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Found 7,062 Skills
Production-ready React form patterns using React Hook Form (default) and TanStack Form with Zod integration. Use when building forms in React applications. Implements reward-early-punish-late validation timing.
Fast Track for Feature Process - When requirements are clear and scope is small, skip the complete design process, write a compact {slug}-design.md, and proceed directly to implementation after one confirmation from the user. What is compressed is divergent discussions and phased reviews, not quality standards - code pointers, acceptance criteria, etc., must not be omitted. Trigger scenarios: User says "quick mode", "fastforward", "cut the steps", "just start working". Not suitable for complex features involving cross-subsystem integration, new terminology sorting, or more than 4 promotion steps - in these cases, proactively inform the user to revert to the complete process.
Document the pitfalls encountered or good practices discovered during this work into searchable learning documents, so that both AI and humans can look them up when similar tasks arise in the future. Two tracks: The pitfall track records experiences where "things should have worked but didn't" — bugs, configuration traps, environment issues, integration failures; The knowledge track records findings that "should be the default approach going forward" — best practices, workflow improvements, reusable patterns. Trigger scenarios: Proactively prompt for input when wrapping up feature-acceptance or issue-fix, or when the user says phrases like "document knowledge", "learning", "document learnings", "record this experience". Spec documents record what was done and how it was done, while learning documents record what pitfalls were encountered / what was learned — the two complement each other and are not interchangeable.
Grafana Pyroscope continuous profiling platform. Covers instrumentation of Go/Java/Python/Ruby/Node.js/ .NET/Rust apps via SDKs or eBPF (Alloy), flame graph analysis, ProfileQL queries, server configuration and architecture, Grafana Cloud Profiles integration, and trace-profile linking (Span Profiles). Use when working with profiling data, instrumenting apps for Pyroscope, analyzing performance profiles, or deploying Pyroscope server.
Prometheus and Grafana Cloud Metrics overview including PromQL query language, Metrics Drilldown, alerting, recording rules, and integration patterns. Use when working with Prometheus, writing PromQL queries, configuring alerting, or discussing metrics architecture and best practices.
Grafana Tempo distributed tracing backend. Covers TraceQL query language (span selectors, attribute scopes, pipeline operators, structural operators, metrics functions), trace ingestion via OTLP/Jaeger/Zipkin, Tempo architecture (distributor/ingester/compactor/querier/metrics-generator), full configuration reference with YAML, metrics-from-traces (span metrics, service graphs, TraceQL metrics), deployment modes (monolithic/microservices/Helm/Kubernetes), multi-tenancy, performance tuning, caching, and HTTP API. Use when working with distributed traces, writing TraceQL queries, deploying Tempo, configuring trace pipelines, or setting up Grafana-Tempo integrations (traces-to-logs, traces-to-metrics, traces-to-profiles).
Use this skill whenever the user wants browser-based end-to-end tests for an Adobe App Builder application. Covers Playwright E2E testing for ExC Shell SPAs, AEM extension UIs, and full-stack flows. Use when the user mentions: "E2E test", "end-to-end test", "Playwright", "browser test", "test my SPA in the browser", "test my AEM extension", "test the full flow", "integration test with UI", "headless browser test", "E2E in CI". This skill is for BROWSER-based testing only. For Jest unit tests of actions or React components, use appbuilder-testing instead.
Mozilla Observatory integration. Manage data, records, and automate workflows. Use when the user wants to interact with Mozilla Observatory data.
Provides guidance for LLM post-training with RL using slime, a Megatron+SGLang framework. Use when training GLM models, implementing custom data generation workflows, or needing tight Megatron-LM integration for RL scaling.
Document the pitfalls encountered or good practices discovered during this work into searchable learning documents, which can be accessed by both AI and humans when similar tasks arise in the future. Two tracks: The pitfall track records experiences where "things should have worked but didn't" — including bugs, configuration traps, environment issues, and integration failures; The knowledge track records findings that "should be the default approach going forward" — including best practices, workflow improvements, and reusable patterns. Trigger scenarios: Proactively prompt at the end of feature-acceptance or issue-fix workflows, or when the user mentions phrases like "document knowledge", "learning", "document learnings", or "record this experience". Spec documents record what was done, while learning documents record what pitfalls were encountered / what was learned — they complement each other and are not interchangeable.
AWS CloudFormation patterns for ECS clusters, services, and task definitions. Use when creating ECS infrastructure with CloudFormation, configuring container definitions, scaling policies, service discovery, load balancing integration, and implementing template structure with Parameters, Outputs, Mappings, Conditions, cross-stack references, and blue/green deployments with CodeDeploy.
Refactor PyTorch code to improve maintainability, readability, and adherence to best practices. Identifies and fixes DRY violations, long functions, deep nesting, SRP violations, and opportunities for modular components. Applies PyTorch 2.x patterns including torch.compile optimization, Automatic Mixed Precision (AMP), optimized DataLoader configuration, modular nn.Module design, gradient checkpointing, CUDA memory management, PyTorch Lightning integration, custom Dataset classes, model factory patterns, weight initialization, and reproducibility patterns.