3,111 artifacts
Skills, MCPs, agents, and plugins. Search to find fast, or page through the catalog.
Conduct preliminary research on a topic and generate research outline. For academic research, benchmark research, technology selection, etc.
Delegate coding, code review, analysis, and research jobs to Google Antigravity CLI (agy) sub-agents that run alongside your own work. Use this whenever the user asks to spin off, offload, or delegate a task to Antigravity, agy, Gemini, or an "external agent"; wants a second opinion or independent review from a different model; wants intensive repo work (audits, large refactors, research sweeps) run in the background while you keep working; or says things like "have Antigravity do it", "spin up a sub-agent for this", or "get more done in parallel". Also use it proactively when a task is a good fit for parallel delegation and the user has expressed a preference for using Antigravity workers.
Accessibility patterns for WCAG 2.2 compliance, keyboard focus management, React Aria component patterns, cognitive inclusion, native HTML-first philosophy, and user preference honoring. Use when implementing screen reader support, keyboard navigation, ARIA patterns, focus traps, accessible component libraries, reduced motion, or cognitive accessibility.
AI-assisted UI generation patterns for json-render, v0.app, Google Stitch, Bolt Cloud, and Cursor workflows. Covers prompt engineering for component and full-stack app generation, review checklists for AI-generated code, design token injection, refactoring for design system conformance, and CI gates for quality assurance. Use when generating UI components with AI tools, rendering multi-surface MCP visual output, reviewing AI-generated code, or integrating AI output into design systems.
Animation and motion design patterns using Motion library (formerly Framer Motion) and View Transitions API. Use when implementing component animations, page transitions, micro-interactions, gesture-driven UIs, or ensuring motion accessibility with prefers-reduced-motion.
ASCII diagram patterns for architecture, workflows, file trees, and data visualizations. Use when creating terminal-rendered diagrams, box-drawing layouts, progress bars, swimlanes, or blast radius visualizations.
Assesses and rates quality 0-10 across multiple dimensions (correctness, maintainability, security, performance, testability, simplicity) with pros/cons analysis. Compares against project conventions and prior decisions from memory. Produces structured evaluation reports with actionable improvement suggestions. Use when evaluating code, designs, architectures, or comparing alternative approaches.
Async job processing patterns for background tasks, Celery workflows, task scheduling, retry strategies, and distributed task execution. Use when implementing background job processing, task queues, or scheduled task systems.
Audits OrchestKit sub-agent activation from real spawn telemetry — computes the generic-vs-specialist spawn split, flags dormant agents (never fired), and classifies each as fires/mis-triggered/niche. The agent-side analogue of audit-skills. Use when specialized agents feel under-used, before pruning the catalog, or after wiring new agent spawn paths.
Single-pass codebase analysis leveraging Opus 4.8 1M context for comprehensive security scanning, architecture review, and dependency auditing. Loads entire codebases for cross-file pattern detection and generates structured audit reports with severity-ranked findings. Use when you need whole-project analysis before releases or security reviews.
Audits all OrchestKit skills for quality, completeness, and compliance with authoring standards. Use when checking skill health, before releases, or after bulk skill edits to surface SKILL.md files that are too long, have missing frontmatter, lack rules/references, or are unregistered in manifests.
Intent-classified router — the front door to OrchestKit and the DEFAULT entry point for any goal-shaped request. Takes a plain-English goal, classifies it into one intent category, and routes to the right specialist skill (/ork:fix-issue, /ork:cover, /ork:brainstorm, /ork:implement, /ork:review-pr, /ork:verify, a /goal optimization loop, or the skill-evolution gate). A goal that maps unambiguously to one skill short-circuits straight to it — routing is never overhead, so use it even when you think you know the target skill. Skip only when already executing inside another skill (no recursion). Triggers on: auto, do this, figure out, just make, get it to, I want, help me, fix, build, improve, any goal description.
Run isolated eval and grading calls using CC 2.1.81 --bare mode. Constructs claude -p --bare invocations for skill evaluation, trigger testing, and LLM grading without plugin/hook interference. Use when running eval pipelines, grading skill outputs, benchmarking prompt quality, or testing trigger accuracy in isolation.
