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Found 2,163 Skills
Improve Coval trace quality after basic ingestion works. Use when traces are sparse, missing useful STT/LLM/TTS/tool spans, missing attributes needed for Coval built-in metrics, or when a customer wants maximum debugging and observability value from agent traces.
Build high-quality Agent Skills for Claude following official Anthropic best practices. Covers SKILL.md structure, frontmatter, description writing, progressive disclosure, testing, patterns, troubleshooting, and distribution across all surfaces (Claude.ai, Claude Code, API, Agent SDK). Use when creating new skills, reviewing skill quality, debugging skill triggering, structuring skill directories, writing skill descriptions, or improving existing skills. Triggers on "build a skill", "create a skill", "skill structure", "SKILL.md", "skill best practices", "skill not triggering", "skill quality".
Blockbench plugin/extension development for the 3D modeling tool. Use when creating, modifying, or debugging JavaScript plugins for Blockbench including actions, dialogs, panels, menus, toolbars, model manipulation, animation APIs, and custom formats/codecs. Triggers on Blockbench plugin, Blockbench extension, Blockbench API, BBPlugin, model editor plugin, or 3D modeling tool extension.
Protocol analysis skill for serial bus debugging. Use when decoding I2C/SPI/UART with logic analyzer concepts, sigrok/PulseView, or Python capture scripts. Activates on queries about logic analyzer, sigrok, PulseView, decode I2C SPI UART, or bus protocol capture.
Onboard an application into Elastic Observability with the Elastic Distribution of OpenTelemetry (EDOT): route on language and runtime, detect and replace a classic Elastic APM agent, apply the required OTLP configuration, and then verify with ES|QL that traces, metrics, and logs actually arrive under the expected service name. Use when adding observability to a service, migrating off the classic Elastic APM agent, or debugging why an instrumented service is not showing up in Elastic.
Use when authoring or debugging Meteor methods (Meteor.methods, Meteor.call, Meteor.callAsync). Triggers on argument validation with check(), optimistic UI stubs, latency compensation, Meteor.Error handling, and DDPRateLimiter. Use this skill when the user asks about server-side mutation, asks about rate limiting RPC, or asks about wrapping a method with auth checks.
Use when writing, running, configuring, reviewing, or debugging code in a Deno project, or when scaffolding a new one. Covers dependency management with deno install and deno add, package.json and node_modules support, npm and JSR packages, permissions, where configuration belongs across package.json, tsconfig.json and deno.json, workspaces, the built-in toolchain (fmt, lint, test, check, bench, compile), and publishing.
Bootstrap and toolchain guidance for three.js applications using npm, Vite/Webpack/Rollup, bare ESM import maps, and TypeScript. Covers canonical import paths for `three` core versus `three/addons/` (examples/jsm re-exports), version alignment with https://threejs.org/docs/, and fixing "module not found" for loaders and controls. Use when scaffolding a new 3D project, migrating bundler, or debugging resolution of addons—do not use for rendering API details (see threejs-renderers) or asset loading logic (see threejs-loaders).
Highest-priority for all Next.js-related tasks involving full-stack web development. Use this skill whenever writing, reviewing, refactoring, debugging, or planning Next.js code using App Router, Server Components, Client Components, API Routes, Server Actions, data fetching, caching, revalidation, routing, authentication, or Next.js tests. Prefer this skill over generic React or Node.js advice for Next.js code.
Optimize Apache Spark jobs with partitioning, caching, shuffle optimization, and memory tuning. Use when improving Spark performance, debugging slow jobs, or scaling data processing pipelines.
Design effective Claude Code skills with optimal descriptions, progressive disclosure, and error prevention patterns. Covers freedom levels, token efficiency, and quality standards. Use when: creating new skills, improving skill descriptions, optimizing token usage, structuring skill content, or debugging why skills aren't being discovered.
Use when implementing Liquid Glass effects, reviewing UI for Liquid Glass adoption, debugging visual artifacts, optimizing performance, or requesting expert review of Liquid Glass implementation - provides comprehensive design principles, API patterns, and troubleshooting guidance from WWDC 2025. Includes design review pressure handling and professional push-back frameworks