficiary feedback integration, and funder reporting accuracy..

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 e-discovery and litigation document review systems -- data collection pipelines (PST, MBOX, SharePoint, Slack), document processing (OCR via Tesseract/ABBYY, metadata extraction, deduplication), Technology Assisted Review (TAR 1.0/2.0/CAL with recall/precision tracking).

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Analyzes audit readiness systems for internal control testing, evidence collection workflows, statistical sampling methodology, audit finding documentation, and remediation tracking using PCAOB, ISA, and SOX compliance frameworks..

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Design a research study. Use when the user says "study design", "design my research", "what research method should I use", "how do I test this hypothesis", "research plan", "methodology section", "experimental design", "survey design", "qualitative study design", "how to structure my study", "sample size", "data collection plan", or needs to plan how to test a research question or hypothesis - even if they don't explicitly say "study design".

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。

Smallest personal narrated-explainer-video pipeline (spoken narration over visuals, not an audio podcast), fully tool-agnostic and autonomous by default — topic → research ∥ asset collection → script → TTS → video → 4K render ∥ publish info + cover. The skill defines the pipeline logic and self-verified checkpoints; any TTS backend and any video tool (Remotion, HyperFrames, CapCut, ...) work, and how much human oversight to apply is set by the working project's AGENTS.md/CLAUDE.md, not here. Use when the user wants a quick personal narrated video with minimal steps, whether or not they name the tool stack. Do NOT trigger for audio-only podcasts, written episodic content, or heavy multi-format production.

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。

Configures GKE observability, including Cloud Logging, Cloud Monitoring, and managed Prometheus. Use when configuring GKE monitoring, setting up GKE logging, or configuring Prometheus metrics collection. Don't use to configure local application logging frameworks or external APMs outside GKE.

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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。

Use this skill when designing, troubleshooting, or reviewing FSL Mobile worker workflows — job lifecycle management, offline data capture, customer signature collection, parts consumption, and geolocation-triggered status transitions. Trigger keywords: FSL mobile, field service mobile workflow, offline job lifecycle, technician mobile, service appointment status, briefcase priming, parts consumption mobile, signature capture FSL. NOT for installing, branding, or extending the mobile app itself — use admin/fsl-mobile-app-setup. NOT for offline priming limits and sync conflict resolution — use architect/fsl-offline-architecture.

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Migrating Classic Custom Buttons (Detail Page Buttons, List Buttons, Mass Action Buttons) and JavaScript Buttons to Lightning Quick Actions, Screen Flow Actions, LWC Quick Actions, and headless Quick Actions. Covers JavaScript-button-to-action translation patterns (sforce.apex.execute → Apex action, navigation alerts → toast events, MassAction.update → record-collection processing), URL button translation, and the irreversible loss of Classic JavaScript button capability in Lightning. NOT for new Quick Action design (use admin/global-actions-and-quick-actions) or Lightning record page composition (use admin/lightning-app-builder-advanced).

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 planning or executing the human side of an Agentforce or Einstein AI feature rollout — user trust-building, AI-specific training, structured feedback collection via the Feedback API, and adoption measurement via Agentforce Analytics. NOT for turning the Einstein for Sales features on — use agentforce/agentforce-sales-ai-setup. NOT for rollout training with no AI in scope — use admin/change-management-and-training.

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。

Detects data collection patterns that exceed what is necessary for the app's stated functionality, enforcing Guideline 5.1.1(iii) data minimization requirements.

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。

สร้าง collection plan + mood board + pattern brief + fabric spec สำหรับแบรนด์แฟชั่น

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。

Wine expert for pairing, tasting, regions, and building a wine collection

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。

Guide for contributing new skills to the skills-x collection. This skill should be used when users want to add new open-source skills from external sources (like agentskills.io or anthropics/skills) to the skills-x repository. It covers the complete workflow from discovery to publishing.

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Retrospective learning from a completed experience. Takes a project, incident, decision that played out, or time period; gathers ground truth (observations) separately from recollections; spawns reflectors applying different lenses (what-worked-vs-got-lucky, what-didn't, what-surprised, system-rewards-vs-intent, decisions-that-aged, what-to-tell-past-self, patterns-that-recur) in isolation; synthesizes into updated mental models as first-class output. Produces feedback only — no code, no tickets, no artifacts.

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Run the same scrape or task across many accounts at once - each in its own browser profile with its own fingerprint, cookies and exit IP - and read data from sites that need a session or that answer a plain HTTP scraper with a captcha while answering a real browser. One command per site returns JSON, so there are no selectors to write or repair: Amazon and Walmart search, Google and DuckDuckGo results, Reddit posts and comments, X timeline, Medium, Yelp, Indeed job listings, Hacker News, GitHub, PyPI, npm, plus exit-IP and fingerprint checks. Use for multi-account operations you are authorized to run, price and listing monitoring per region, SERP collection, logged-in data, and scrapers that keep breaking. Also for 'scrape amazon', 'google search results', 'reddit data', 'scrape logged in site', 'run task on many accounts', 'site blocks my scraper', 'scraping without selectors'. Node or Python.

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Build a fully automated AI-powered data collection agent for any public source — job boards, prices, news, GitHub, sports, anything. Scrapes on a schedule, enriches data with a free LLM (Gemini Flash), stores results in Notion/Sheets/Supabase, and learns from user feedback. Runs 100% free on GitHub Actions. Use when the user wants to monitor, collect, or track any public data automatically.

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。

Checks structured logging, health check endpoints, metrics collection, request tracing, log levels. Returns findings with severity, location, effort, recommendations.

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。