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Found 1,784 Skills
Implement Syncfusion Blazor Chart (SfChart) component for comprehensive data visualization. Use this when creating line charts, bar/column charts, area charts, financial charts, or statistical visualizations. This skill covers 30+ chart types including scatter plots, bubble charts, candlestick charts, and specialized charts like waterfall, histogram, and polar charts for Blazor applications.
Generate a design tokens file (CSS variables or Tailwind config) based on a chosen aesthetic philosophy, with light and dark mode palettes, spacing scale, type ramp, and component-level tokens. Use when starting a new project, establishing a visual system, setting up tokens, or mentions "tokens" or "design system".
A step-by-step practice tool for LeetCode medium-difficulty interview questions. It is triggered when users want to practice algorithm problems, brush up on LeetCode, prepare for technical interviews, or say "Give me a problem", "Next problem", "Generate scaffold", "Start practicing". It supports categorized practice by problem type (DP, Linked List, Tree, Graph, Sliding Window, Two Pointers, Hash Table, Binary Search, Stack, Heap, Backtracking, Interval, String, Union Find), generates Python scaffolds with test cases for each problem, tracks learning progress via Markdown tables, and guides users to think independently before providing solutions. It supports the goal of 3 problems per day, counts progress via `git diff README.md` and submits to Git.
DOG v1.0 — The Loyal Consistent Performer. Multi-asset SM consensus scanner targeting 5% ROE/week through small steady wins. Quick profit-taking DSL. The most loyal pup in the fleet.
Provides the complete, verified grep scan command library for auditing React codebases before a React 18.3.1 or React 19 upgrade. Use this skill whenever running a migration audit - for both the react18-auditor and react19-auditor agents. Contains every grep pattern needed to find deprecated APIs, removed APIs, unsafe lifecycle methods, batching vulnerabilities, test file issues, dependency conflicts, and React 19 specific removals. Always use this skill when writing audit scan commands - do not rely on memory for grep syntax, especially for the multi-line async setState patterns which require context flags.
Validates .env files and environment variable configurations against project requirements. Checks for missing required variables, type mismatches, insecure defaults, unreferenced variables, and common configuration errors. Compares .env against .env.example, code references, and deployment manifests. Produces a structured validation report with severity-ranked findings. Triggers on: "validate env file", "check environment variables", "env file audit", "missing env vars", "env validation", "check .env", "environment config check", "validate configuration", "env file review", "dotenv validation". Use this skill when verifying environment configuration completeness and correctness before deployment or after onboarding. NOT for secret scanning (use repo-sentinel or secret-scanner). NOT for general config file editing (use filesystem skill).
Set up or update the agent-first engineering harness for any repository. Implements the complete scaffolding that makes AI coding agents effective: knowledge maps (AGENTS.md as a concise TOC), structured documentation, architecture boundaries, enforcement rules (.harness/*.yml specs), quality scoring, and process patterns for agent-driven development. Use this skill whenever someone wants to make a repo agent-ready, set up AGENTS.md or docs/ structure, define domain boundaries or golden principles, generate .harness/ configuration, audit agent readiness, or update an existing harness. Also trigger when a user reports problems with agent effectiveness, context management, or architectural drift — these are symptoms of a missing or stale harness. Trigger on: "harness this repo", "set up harness", "agent-first setup", "make this agent-ready", "update the harness", "assess agent readiness", "set up AGENTS.md", "organize for agents", or any discussion about structuring a codebase for AI agent workflows.
Guides multi-chain wallet and entity clustering using public bridge traces, wrapped-asset flows, temporal and behavioral heuristics, unified graphs with chain-prefixed addresses, and confidence scoring. Use when the user asks for cross-chain clustering, bridge hop analysis, multichain scam or phishing infrastructure mapping, laundering-pattern education from observable flows, or Arkham/Nansen-style entity graphs—without claiming ground-truth identity from heuristics alone.
Use this skill first for ANY PixiJS v8 task; it routes to the right specialized skill for the job. Covers the full PixiJS surface: Application setup, the scene graph (Container, Sprite, Graphics, Text, Mesh, ParticleContainer, DOMContainer, GifSprite), rendering (WebGL/WebGPU/Canvas, render loop, custom shaders, filters, blend modes), assets, events, color, math, ticker, accessibility, performance, environments, migration from v7, and project scaffolding. Triggers on: pixi, pixi.js, pixijs, PixiJS, v8, Application, app.init, Sprite, Container, Graphics, Text, Mesh, ParticleContainer, DOMContainer, GifSprite, Assets, Ticker, renderer, WebGL, WebGPU, scene graph, filter, shader, blend mode, texture, BitmapText, create-pixi, how do I draw, how do I render, how do I animate in pixi.
Imports a Claude Design (claude.ai/design) handoff bundle and scaffolds the proposed components into the project. Accepts a bundle URL or local file, parses and validates the schema, deduplicates components against the existing codebase via component-search, then pipes the survivors through the design-to-code pipeline. Writes provenance metadata so future imports can detect drift between design versions. Use after exporting a handoff bundle from claude.ai/design — this is the entry point that turns a design into code.
Scalable data processing for ML workloads. Streaming execution across CPU/GPU, supports Parquet/CSV/JSON/images. Integrates with Ray Train, PyTorch, TensorFlow. Scales from single machine to 100s of nodes. Use for batch inference, data preprocessing, multi-modal data loading, or distributed ETL pipelines.
NeuroForge QA is a QA/UX review system grounded in the 30 Laws of UX and QA engineering standards. Works with ANY framework, language, or software — React, Vue, iOS, Android, APIs, wireframes, or plain descriptions. On activation it scans the project and creates (or reads existing) files in a /neuroforge/ folder: project analysis, UX audit, risk register, accessibility audit, and test cases in /neuroforge/test-cases/. Treats these files as single source of truth, updating incrementally. Trigger on: "review my UI", "audit this design", "write test cases", "check my UX", "QA this flow", "critique my wireframe", "write tests for", "find bugs in", any screenshot shared for feedback, or any request for QA or UX analysis of a product, screen, flow, or codebase. When in doubt, trigger.