OrchestKit security wrapper for browser automation. Adds URL blocklisting, rate limiting, robots.txt enforcement, and ethical scraping guardrails on top of the upstream agent-browser skill. Use when automating browser workflows that need safety guardrails.
Business case analysis with ROI, NPV, IRR, payback period, and TCO calculations for investment decisions. Use when building financial justification, cost-benefit analysis, build-vs-buy comparisons, or sensitivity analysis.
Chain patterns for CC 2.1.71 pipelines — MCP detection, handoff files, checkpoint-resume, worktree agents, CronCreate monitoring. Use when building multi-phase pipeline skills. Loaded via skills: field by pipeline skills (fix-issue, implement, brainstorm, verify). Not user-invocable.
Rate-limit-resilient pipeline with checkpoint/resume for long multi-phase sessions. Saves progress to .claude/pipeline-state.json after each phase. Use when starting a complex multi-phase task that risks hitting rate limits, when resuming an interrupted session, or when orchestrating work spanning commits, GitHub issues, and large file changes.
Diagnose a failing CI run against an 11-pattern playbook. Classifies the failure, cites the relevant memory entry, proposes the exact fix command — but NEVER applies without explicit user approval. Use when a specific PR check or GitHub Actions run failed and you want a diagnosis instead of speculation. Don't use for org-wide CI sweeps (that's /status) or for app-level test failures (the playbook is CI-infra-specific).
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Apply iPolloWork's modern, minimal-change engineering standards when adding features, fixing bugs, refactoring, changing UI, or upgrading dependencies. Use for every implementation task in the iPolloWork repository.
Agent-first screenshots — an agent drives the real app via CDP and produces clean, defect-free product screenshots (newsletters, landing pages, social, decks, PR). Dual-channel verification (DOM + pixels + vision) in a capture loop. Use for any "take/redo screenshots of the app" task.
Local iPolloWork Electron browser automation with CDP. Use when driving a local Electron dev app, browser_list, browser_snapshot, browser_eval, composer automation, or local UI smoke tests.
Create an OpenCode plugin for iPolloWork. Scaffolds the plugin file with the correct API shape, tool definitions, and hook registration. Use when the user asks to 'create a plugin', 'write a plugin', or 'make a plugin that does X'.
Launch and control standalone Chrome in a Daytona sandbox via CDP. Use for web sign-in, OAuth, Den Web setup, browser-only flows, or when the app should not be driven through Electron CDP.
Daytona cloud server, Den sandbox, desktop plus cloud e2e, marketplace server, worker proxy, cloud auth, org policies, connect Electron to Den. Use for server-side setup in validated flows.
Daytona development environment overview. Use when the user asks about Daytona setup, Daytona toolbox, dev environment, noVNC, CDP, server sandbox, secrets volume, Electron sandbox, standalone Chrome, validation, or artifacts volume.
Electron and Den, desktop plus cloud, two-sandbox e2e, cloud auth, marketplace, org policy, worker proxy, provider sync, desktop handoff. Validate Electron against a Daytona Den server with unified proof.
do e2e tests, validate feature, prove it works, pass/fail, frame proof, screenshots, CDP assertions. Daytona validation loop for real app behavior with repair before declaring success.
frame proof, HTML frames, screenshots, recording, PR proof, e2e evidence, validate visually. Daytona artifacts workflow for validated screenshots and optional videos.
test on Windows, enterprise CA, corporate certificate, GPO cert, TLS fetch failed, Windows sandbox, daytona windows, self-hosted cert. Use when validating iPolloWork Windows enterprise TLS/OS-trust fixes in a Daytona Windows sandbox.
create a fraimz, make fraimz, prove it works, frame proof, PR proof, validate experience, e2e evidence, fraimz.html. The full fraimz loop — frame the claim, drive the real app via CDP, validate/repair, output fraimz.html. Use whenever a task ends with "please create a fraimz" or any change needs end-to-end proof.
get an env var, fetch a secret, missing env var, missing token/API key, load secrets from Infisical, infisical. Fetch secrets from the team's Infisical workspace into the shell environment so subsequent commands can use them.
do e2e tests, run e2e, validate feature, prove it works, PR proof, frame proof, pnpm evals. Launches iPolloWork on Daytona or local Electron and runs the coded eval flows via CDP. Launch + run mechanics; the proof loop itself is the fraimz skill.
