Skills
97,222 packaged capabilities and playbooks for agents. Install any of them with one command.
Development
Use this skill when analyzing technical debt in a codebase, documenting code quality issues, creating technical debt registers, or assessing code maintainability. Use it to identify code smells, architectural issues, dependency problems, missing documentation, security vulnerabilities, and create comprehensive technical debt documentation
Guides the agent through Vue-specific patterns for Capacitor app development. Covers Vue 3 Composition API with Capacitor plugins, custom composables for native features, reactive plugin state, lifecycle hook patterns, Vue Router deep link integration, platform detection, PWA Elements setup, Quasar Framework integration, and Nuxt integration. Do not use for creating a new Capacitor app from scratch, upgrading Capacitor versions, installing specific plugins, Ionic Framework with Vue setup, or non-Vue frameworks.
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
Quantizes LLMs to 8-bit or 4-bit for 50-75% memory reduction with minimal accuracy loss. Use when GPU memory is limited, need to fit larger models, or want faster inference. Supports INT8, NF4, FP4 formats, QLoRA training, and 8-bit optimizers. Works with HuggingFace Transformers.
Generate a knowledge-learning website for any field with a single sentence. AI automatically understands the topic, creates content, generates pages, and deploys the site. Suitable for any field that requires systematic learning, such as evolutionary psychology, LLM terminology, chemical elements, and historical events.
Configures trace spans, defines custom metrics, sets up log exporters, and optimizes sampling strategies for OpenTelemetry instrumentation. Use when instrumenting applications with traces, metrics, or logs. Triggers on requests for observability, telemetry, tracing, metrics collection, logging integration, or OTel setup.
Product changelog and release notes that users actually read. Covers categorization, user-facing language, visuals, and distribution. Use for: release notes, changelogs, product updates, feature announcements, versioning. Triggers: changelog, release notes, product update, version notes, what's new, feature announcement, product changelog, update log, release announcement, version release, product release, ship notes
Create and validate OmniStudio FlexCards with 130-point scoring. Use when building at-a-glance UI cards, configuring data source bindings to Integration Procedures, or reviewing existing FlexCard definitions for accessibility and performance. TRIGGER when: the user creates FlexCards, configures data sources, designs card layouts, or asks about OmniUiCard metadata. DO NOT TRIGGER when: building OmniScripts (use sf-industry-commoncore-omniscript), creating Integration Procedures (use sf-industry-commoncore-integration-procedure), or analyzing dependencies (use sf-industry-commoncore-omnistudio-analyze).
High-level PyTorch framework with Trainer class, automatic distributed training (DDP/FSDP/DeepSpeed), callbacks system, and minimal boilerplate. Scales from laptop to supercomputer with same code. Use when you want clean training loops with built-in best practices.
Guides the agent through migrating an existing Ionic Appflow project to Capawesome Cloud. Imports an Ionic Appflow organization export with the Capawesome CLI apps:import command — apps, signing certificates, environments, live update channels, native configurations, store destinations, build automations, and repository links — then covers the remaining work: swapping the Live Update SDK (@capacitor/live-updates or cordova-plugin-ionic), mapping configuration options, completing the manual follow-ups, updating CI/CD pipelines, and verifying builds, live updates, and store deployments. Includes a manual fallback for teams without Appflow organization admin access. Pure Cordova apps use @capawesome/cordova-live-update. Do not use for setting up Capawesome Cloud from scratch without an existing Appflow project, for non-Capacitor mobile frameworks, or for migrating Ionic Enterprise plugins (Auth Connect, Identity Vault, Secure Storage) — use the ionic-enterprise-sdk-migration skill instead
Port an existing Remotion (React) composition to HyperFrames HTML. Use ONLY when the user explicitly asks to port/convert/migrate/translate a Remotion source. Do NOT use: (a) authoring a new HyperFrames composition; (b) Remotion mentioned in passing; (c) Remotion code shared as reference only; (d) "same video as my Remotion one" without an explicit migrate request — treat as fresh build. In doubt → `/general-video`. One-way, Remotion-only: no reverse export (HyperFrames→Remotion or any framework), no non-Remotion source (After Effects, Framer Motion, plain React/CSS) → out of scope, re-create via `/general-video`. Flags unsupported patterns (useState, useEffect, async calculateMetadata, third-party React libs, `@remotion/lambda`) and recommends runtime interop over lossy translation. Unsure whether to port vs. build fresh, or only a passing Remotion mention? → /hyperframes.
When the user wants to choose or optimize rendering strategy for SEO. Also use when the user mentions "SSR," "SSG," "CSR," "ISR," "static rendering," "dynamic rendering," "server-side rendering," "client-side rendering," "JavaScript rendering," "pre-rendering," "prerender," "content in initial HTML," or "crawler visibility." For crawl issues, use site-crawlability.
