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Found 6,941 Skills
Reference documentation for live music data APIs and ID mapping between services. Use when integrating MusicBrainz, Setlist.fm, JamBase, Bandsintown, Ticketmaster, or other concert/artist APIs.
Composition and signal integrity specialist - Masters GDScript 2.0, C# integration, node-based architecture, and type-safe signal design for Godot 4 projects
Display advertising and programmatic media buying specialist covering managed placements, Google Display Network, DV360, trade desk platforms, partner media (newsletters, sponsored content), and ABM display strategies via platforms like Demandbase and 6Sense.
Audit Android Jetpack Compose repositories for performance, state management, side effects, and composable API quality. Scans source code, scores each category from 0-10, writes a strict markdown report, and summarizes the most important fixes. Use when reviewing a Compose codebase, rating repository quality, inspecting recomposition/state issues, or running a Compose audit.
Apply PyGraphistry graph ML/AI workflows such as UMAP, DBSCAN, embedding-based anomaly analysis, and fit/transform pipelines on nodes or edges. Use for feature-driven exploration, clustering, anomaly triage, and graph-AI notebook workflows.
Investigate LLM analytics evaluations of both types — `hog` (deterministic code-based) and `llm_judge` (LLM-prompt-based). Find existing evaluations, inspect their configuration, run them against specific generations, query individual pass/fail results, and generate AI-powered summaries of patterns across many runs. Use when the user asks to debug why an evaluation is failing, surface common failure modes, compare results across filters, dry-run a Hog evaluator, prototype a new LLM-judge prompt, or manage the evaluation lifecycle (create, update, enable/disable, delete).
Context-aware translation that preserves tone, style, and natural word order. Use when translating UI strings, documentation, marketing copy, or any multilingual content. Infers register, domain, and style from the source text and surrounding codebase context.
Decompose requirements into structured task lists and build a task management system for long-running Agents (based on the Anthropic Effective harnesses methodology). Automatically trigger when users need to manage multi-session development tasks, track feature completion progress, or request "task decomposition", "task management", or "project planning".
Scan codebases for technical debt, score severity, track trends, and generate prioritized remediation plans. Use when users mention tech debt, code quality, refactoring priority, debt scoring, cleanup sprints, or code health assessment. Also use for legacy code modernization planning and maintenance cost estimation.
Generates complete, production-ready SaaS project boilerplate including authentication, database schemas, billing integration, API routes, and a working dashboard using Next.js 14+ App Router, TypeScript, Tailwind CSS, shadcn/ui, Drizzle ORM, and Stripe. Use when the user wants to create a new SaaS app, start a subscription-based web project, scaffold a Next.js application, or mentions terms like starter template, boilerplate, new project, or wiring up auth and payments.
This skill is used when users explicitly request "LaTeX template optimization", "style parameter alignment", "pixel-level comparison", "make-latex-model" or the old notation "make_latex_model", or when they want to turn a project in ChineseResearchLaTeX into a high-quality template. It supports four product lines: NSFC / paper / thesis / cv; first determine whether to modify the project layer or the public package based on the actual hierarchy of packages/ and projects/, then use the official build entry of each product line for acceptance. If modification to the public packages under packages/ is necessary, a regression plan for affected templates must be generated first and relevant regression completed; the NSFC special tool is only used on demand when it clearly falls into the NSFC parameter alignment scenario.
Store and query vector embeddings using Amazon S3 Vectors, a cost-effective long-term vector storage service with its own API namespace (s3vectors). Triggers on: create S3 vector bucket, vector index, store embeddings, semantic search, RAG vector storage, similarity search, vector database, migrate from other vector databases. Do NOT use for: querying tabular data (use querying-data-lake), S3 object storage, or hundreds/thousands of sustained QPS (use OpenSearch).