Loading...
Loading...
Found 552 Skills
Invoke the `groundcover` Go CLI to manage Groundcover resources (dashboards, monitors, silences, connected apps, notification routes, API keys, policies, integrations, pipelines, workflows) AND to answer production observability questions by querying logs, traces, metrics, k8s inventory, and k8s events. Use whenever a task needs an authenticated call against the Groundcover API or whenever the user is debugging a prod issue and asks things like "why is X erroring in prod", "show me logs for service Y", "what's the p99 latency on Z", "what pods are crashlooping", "search traces for slow requests", "any k8s events for namespace N", "is service S receiving traffic", "list groundcover monitors", "create a silence", "update notification route", "hit a groundcover endpoint". Covers required env vars, the SDK-backed vs raw command split, and concrete request-body templates for logs/traces/metrics/k8s so the CLI can be driven from anywhere.
Sets up AWS Resilience Hub v2 from scratch: creates resilience policies with SLO targets, registers systems and user journeys, onboards services with input sources, and runs a first failure mode assessment. Applies when the user wants to get started with Resilience Hub v2, create a policy, onboard a service, or run an assessment — including creating one concrete policy with specific availability/RTO/RPO targets and a DR approach for a single service (even a tier-1 one). Does not apply to FIS experiments or ARC routing controls.
Identify which field values correlate with bad behavior (slowness, errors, anomalies, unusual values) using phi-coefficient correlation analysis over OPAL. Works on any time-series data — metrics, structured logs, span/trace data, or any dataset where rows can be split into a 'bad' and 'good' cohort by a threshold. Use when: (1) User asks for root-cause analysis on a dataset or metric (2) User wants to know what attributes / dimensions / values are most associated with a failure mode, anomaly, or unusual cohort (3) Investigating which services, hosts, regions, namespaces, or attributes drive outliers (4) User mentions phi coefficient, correlation, or outlier detection (5) User asks 'why is X slow/failing', 'what caused the errors on X', or 'what's different about the bad cohort'.
Guides tenant isolation architecture in Qdrant for multi-tenant or multi-user applications. Use when someone asks 'how to isolate customer data', 'how to build multi-tenant search/RAG', 'how many collections should I create', 'how to partition tenants by payload', 'a customer's data legally has to stay in a certain country or region'. Also use when they describe a symptom: one customer's data is way bigger than the rest and slowing everyone down, or one tenant is hogging resources.
SQL and NoSQL schema design with normalization, indexing, and migration patterns. Use when designing database schemas, creating tables, optimizing slow queries, or planning database migrations.
Automatically analyze performance issues when user mentions slow pages, performance problems, or optimization needs. Performs focused performance checks on specific code, queries, or components. Invoke when user says "this is slow", "performance issue", "optimize", or asks about speed.
Render views and templates in Phoenix using HEEx templates, function components, slots, and assigns
Manage power and performance metrics and diagnostic logs using the `asc` CLI tool. Use this skill when: (1) Listing performance metrics for an app: "asc perf-metrics list --app-id <id>" (2) Listing performance metrics for a build: "asc perf-metrics list --build-id <id>" (3) Filtering metrics by type: "asc perf-metrics list --app-id <id> --metric-type LAUNCH" (4) Listing diagnostic signatures for a build: "asc diagnostics list --build-id <id>" (5) Filtering diagnostics: "asc diagnostics list --build-id <id> --diagnostic-type HANGS" (6) Viewing diagnostic logs: "asc diagnostic-logs list --signature-id <id>" Also trigger when the user mentions: performance metrics, launch time, hang rate, disk writes, memory usage, battery life, termination, animation hitches, diagnostic signatures, call stacks, power metrics, app performance monitoring, "why is my app slow", "check hangs", "check launch time"
Use when optimizing application performance, reducing load times, improving database queries, meeting performance budgets, or diagnosing bottlenecks in web applications or APIs. Triggers: slow page loads, poor Web Vitals, database timeouts, large bundle size, user-reported sluggishness, scaling preparation.
Prevent Ethereum hashing bugs in JavaScript and TypeScript. Node's sha3-256 is NIST SHA3, not Ethereum Keccak-256, and silently breaks selectors, signatures, storage slots, and address derivation.
Diagnose, compare, and optimize Apache Spark applications and SQL queries using Spark History Server data. Use this skill whenever the user wants to understand why a Spark app is slow, compare two benchmark runs or TPC-DS results, find performance bottlenecks (skew, GC pressure, shuffle spill, straggler tasks), get tuning recommendations, or optimize Spark/Gluten configurations. Also trigger when the user mentions 'diagnose', 'compare runs', 'why is this query slow', 'tune my Spark job', 'benchmark comparison', 'performance regression', or asks about executor skew, shuffle overhead, AQE effectiveness, or Gluten offloading issues.
Apply Hierarchical Linear Modeling (HLM) to analyze nested data structures with random intercepts and slopes, accounting for intra-class correlation and cross-level interactions. Use this skill when the user has students nested in schools, employees in firms, or repeated measures in individuals, needs to partition variance across levels, or when they ask 'how do I handle nested data', 'what is ICC', or 'do group-level factors moderate individual-level relationships'.