Generate database seed scripts with realistic sample data. Reads Drizzle schemas or SQL migrations, respects foreign key ordering, and produces idempotent TypeScript or SQL seed files. Handles D1 batch limits, unique constraints, and domain-appropriate data. Use when populating dev/demo/test databases. Triggers: 'seed database', 'seed data', 'sample data', 'populate database', 'db seed', 'test data', 'demo data', 'generate fixtures'
A collection of skills for architecting and implementing production-ready code using Google Maps Platform APIs and SDKs for any map, place, address, geocoding, routing/ETA (including eco-friendly routing), nearby search, 3D / Street View / static map, marker clustering, custom styling, drawing, geofencing, heatmap, or environmental (air quality / pollen / solar / weather) feature — across Web, Android, iOS, and Web Services APIs. For prototyping, use the public Maps Demo Key — no billing setup and no Cloud project required, covering a growing set of the most popular Google Maps Platform APIs. For production, the skill prompts you to create and restrict your own key. All non-trivial code is grounded in freshly retrieved docs via the Google Maps Platform Code Assist service (no reliance on training-data memory).
Half-Quadratic Quantization for LLMs without calibration data. Use when quantizing models to 4/3/2-bit precision without needing calibration datasets, for fast quantization workflows, or when deploying with vLLM or HuggingFace Transformers
This skill should be used when the user asks to "add MCP App support to my web app", "turn my web app into a hybrid MCP App", "make my web page work as an MCP App too", "wrap my existing UI as an MCP App", "convert iframe embed to MCP App", "turn my SPA into an MCP App", or needs to add MCP App support to an existing web application while keeping it working standalone. Provides guidance for analyzing existing web apps and creating a hybrid web + MCP App with server-side tool and resource registration.
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.
OmniStudio Integration Procedure creation and validation with 110-point scoring. Use when building server-side process orchestrations that combine Data Mapper actions, Apex Remote Actions, HTTP callouts, and conditional logic. TRIGGER when: user creates Integration Procedures, adds Data Mapper steps, configures Remote Actions, or reviews existing IP configurations. DO NOT TRIGGER when: building OmniScripts (use sf-industry-commoncore-omniscript), creating Data Mappers directly (use sf-industry-commoncore-datamapper), or analyzing cross-component dependencies (use sf-industry-commoncore-omnistudio-analyze).
Integrate Circle Gateway to hold a unified USDC balance across multiple blockchains and transfer USDC instantly (<500ms) via permissionless deposit, burn, and mint workflows. Available on 11 EVM chains + Solana (mainnet and testnet), plus Arc (mainnet and testnet). Use when: enabling chain-agnostic user experiences, low-latency or instant next-block finality is required, capital needs to be pooled across chains for greater capital efficiency, or building apps with consolidated crosschain balances. Triggers on: Gateway, Gateway Wallet, Gateway Minter, gatewayMint, burn intent, unified balance, instant crosschain transfer.
Use when configuring Django to store static and media files on AWS S3 with django-storages. Invoke when working with the STORAGES setting, S3 buckets, presigned URLs, CloudFront, or boto3-backed file storage in settings.py. Configures the Django 4.2+ STORAGES dict, public/private custom backends, presigned GET/POST URLs, IAM policies, and S3 mocking for tests. Trigger terms: django-storages, S3, boto3, S3Boto3Storage, STORAGES, presigned URL, CloudFront, media files, collectstatic, AWS_STORAGE_BUCKET_NAME.
Fast tokenizers optimized for research and production. Rust-based implementation tokenizes 1GB in <20 seconds. Supports BPE, WordPiece, and Unigram algorithms. Train custom vocabularies, track alignments, handle padding/truncation. Integrates seamlessly with transformers. Use when you need high-performance tokenization or custom tokenizer training.
Framework for state-of-the-art sentence, text, and image embeddings. Provides 5000+ pre-trained models for semantic similarity, clustering, and retrieval. Supports multilingual, domain-specific, and multimodal models. Use for generating embeddings for RAG, semantic search, or similarity tasks. Best for production embedding generation.
Choose and implement the right Circle wallet type for your application. Compares developer-controlled, user-controlled, and modular (passkey) wallets across custody model, key management, account types, blockchain support, and use cases. Use whenever blockchain wallet integrations are required for onchain application development. Triggers on: which wallet, choose wallet, wallet comparison, EOA vs SCA vs Modular Wallet, custody model, programmable wallets.
Activation-aware weight quantization for 4-bit LLM compression with 3x speedup and minimal accuracy loss. Use when deploying large models (7B-70B) on limited GPU memory, when you need faster inference than GPTQ with better accuracy preservation, or for instruction-tuned and multimodal models. MLSys 2024 Best Paper Award winner.
Integrates local AI capabilities into applications using Embeddable Lemonade. Use when the user wants to add local AI, offline AI, private AI, on-device AI, a local LLM, local chat, embeddings, image generation, speech-to-text, or text-to-speech to an existing app; replace or supplement OpenAI, Anthropic, Ollama, or other cloud AI APIs with a local backend; only use to convert user apps. Do not use when the user just wants the agent itself to generate images, transcribe, or speak locally in the current workspace, even to cut their own API bill.
