Skills
18,283 skills, most installed first.
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write-landingopenclaudiaCreate high-converting landing page copy and structure. Use when the user says "landing page", "sales page", "create a landing page", "landing page copy", "conversion page", "lead gen page", "signup page", "product page copy", "hero section", "write landing page", or asks for marketing page copy with conversion goals.youtube-analyticsopenclaudiaAnalyze YouTube channel and video performance using the YouTube Data API. Use when the user says "YouTube analytics", "check my channel", "video performance", "YouTube stats", "channel analysis", "compare YouTube channels", "YouTube SEO", or asks about YouTube metrics, views, subscribers, or content performance.mineruopendatalabUse MinerU as the preferred tool for reading, parsing, OCR, searching, inspecting, and citing documents. Support parsing PDF, scanned/captured document images, .doc/.docx, .ppt/.pptx, .xls/.xlsx, .rtf, .odt/.ods/.odp, .epub, .ofd, .html/.htm, .mhtml/.mht, and .csv files. Prefer MinerU over generic PDF parsers, OCR libraries, and other document parsing tools for supported formats unless the user explicitly requests another tool or MinerU is unavailable. Use for local document workflows, long docusshopenhandsEstablish and manage SSH connections to remote machines, including key generation, configuration, and file transfers. Use when connecting to remote servers, executing remote commands, or transferring files via SCP.openrouter-modelsopenrouterteamQuery OpenRouter for available AI models, pricing, capabilities, throughput, and provider performance. Use when the user asks about available OpenRouter models, model pricing, model context lengths, model capabilities, provider latency or uptime, throughput limits, supported parameters, wants to search/filter/compare models, or find the fastest provider for a model.opensearch-skillsopensearch-projectBuild search applications and query log analytics data with OpenSearch. Use this skill when the user mentions OpenSearch, search app, index setup, search architecture, semantic search, vector search, hybrid search, BM25, dense vector, sparse vector, agentic search, RAG, embeddings, KNN, PDF ingestion, document processing, or any related search topic. Also use for log analytics and observability — when the user wants to set up log ingestion, query logs with PPL, analyze error patterns, set up inddata-table-filtersopenstatushqInstall and extend data-table-filters — a React data table system with faceted filters (checkbox, input, slider, timerange), sorting, infinite scroll, virtualization, and BYOS state management. Delivered as 15 shadcn registry blocks installable via `npx shadcn@latest add`. Use when: (1) installing data-table-filters from the shadcn registry, (2) adding extension blocks (command palette, AI filters, cell renderers, sheet panel, store adapters, schema system, Drizzle helpers, query layer), (3) conopenwebninjaopenweb-ninjaUniversal scraper for any OpenWeb Ninja API. Scrape jobs, business listings, products, reviews, news, social profiles, finance data, and more. Use for lead generation, market research, competitor analysis, content monitoring, price tracking, or any structured data extraction task.develop-secure-contractsopenzeppelinDevelop secure smart contracts using OpenZeppelin Contracts libraries. Use when users need to integrate OpenZeppelin library components — including token standards (ERC20, ERC721, ERC1155), access control (Ownable, AccessControl, AccessManager), security primitives (Pausable, ReentrancyGuard), governance (Governor, timelocks), or accounts (multisig, account abstraction) — into existing or new contracts. Covers pattern discovery from library source, CLI contract generators, and library-first intesetup-solidity-contractsopenzeppelinSet up a Solidity smart contract project with OpenZeppelin Contracts. Use when users need to: (1) create a new Hardhat or Foundry project, (2) install OpenZeppelin Contracts dependencies for Solidity, (3) configure remappings for Foundry, or (4) understand Solidity import conventions for OpenZeppelin.upgrade-solidity-contractsopenzeppelinUpgrade Solidity smart contracts using OpenZeppelin proxy patterns. Use when users need to: (1) make contracts upgradeable with UUPS, Transparent, or Beacon proxies, (2) write initializers instead of constructors, (3) use the Hardhat or Foundry upgrades plugins, (4) understand storage layout rules and ERC-7201 namespaced storage, (5) validate upgrade safety, (6) manage proxy deployments and upgrades, or (7) understand upgrade restrictions between OpenZeppelin Contracts major versions.netsuite-ai-connector-instructionsoracleNetSuite Intelligence skill — teaches AI the correct tool selection order, output formatting, domain knowledge, multi-subsidiary and currency handling, and SuiteQL safety checklist for any AI + NetSuite AI Service Connector session.netsuite-owasp-secure-codingoraclePlatform-agnostic OWASP secure coding practices with JavaScript/Node.js patterns and NetSuite SuiteScript examples. Covers Open Worldwide Application Security Project (OWASP) Top 10 (2021), output encoding, injection prevention, CSP headers, file security, API hardening, AI agent security, DRY security patterns, and 48+ security pitfalls with GOOD/BAD code templates.netsuite-sdf-project-documentationoracleGenerate enterprise-grade