Manages shadcn components and projects — adding, searching, fixing, debugging, styling, and composing UI. Provides project context, component docs, and usage examples. Applies when working with shadcn/ui, component registries, presets, --preset codes, or any project with a components.json file. Also triggers for "shadcn init", "create an app with --preset", or "switch to --preset".
upload a photo/image/screenshot, host an image, get a public image URL, put images on Vercel Blob, embed images in a PR/comment/doc. Upload local images to Vercel Blob and print public URLs.
write the voice-over, demo script first, voiceover instead of PRD, voiceover-first development, align on the demo, script the demo, ship a feature demo-first. The whole demo-driven journey — approve the narration BEFORE any code, then build on a fresh worktree until the demo holds and open the PR with the proof on it. Use when a feature request arrives, or when the user runs /voiceover.
Use when: the user asks to make or render charts with flint-chart, visualize tabular data, generate a ChartAssemblyInput, validate/render through MCP, or add Flint to a JS/TS project. Author the semantic spec, transform data before Flint when needed, install/import Flint only when executable code is needed, and reserve backend-specific style tweaks for after compiling from Flint.
Framework for building LLM-powered applications with agents, chains, and RAG. Supports multiple providers (OpenAI, Anthropic, Google), 500+ integrations, ReAct agents, tool calling, memory management, and vector store retrieval. Use for building chatbots, question-answering systems, autonomous agents, or RAG applications. Best for rapid prototyping and production deployments.
Multi-agent orchestration framework for autonomous AI collaboration. Use when building teams of specialized agents working together on complex tasks, when you need role-based agent collaboration with memory, or for production workflows requiring sequential/hierarchical execution. Built without LangChain dependencies for lean, fast execution.
Autonomous AI agent platform for building and deploying continuous agents. Use when creating visual workflow agents, deploying persistent autonomous agents, or building complex multi-step AI automation systems.
Provides guidance for automatically evolving and optimizing AI agents across any domain using LLM-driven evolution algorithms. Use when building self-improving agents, optimizing agent prompts and skills against benchmarks, or implementing automated agent evaluation loops.
Track ML experiments with automatic logging, visualize training in real-time, optimize hyperparameters with sweeps, and manage model registry with W&B - collaborative MLOps platform
Visualize training metrics, debug models with histograms, compare experiments, visualize model graphs, and profile performance with TensorBoard - Google's ML visualization toolkit
Provides guidance for experiment tracking with SwanLab. Use when you need open-source run tracking, local or self-hosted dashboards, and lightweight media logging for ML workflows.
Track ML experiments, manage model registry with versioning, deploy models to production, and reproduce experiments with MLflow - framework-agnostic ML lifecycle platform
Serves LLMs with high throughput using vLLM's PagedAttention and continuous batching. Use when deploying production LLM APIs, optimizing inference latency/throughput, or serving models with limited GPU memory. Supports OpenAI-compatible endpoints, quantization (GPTQ/AWQ/FP8), and tensor parallelism.
Optimizes LLM inference with NVIDIA TensorRT for maximum throughput and lowest latency. Use for production deployment on NVIDIA GPUs (A100/H100), when you need 10-100x faster inference than PyTorch, or for serving models with quantization (FP8/INT4), in-flight batching, and multi-GPU scaling.
Fast structured generation and serving for LLMs with RadixAttention prefix caching. Use for JSON/regex outputs, constrained decoding, agentic workflows with tool calls, or when you need 5× faster inference than vLLM with prefix sharing. Powers 300,000+ GPUs at xAI, AMD, NVIDIA, and LinkedIn.