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Found 2,356 Skills
Interpret Apache Doris query runtime profiles, especially profile bottleneck triage, misleading wait counters, per-operator metric priority, scan, join-order/runtime-filter analysis, and evidence-bounded performance explanations. Use when given a Doris profile, query id, profile URL/text, or a request to explain Doris query performance.
Use this skill when the user wants to analyze social media performance, content metrics, engagement quality, channel ROI, post patterns, audience response, reporting, or what to post more or less of. Trigger phrases include "social media analysis," "analyze my social posts," "social ROI," "content performance," "engagement analysis," "post metrics," "social media report," and "what worked on social."
Guides development of Fastify Node.js backend servers and REST APIs using TypeScript or JavaScript. Use when building, configuring, or debugging a Fastify application — including defining routes, implementing plugins, setting up JSON Schema validation, handling errors, optimising performance, managing authentication, configuring CORS and security headers, integrating databases, working with WebSockets, and deploying to production. Covers the full Fastify request lifecycle (hooks, serialization, logging with Pino) and TypeScript integration via strip types. Trigger terms: Fastify, Node.js server, REST API, API routes, backend framework, fastify.config, server.ts, app.ts.
Replay a HAR file as a mock backend to reproduce frontend performance issues with production data. Use when asked to replay a HAR file, reproduce a dashboard with a HAR, or test frontend performance with captured traffic.
Find and fix game performance problems methodically — measure with the engine profiler first, reason about the frame-time budget, locate the CPU-vs-GPU bottleneck, then apply the right fix: object pooling, draw-call batching, fewer allocations/GC spikes, and asset budgets. Engine- neutral method that pairs with each engine's profiler. Use when the user mentions performance, optimize, low/dropping FPS, frame drops, stutter, lag, profiler, frame budget, draw calls, batching, garbage collection/GC spikes, object pooling, or "the game runs slow".
Routes any game-development request to the right specialized skill(s): it detects the engine (Godot, Unity, Unreal, Bevy, Phaser, PixiJS, three.js, LÖVE, pygame, Roblox) and the task, then reads the chosen skill before acting. Use to make a game or to decide which skill applies — for players, levels, enemies, shaders, UI/UX, cameras, game feel, physics, input, audio, saving, multiplayer, AI, dialogue, procedural generation, or performance, for genres (platformer, roguelike, RPG, FPS, tower-defense, card game, visual novel, survival-crafting, puzzle), and for shipping (game jam, Steam, itch). Start here when unsure which gamedev skill to use.
Vision-language pre-training framework bridging frozen image encoders and LLMs. Use when you need image captioning, visual question answering, image-text retrieval, or multimodal chat with state-of-the-art zero-shot performance.
Use when automating Instruments profiling, running headless performance analysis, or integrating profiling into CI/CD - comprehensive xctrace CLI reference with record/export patterns
JavaScript micro-optimizations and performance patterns. Use when optimizing loops, array operations, caching, or DOM manipulation. Includes Set/Map usage, early returns, and memory-efficient patterns.
LLM prompt testing, evaluation, and CI/CD quality gates using Promptfoo. Invoke when: - Setting up prompt evaluation or regression testing - Integrating LLM testing into CI/CD pipelines - Configuring security testing (red teaming, jailbreaks) - Comparing prompt or model performance - Building evaluation suites for RAG, factuality, or safety Keywords: promptfoo, llm evaluation, prompt testing, red team, CI/CD, regression testing
Use when conducting comprehensive code review for pull requests across multiple quality dimensions. Orchestrates 12-15 specialized reviewer agents across 4 phases using star topology coordination. Covers automated checks, parallel specialized reviews (quality, security, performance, architecture, documentation), integration analysis, and final merge recommendation in a 4-hour workflow.
This skill should be used when the user asks to "optimize a DSPy program", "use MIPROv2", "tune instructions and demos", "get best DSPy performance", "run Bayesian optimization", mentions "state-of-the-art DSPy optimizer", "joint instruction tuning", or needs maximum performance from a DSPy program with substantial training data (200+ examples).