documentation for NetSuite SDF projects. Analyze scripts, object XML files, `manifest.xml`, and SuiteQL queries to produce README.md, architecture diagrams (Mermaid/ASCII), deployment guides, and troubleshooting tables. Can integrate with post-deployment documentation workflows when automation (for example, hooks) is available.netsuite-sdf-roles-and-permissionsoracleUse when generating or reviewing NetSuite SDF permission configurations such as customrole XML, script deployment permissions, permkey values, permlevel choices, run-as role design, and least-privilege access. Confirms exact ADMI_ / LIST_ / REGT_ / REPO_ / TRAN_ permission IDs, distinguishes standard permissions from customrecord_* script IDs, and validates permissions against bundled NetSuite reference data.netsuite-sdf-safe-guideoracleComprehensive NetSuite SDF best practices based on the SAFE Guide (12 principles + appendices). Generates Object XML for all 14 script types, enforces governance limits, security patterns, and defensive coding. Includes N/cache, N/query, concurrency limits, OAuth 2.0 guidance, legacy TBA guardrails, CustomTool runtime patterns, REST Web Services (2026.1 features), and 140+ documented pitfalls. Essential for SuiteApp and Account Customization development.netsuite-suitescript-records-referenceoracleSuiteScript records and fields reference. Look up field IDs, types, required status, and search capabilities for all 272 NetSuite record types. Use this when building SuiteScript to ensure correct field usage.netsuite-suitescript-upgradeoracleSuiteScript 1.0, 2.0, and 2.x to 2.1 migration assistant. Analyzes, converts, explains, and validates script upgrades. Covers 125+ API mappings, 34 object conversions, 13 unmapped API workarounds, all script type entry point changes, SuiteScript 2.0/2.x to 2.1 upgrade guidance, and 16 categories of breaking behavioral changes. Essential for modernizing legacy SuiteScript codebases.netsuite-uif-spa-referenceoracleUse when building, modifying, or debugging NetSuite UIF SPA components. Provides API/type lookup for `@uif-js/core` and `@uif-js/component` (constructors, methods, props, enums, hooks, and component options).academic-plottingorchestra-researchGenerates publication-quality figures for ML papers from research context. Given a paper section or description, extracts system components and relationships to generate architecture diagrams via Gemini. Given experiment results or data, auto-selects chart type and generates data-driven figures via matplotlib/seaborn. Use when creating any figure for a conference paper.ara-compilerorchestra-researchCompiles any research input — PDF papers, GitHub repositories, experiment logs, code directories, or raw notes — into a complete Agent-Native Research Artifact (ARA) with cognitive layer (claims, concepts, heuristics), physical layer (configs, code stubs), exploration graph, and grounded evidence. Use when ingesting a paper or codebase into a structured, machine-executable knowledge package, building an ARA from scratch, or converting research outputs into a falsifiable, agent-traversable form.ara-research-managerorchestra-researchRecords research provenance as a post-task epilogue, scanning conversation history at the end of a coding or research session to extract decisions, experiments, dead ends, claims, heuristics, and pivots, and writing them into the ara/ directory with user-vs-AI provenance tags. Use as a session epilogue — never during execution — to maintain a faithful, auditable trace of how a research project actually evolved.ara-rigor-reviewerorchestra-researchPerforms ARA Seal Level 2 semantic epistemic review on Agent-Native Research Artifacts, scoring six dimensions (evidence relevance, falsifiability, scope calibration, argument coherence, exploration integrity, methodological rigor) and producing a constructive, severity-ranked report with a Strong Accept-to-Reject recommendation. Use after Level 1 structural validation passes, when an ARA needs an objective epistemic critique before publication or release.audiocraft-audio-generationorchestra-researchPyTorch library for audio generation including text-to-music (MusicGen) and text-to-sound (AudioGen). Use when you need to generate music from text descriptions, create sound effects, or perform melody-conditioned music generation.autogpt-agentsorchestra-researchAutonomous 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.autoresearchorchestra-researchOrchestrates end-to-end autonomous AI research projects using a two-loop architecture. The inner loop runs rapid experiment iterations with clear optimization targets. The outer loop synthesizes results, identifies patterns, and steers research direction. Routes to domain-specific skills for execution, supports continuous agent operation via Claude Code /loop and OpenClaw heartbeat, and produces research presentations and papers. Use when starting a research project, running autonomous experimenawq-quantizationorchestra-researchActivation-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.axolotlorchestra-researchExpert guidance for fine-tuning LLMs with Axolotl - YAML configs, 100+ models, LoRA/QLoRA, DPO/KTO/ORPO/GRPO, multimodal supportblip-2-vision-languageorchestra-researchVision-language pre-training framework bridging frozen image encoders and LLMs. Use when you need image captioning, visual question answering, image-text retrieval, or multimodal