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Found 872 Skills
Scrape and extract public data from 27+ social media platforms using the ScrapeCreators REST API. Covers TikTok, Instagram, YouTube, LinkedIn, Facebook, Twitter/X, Reddit, Threads, Bluesky, Pinterest, Snapchat, Twitch, Kick, Truth Social, TikTok Shop, Google, and link-in-bio services (Linktree, Komi, Pillar, Linkbio, Linkme, Amazon Shop). Use when the user asks to scrape, fetch, extract, search, or look up social media profiles, posts, videos, reels, comments, transcripts, followers, ads, hashtags, trending content, or engagement metrics from any social platform. Also use when user mentions ScrapeCreators, social media API, ad library, or creator data.
Automate batch creation and management of Huawei Cloud CES alarm rules for ECS instances using hcloud CLI v7.2.2+. Use this skill to: (1) batch create alarms with templates (web/database), (2) update SMN notifications, (3) query ECS metrics and alarm lists. Trigger: "ECS alert", "create alert", "list alarms", "CPU alert", "memory alert", "ECS monitoring", "监控告警", "创建告警", "ECS 监控", "告警规则", "查询告警"
Social Engagement Benchmark for cross-platform social media engagement benchmark collection, research, monitoring, analysis, and export. Use when the user asks to scrape, extract, collect, export, monitor, research, analyze, or find browser-visible cross-platform social media data for this workflow: Collect comparable engagement metrics for creators, brands, competitors, or campaigns. Covers searches such as Social Engagement Benchmark, cross-platform social media engagement benchmark scraper, cross-platform social media engagement benchmark extractor, cross-platform social media engagement benchmark export, cross-platform social media engagement benchmark research. Supports public or authorized browser-visible data through BrowserAct.
Collect ranking pages from Bilibili — ranked entries, positions, category, metrics. Use when the user wants to monitor ranking lists and top items.
Measure GTM with the metrics that matter (net developer retention, DREAM funnel) instead of vanity numbers. Use when the founder has dashboards full of stars and pageviews but can't tell if go-to-market is working, or is optimizing acquisition over a leaky bucket.
Generate a monthly financial summary with metrics, trends, and anomalies.
Analyzes observability signals from customer GenAI applications with DQL. Reads OpenTelemetry GenAI spans and LLM evaluation bizevents. Use for: golden signals (traffic, errors, latency, saturation); LLM signals (model, provider, tokens); cost/token analytics, usage attribution, and prompt caching; agent signals (tool calls, steps, failures, loop detection, Smartscape topology); conversation/session analytics; guardrails (blocked/truncated responses); and evaluation signals (quality, pass/fail). Trigger: "LLM latency", "token usage by model", "cost by model and provider", "cost per conversation", "who is driving token spend", "do I have prompt caching", "failing agent tool calls", "find runaway agents", "responses truncated or blocked", "failed evaluations", "am I hitting rate limits", "token throughput / TPM", "provider throttling or 429s". Do NOT use for: Davis CoPilot/MCP telemetry (dt-platform), generic service metrics (dt-obs-services), logs (dt-obs-logs), or non-GenAI tracing (dt-obs-tracing).
Run bounded, evidence-driven training research through W&B Launch: assess project readiness, establish launchable code and queue capacity, smoke-test real jobs, execute serial trials, compare metrics, and persist resumable research state. Use when a coding agent is asked to autonomously test training hypotheses or tune a real W&B-tracked workload.
Own experiment evidence semantics: decide datasets, baselines, metrics, ablations, robustness tests, chart evidence, and exactly what rows or columns a result table should contain. Use for experiment design, benchmark planning, supplied-result evidence structure, result-table schema, chart-spec semantics, 设计实验, 对比实验, 消融, 结果表证据结构. Do not search literature as the main deliverable, visually beautify or render an already specified table/figure, improve layout/colors/readability, or invent results.
A skill for huawei cloud container(CCE) assessment. It automatically collects metrics and configurations from containerized application environments on Huawei Cloud to generate a comprehensive assessment report. Use this when users want to evaluate if their Huawei Cloud applications align with cloud-native best practices and identify areas for improvement.
How to use KubeSense MCP tools to query logs, traces, and metrics from Kubernetes clusters. Covers tool selection, the discovery-first workflow, and links to datasource-specific skills.
Huawei Cloud CCE Metric analysis skill using the Python dispatcher with hcloud-backed cloud service queries. Use this skill when the user wants to: (1) query Pod/Node/CoreDNS/nginx-ingress/autoscaler/control-plane CPU, memory, disk, QPS, latency, request, connection, certificate, scaling, or error-rate metrics, (2) get resource usage TopN rankings, (3) query ECS/ELB/EIP/NAT cloud resource metrics, (4) aggregate cluster monitoring data with anomaly detection, (5) detect threshold-based resource anomalies. Trigger: user mentions "metric analysis", "指标分析", "CCE metrics", "CCE 指标", "AOM metrics", "AOM 指标", "CoreDNS metrics", "CoreDNS 指标", "nginx ingress metrics", "nginx-ingress 指标", "autoscaler metrics", "autoscaler 指标", "HPA metrics", "HPA 指标", "apiserver metrics", "etcd metrics", "controller manager metrics", "scheduler metrics", "control plane metrics", "控制面指标", "certificate expiration", "证书过期", "resource metrics", "资源指标", "CPU usage", "CPU 使用率", "memory usage", "内存使用率", "performance monitoring", "性能监控", "TopN", "resource ranking", "资源排名"