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Found 1,363 Skills
Focused Signals scout for finding observability gaps in PostHog itself — significant event volumes the team isn't tracking, custom events with no insight or dashboard coverage, insights pointing at events that have stopped firing, dashboards missing related context, critical events with no alerts. Watches the event-stream-vs-saved- inventory delta as the team's product evolves and emits findings recommending new insights, dashboard additions, or alerts when gaps clear the confidence bar. Self-contained peer in the signals-scout-* fleet — no dependencies on other skills.
Deep-dive analysis of a single topic — heat curve, lifecycle stage, viral anomaly check, near-future prediction, related events, and sentiment evolution via the trendradar MCP. Use when the user asks 深挖XX / 这个话题怎么演变的 / XX 的趋势 / 生命周期 / XX 会不会火 / 异常热度 / 舆情演化 / 把 XX 说清楚 / predict X 的走势 / deep dive on X.
Use this skill when your backend needs to read or update RevenueCat state on Android. Covers the RevenueCat REST API (v1 subscribers endpoint, grant/revoke entitlements, attributes), secret vs public SDK API key usage, and why you do not build a receipt verification backend with RevenueCat.
Use this when deciding whether data belongs in Redux, component state, router state, or another external source. Covers state ownership, authority boundaries, slice sizing, and when to move or split data as the app evolves.
Baidu FaMou algorithm skills for efficient algorithm self-evolution. Provides experiment management and visualization capabilities to help optimize complex algorithms. Use when user needs algorithm optimization or experiment management.
Consistent Nova resources—fields, actions, metrics, lenses, filters, authorization—and how to evolve resources alongside schema changes
Guidance for detection engineering in Microsoft Sentinel — building, testing, deploying, and maintaining analytics rules, hunting queries, and SOAR automation. Covers the Content Hub solution model, MITRE ATT&CK mapping, scheduled vs near-real-time (NRT) vs Fusion vs anomalies analytics, KQL detection patterns (joins, summarize, bin, materialize), entity mapping and incident enrichment, custom detections from Defender XDR vs Sentinel-only, automation rules, playbooks (Logic Apps), watchlists, threat intel matching, content as code with Azure DevOps / GitHub repositories integration, and detection lifecycle (validate → tune → version). WHEN: Sentinel analytics rule, KQL detection, MITRE mapping, Sentinel content hub, scheduled analytics, NRT rule, hunting query, Sentinel automation rule, Logic App playbook, custom detection, repositories Sentinel CI/CD, detection-as-code, watchlist, threat intel matching analytics, fusion alerts, anomalies, incident enrichment, entity mapping. DO NOT USE for Sentinel architecture/onboarding (use sentinel), Defender XDR custom detections only (overlap—use the side that owns the data), or generic KQL training.
Build well-crafted production procedural meshes in Three.js. Use for complete hard-surface assemblies and humanoid robots, profile extrusion, parameter-curve and spine lofts, pillow panels, exact polygon cuts, inset, revolve, sweep, solidify, bevels and fillets, shell thickness, direct-topology apertures, semantic mesh writers, or diagnosing primitive-built forms, coplanar flicker, loose/non-manifold geometry, detached parts, interpenetration, support, clearance, and swept-envelope defects.
Deploy applications and websites to Vercel. Use when the user requests deployment actions like "deploy my app", "deploy and give me the link", "push this live", or "create a preview deployment".
Deploy and manage projects on Vercel using token-based authentication. Use when working with Vercel CLI using access tokens rather than interactive login — e.g. "deploy to vercel", "set up vercel", "add environment variables to vercel".
Assess whether source code is ready to deploy to Azure — the check BEFORE infrastructure work. Evaluates build health, app completeness, dependencies and local services, stack compatibility, and deployment feasibility. Answers questions about what your app needs before it can be deployed — frameworks, dependencies, and configuration. Checks whether dependencies are compatible and identifies deployment blockers and unsupported frameworks. WHEN: "evaluate my repo", "is my app ready to deploy", "what does my app need to deploy", "what do I need before deploying", "does my app need", "can I ship this to Azure", "scan my repo for issues", "is this app deployable", "check if my app is ready for Azure", "do I need a Dockerfile", "what's blocking my deployment", "are there any blockers", "are my dependencies compatible", "does Azure support my framework", "what needs to change before deploying", "check my app configuration".
Analyze application logs to identify errors, performance issues, and security anomalies. Use when debugging issues, monitoring system health, or investigating incidents. Handles various log formats including Apache, Nginx, application logs, and JSON logs.