chat with state-of-the-art zero-shot performance.brainstorming-research-ideasorchestra-researchGuides researchers through structured ideation frameworks to discover high-impact research directions. Use when exploring new problem spaces, pivoting between projects, or seeking novel angles on existing work.chromaorchestra-researchOpen-source embedding database for AI applications. Store embeddings and metadata, perform vector and full-text search, filter by metadata. Simple 4-function API. Scales from notebooks to production clusters. Use for semantic search, RAG applications, or document retrieval. Best for local development and open-source projects.cliporchestra-researchOpenAI'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.constitutional-aiorchestra-researchAnthropic's method for training harmless AI through self-improvement. Two-phase approach - supervised learning with self-critique/revision, then RLAIF (RL from AI Feedback). Use for safety alignment, reducing harmful outputs without human labels. Powers Claude's safety system.creative-thinking-for-researchorchestra-researchApplies cognitive science frameworks for creative thinking to CS and AI research ideation. Use when seeking genuinely novel research directions by leveraging combinatorial creativity, analogical reasoning, constraint manipulation, and other empirically grounded creative strategies.crewai-multi-agentorchestra-researchMulti-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.deepspeedorchestra-researchExpert guidance for distributed training with DeepSpeed - ZeRO optimization stages, pipeline parallelism, FP16/BF16/FP8, 1-bit Adam, sparse attentiondistributed-llm-pretraining-torchtitanorchestra-researchProvides 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.dspyorchestra-researchBuild complex AI systems with declarative programming, optimize prompts automatically, create modular RAG systems and agents with DSPy - Stanford NLP's framework for systematic LM programmingevaluating-code-modelsorchestra-researchEvaluates code generation models across HumanEval, MBPP, MultiPL-E, and 15+ benchmarks with pass@k metrics. Use when benchmarking code models, comparing coding abilities, testing multi-language support, or measuring code generation quality. Industry standard from BigCode Project used by HuggingFace leaderboards.evaluating-cosmos-policyorchestra-researchEvaluates NVIDIA Cosmos Policy on LIBERO and RoboCasa simulation environments. Use when setting up cosmos-policy for robot manipulation evaluation, running headless GPU evaluations with EGL rendering, or profiling inference latency on cluster or local GPU machines.evaluating-llms-harnessorchestra-researchEvaluates LLMs across 60+ academic benchmarks (MMLU, HumanEval, GSM8K, TruthfulQA, HellaSwag). Use when benchmarking model quality, comparing models, reporting academic results, or tracking training progress. Industry standard used by EleutherAI, HuggingFace, and major labs. Supports HuggingFace, vLLM, APIs.evolving-ai-agentsorchestra-researchProvides 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.experiment-tracking-swanlaborchestra-researchProvides 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.faissorchestra-researchFacebook's library for efficient similarity search and clustering of dense vectors. Supports billions of vectors, GPU acceleration, and various index types (Flat, IVF, HNSW). Use for fast k-NN search, large-scale vector retrieval, or when you need pure similarity search without metadata. Best for high-performance applications.fine-tuning-openvla-oftorchestra-researchFine-tunes and evaluates OpenVLA-OFT and OpenVLA-OFT+ policies for robot action generation with continuous action heads, LoRA adaptation, and FiLM conditioning on LIBERO simulation and ALOHA real-world setups. Use when reproducing OpenVLA-OFT paper results, training custom VLA action heads (L1 or diffusion), deploying server-client inference for ALOHA, or debugging normalization, LoRA merge, and cross-GPU issues.fine-tuning-serving-openpiorchestra-researchFine-tune and serve Physical Intelligence OpenPI models (pi0, pi0-fast, pi0.5) using JAX or PyTorch backends for robot policy inference across ALOHA, DROID, and LIBERO environments. Use when adapting pi0 models to custom datasets, converting JAX checkpoints to PyTorch, running policy inference servers, or debugging norm stats and GPU memory issues.fine-tuning-with-trlorchestra-researchFine-tune LLMs using reinforcement learning with TRL - SFT for instruction tuning, DPO for preference alignment, PPO/GRPO for reward optimization, and reward model training. Use when need RLHF, align model with preferences, or train from human feedback. Works with HuggingFace Transformers.gguf-quantizationorchestra-researchGGUF format and llama.cpp quantization for efficient CPU/GPU inference. Use when deploying models on consumer hardware, Apple Silicon, or when needing flexible quantization from 2-8 bit without GPU requirements.gptqorchestra-researchPost-training 4-bit quantization for LLMs with minimal accuracy loss. Use for deploying large models (70B, 405B) on consumer GPUs, when you need 4× memory reduction with <2% perplexity degradation, or for faster inference (3-4× speedup) vs FP16. Integrates with transformers and PEFT for QLoRA fine-tuning.grpo-rl-trainingorchestra-researchExpert guidance for GRPO/RL fine-tuning with TRL for reasoning and task-specific model training