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Found 2,806 Skills
Use the Helmor CLI to remote-control Helmor from the terminal. Use when the user asks to inspect Helmor data/settings, manage repositories/workspaces/sessions/files, send prompts to agents, list models, use GitHub integration, inspect scripts, migrate from Conductor, run Helmor as an MCP server, generate shell completions, quit a running app, check/install/update the Helmor CLI beta, install/update Helmor skills through the beta app flow, or needs the Helmor command reference.
Multi-agent collaboration plugin that spawns N parallel subagents competing on the same task via git worktree isolation. Agents work independently, results are evaluated by metric or LLM judge, and the best branch is merged. Use when: user wants multiple approaches tried in parallel — code optimization, content variation, research exploration, or any task that benefits from parallel competition. Requires: a git repo.
Troubleshoot Coval OpenTelemetry trace ingestion, missing trace UI, sparse traces, bad simulation or conversation correlation, auth/org errors, oversized payloads, duplicate spans, and production debugging with Trace Search.
Brev instance operating guidance for NeMo-RL agents working in /home/ubuntu/RL with limited workspace disk, a larger /ephemeral volume, and optional /home/ubuntu/RL/.env secrets. Use when running nemo-rl-auto-research campaigns, experiments, training jobs, model or dataset downloads, shared cache-heavy commands, log-producing runs, checkpoint generation, W&B or Hugging Face authenticated workflows, or any workflow that may create large files on Brev.
Read Figma design files, nodes, rendered images and comments via the Figma REST API. Use when the user mentions Figma, a figma.com file link, implementing a design as code, extracting design tokens / colors / spacing, or summarizing comments on a design.
Pull a Korean brand's published design.md from the ko-design-md catalog (getdesign.kr) and apply its design language — colors, typography, spacing, radius, components, do's & don'ts — to the UI you are building in the CURRENT project. Use this skill whenever the user wants to build or restyle UI in the *style of* a catalogued Korean service — phrases like "토스 디자인으로 만들어줘", "당근 스타일로 이 화면 다시 꾸며줘", "getdesign 카탈로그에서 배민 디자인 가져와서 적용", "KRDS 톤으로 폼 잡아줘", "make this look like Toss", "use the Karrot design system here", or "/use-design-md". Works in ANY repository — it fetches over the network, no local catalog needed. Do NOT use this to ADD a brand to the catalog or edit catalog entries — that is the separate `design-md` producer skill, which only runs inside the ko-design-md repo. If the requested brand isn't in the catalog, say so plainly rather than inventing a design.md.
Enables Redshift system-table (SYS_*) log publishing to S3 Tables in Apache Iceberg format for both Provisioned clusters and Serverless namespaces, verifies publishing status, and queries the published logs via any Iceberg-compatible engine including Redshift and Athena. Covers system tables such as sys_query_history, sys_query_text, sys_connection_log, sys_query_detail, and sys_session_history. Applies when turning on S3 Tables log publishing for a cluster or namespace, confirming publishing status and locating the S3 Tables namespace, querying non-realtime data from Redshift system tables off-cluster at scale, or building dashboards for Redshift monitoring and auditing, especially for historical or high-volume system-table data beyond the in-cluster SYS_ view retention window. Trigger phrases: publish redshift system table log to s3 tables, enable-logging s3 tables, describe redshift logging status, query redshift system tables in athena or redshift, redshift log exports to iceberg.
This skill should be used at the start of any computationally intensive scientific task to detect and report available system resources (CPU cores, GPUs, memory, disk space). It creates a JSON file with resource information and strategic recommendations that inform computational approach decisions such as whether to use parallel processing (joblib, multiprocessing), out-of-core computing (Dask, Zarr), GPU acceleration (PyTorch, JAX), or memory-efficient strategies. Use this skill before running analyses, training models, processing large datasets, or any task where resource constraints matter.
Use this skill when developing browser/Web applications (React/Vue/Angular, static websites, SPAs) that need AI capabilities. Features text generation (generateText) and streaming (streamText) via @cloudbase/js-sdk. Built-in models include Hunyuan (hunyuan-2.0-instruct-20251111 recommended) and DeepSeek (deepseek-v3.2 recommended). NOT for Node.js backend (use ai-model-nodejs), WeChat Mini Program (use ai-model-wechat), or image generation (Node SDK only).
Design and implement multi-cloud strategies spanning AWS, Azure, and GCP with vendor lock-in avoidance, hybrid deployments, and federation.
Async operations and background tasks in GPUI. Use when working with async, spawn, background tasks, or concurrent operations. Essential for handling async I/O, long-running computations, and coordinating between foreground UI updates and background work.
Full-stack web development with Next.js and Turborepo. Stack: Next.js 14+ (App Router, RSC, Server Actions, PPR, SSR, SSG, ISR), Turborepo (monorepo, pipelines, remote caching), RemixIcon (3100+ icons). Capabilities: server components, API routes, middleware, caching strategies, build optimization, monorepo management. Actions: create, build, deploy, optimize Next.js apps, setup monorepo, configure caching. Keywords: Next.js, App Router, Server Components, RSC, Server Actions, SSR, SSG, ISR, PPR, Turborepo, monorepo, remote cache, build pipeline, parallel execution, workspace, pnpm, icons. Use when: building Next.js apps, implementing SSR/SSG, setting up monorepos, optimizing build performance, configuring caching strategies, managing shared dependencies.