Extend context windows of transformer models using RoPE, YaRN, ALiBi, and position interpolation techniques. Use when processing long documents (32k-128k+ tokens), extending pre-trained models beyond original context limits, or implementing efficient positional encodings. Covers rotary embeddings, attention biases, interpolation methods, and extrapolation strategies for LLMs.
Adds PyTorch FSDP2 (fully_shard) to training scripts with correct init, sharding, mixed precision/offload config, and distributed checkpointing. Use when models exceed single-GPU memory or when you need DTensor-based sharding with DeviceMesh.
OpenAI's model connecting vision and language. Enables zero-shot image classification, image-text matching, and cross-modal retrieval. Trained on 400M image-text pairs. Use for image search, content moderation, or vision-language tasks without fine-tuning. Best for general-purpose image understanding.
Clarify ambiguous requirements through focused dialogue before implementation. Use when requirements are unclear, features are complex (>2 days), or involve cross-team coordination. Ask two core questions - Why? (YAGNI check) and Simpler? (KISS check) - to ensure clarity before coding.
Build crypto wallets using Circle Modular Wallets SDK with passkey authentication, gasless transactions, and extensible module architecture. Use when: creating crypto wallets with passkey-based (WebAuthn) registration and login, sending gasless transactions using Circle Gas Station paymaster, batching multiple transactions into a single user operation, implementing passkey recovery using BIP-39 mnemonic phrases, building advanced onchain wallets with custom modules (multisig, subscriptions, session keys). Triggers on: MSCA, passkey authentication, WebAuthn, paymaster, Gas Station, ERC-4337, ERC-6900, toCircleSmartAccount, toModularTransport, sendUserOperation, 2D nonce, passkey recovery, EIP-1193 provider.
Turns a dbt Core project into an Airflow DAG/TaskGroup using Astronomer Cosmos. Use when turning a dbt Core project into an Airflow DAG or TaskGroup with Astronomer Cosmos. Before implementing, verify the dbt engine, warehouse, Airflow version, execution environment, DAG vs. TaskGroup, and manifest availability.
Educational GPT implementation in ~300 lines. Reproduces GPT-2 (124M) on OpenWebText. Clean, hackable code for learning transformers. By Andrej Karpathy. Perfect for understanding GPT architecture from scratch. Train on Shakespeare (CPU) or OpenWebText (multi-GPU).
Tailwind CSS patterns and conventions. Use when writing responsive designs, implementing dark mode, creating reusable component styles, configuring Tailwind, or migrating from v3 to v4. Triggers on tasks involving Tailwind classes, responsive design, dark mode, CSS styling, or "migrate to Tailwind v4".
Designs and fixes CI/CD pipelines for Capacitor apps - GitHub Actions, GitLab CI, Fastlane, or Capgo Cloud Build (npx @capgo/cli@latest build request) - covering web build and tests, cap sync, iOS archive/export/upload with App Store Connect API keys, Android AAB signing and Play upload, Capgo live-update uploads, secrets, caching, and build-number bumping. Use when choosing runner images and toolchains (Node 22/24, macos-26 vs xcode-27 runners, JDK 21, Gradle 9 / AGP 9 for Capacitor 9), or when CI fails with errors like "No signing certificate", "requires a provisioning profile", "Unsupported class file major version", "Android Gradle plugin requires Java 17", "The engine node is incompatible", or "Could not find the web assets directory". Do not use for one-off manual store submission (capacitor-app-store), Capgo Cloud Build credential onboarding in depth (capgo-native-builds), Capgo channel/release strategy (capgo-release-workflows), or test authoring (capacitor-testing)
Train Mixture of Experts (MoE) models using DeepSpeed or HuggingFace. Use when training large-scale models with limited compute (5× cost reduction vs dense models), implementing sparse architectures like Mixtral 8x7B or DeepSeek-V3, or scaling model capacity without proportional compute increase. Covers MoE architectures, routing mechanisms, load balancing, expert parallelism, and inference optimization.
Provides guidance for mechanistic interpretability research using TransformerLens to inspect and manipulate transformer internals via HookPoints and activation caching. Use when reverse-engineering model algorithms, studying attention patterns, or performing activation patching experiments.
Configuration reference for domestic Git platforms—SSH/HTTPS/credentials/CI integration differences and mirror synchronization for Gitee, Coding.net, 极狐 GitLab, and CNB. Invoke only when the user explicitly says /chinese-git-workflow; do not trigger automatically based on context.
Provides PyTorch-native distributed LLM pretraining using torchtitan with 4D parallelism (FSDP2, TP, PP, CP). Use when pretraining Llama 3.1, DeepSeek V3, or custom models at scale from 8 to 512+ GPUs with Float8, torch.compile, and distributed checkpointing.
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.