Skills
100,928 packaged capabilities and playbooks for agents. Install any of them with one command.
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Use this skill when the user asks about Syncfusion Vue licensing, license keys, license registration, license validation errors, trial licenses, or license troubleshooting. This skill provides comprehensive guidance on generating, registering, and managing Syncfusion license keys for Vue applications, including edition-based and platform-based licensing models, registration methods, CI/CD integration, and resolving common licensing errors.
Access a collection of open-source molecular design and structural biology tools on the Tamarind Bio platform, via its REST API or MCP server — no local GPUs required. Tamarind bundles popular open-source models for structure prediction (AlphaFold, Boltz, Chai, ESMFold), protein, binder, and de novo design (RFdiffusion, ProteinMPNN, BoltzGen), antibody and nanobody design and developability, protein-ligand docking (DiffDock, Autodock Vina), binding-affinity prediction, MSA generation, and molecular dynamics. Use when the user mentions Tamarind or tamarind.bio, wants to run any of these open-source tools in the cloud, references app.tamarind.bio/api or the x-api-key header, or needs to submit batches of sequences for structural or biophysical characterization.
Your team has high autonomy — and standups are vague, weekly meetings drift into status reports, and 1:1s produce nothing but "I'm fine." This playbook fixes all three layers at once. What's inside: • Daily Standup system — 3-field format, good sample, 4 COO principles to keep it honest • Weekly Meeting — strict 25-min agenda, Scrum Master rotation, 0–10 pulse check • 1:1 Framework — 9-dimension quantified pulse, 5-group deep question bank, bilateral feedback structure • Diagnostic layer — "stuck-at" detection: info / authority / collaboration / capability / direction • FAQ for 4 common failure modes (dead standups, status-report meetings, "I'm fine" 1:1s, scaling beyond 10 people). Triggers: "团队 motivation" | "standup 没人写" | "例会变成汇报" | "1:1 问不出真话" | "高自由度团队管理" | "COO 如何管人" | "team motivation" | "standup best practices" | "1on1 framework" | "weekly meeting template" | "チームモチベーション" | "팀 동기부여" | "AI startup team ops" | "remote team alignment" | "engagement下降"
Coordinate team task execution on OpenAnt. Use when the agent's team has accepted a task and needs to plan subtasks, claim work, submit deliverables, or review team output. Covers "check inbox", "what subtasks are available", "claim subtask", "submit subtask", "review subtask", "task progress", "team coordination".
Writing developer-facing prose that can be skimmed first and trusted enough to finish — READMEs, guides, tutorials, reference docs, proposals, PR descriptions, release notes. Load before drafting, rewriting, restructuring, or editing technical documents, when a doc reads as a wall of text, claims need receipts, or docs must serve AI agents. Covers reader-first structure, falsifiable claims, docs-as-behavior verification, and agent-readable reference shape. For sentence-level voice or generic AI-shaped prose use clarity; for plain English, lay readers, translation, or ASD-STE100 use simple-english; for recording a learning use expectations; for diagrams use diagrams; for API semantics use api-design; for CLI help use cli-design.
Support QA and testing work across formats: planning checks, writing test cases, reviewing coverage, designing Playwright browser and component integration tests, creating locators, and keeping automated tests focused on observable product behavior.
Conduct an evidence-backed TikTok Shop business study with current KOLSprite product, creator, video, shop, and caption data. Use for opportunity evaluation, cold-start planning, winning-content research, creator selection, competitor comparison, growth diagnosis, and questions such as whether and how a direction is worth testing. Do not use for a single direct lookup or caption-only request.
PyTorch-based ML platform for drug discovery: graph molecular representation learning, property prediction (ADMET, activity), retrosynthesis, drug-target interaction (DTI), and pretraining on large molecular datasets. Provides GNN layers (GraphConv, GAT, MPNN), pretrained models, and benchmark datasets.
Transcribe audio and video from URLs (YouTube, direct media links) using WhisperKit locally. Optionally analyze transcripts with AI when explicitly requested. Use when users provide URLs to media content and request transcription or speech-to-text conversion.
Convert and browse session transcripts as HTML or Markdown. Supports Claude Code JSONL logs (auto-saved to ~/.claude/projects/) and GitHub Copilot CLI JSONL logs (auto-saved to ~/.copilot/session-state/*/events.jsonl). Auto-detects log source based on available directories and file format. Supports viewing the current session, a specific session by ID, agent background task output files, or all project sessions with optional date-range filtering.
HuggingFace Transformers with biomedical LMs (BioBERT, PubMedBERT, BioGPT, BioMedLM) for scientific NLP: NER (genes, diseases, chemicals), relation extraction, QA, text classification, abstract summarization. Covers loading, biomedical tokenization, inference pipelines, fine-tuning. Alternatives: spaCy en_core_sci_lg (rule-based NER), Stanza (biomedical models), NLTK.
"How to create and use Account (signer) in @aptos-labs/ts-sdk. Covers Account.generate(), fromPrivateKey(), fromDerivationPath(), Ed25519 vs SingleKey vs MultiKey vs Keyless, serialization (fromHex/toHex). Triggers on: 'Account.generate', 'Account.fromPrivateKey', 'Ed25519PrivateKey', 'SDK account', 'mnemonic', 'SingleKeyAccount', 'KeylessAccount'."
"How to create and use AccountAddress in @aptos-labs/ts-sdk. Covers address format (AIP-40), from/fromString/fromStrict, special addresses, LONG vs SHORT form, and derived addresses (object, resource, token, user-derived). Triggers on: 'AccountAddress', 'AccountAddress.from', 'AIP-40', 'derived address', 'createObjectAddress', 'createResourceAddress', 'createTokenAddress'."
Turso SQLite database. Covers encryption, sync, agent patterns. Use when working with Turso/libSQL embedded databases, configuring encryption-at-rest, setting up sync replication, or building agent-friendly database patterns. Keywords: Turso, libSQL, embedded, SQLite, encryption, sync.
Tiered LLM extraction pattern for large corpus processing (email archives, document dumps, transcript libraries). A utility-tier model triages and classifies at speed; the reasoning tier does the default deep read; the deep tier is the escalation for the highest-value content. Prevents spending deep-tier money on noise while ensuring the important content gets the best eyes. A deterministic privacy wall runs before any LLM call.
UI design system toolkit for Senior UI Designer including design token generation, component documentation, responsive design calculations, and developer handoff tools. Use for creating design systems, maintaining visual consistency, and facilitating design-dev collaboration.
UI/UX design intelligence for web and mobile. Includes 50+ styles, 161 color palettes, 57 font pairings, 161 product types, 99 UX guidelines, and 25 chart types across 10 stacks (React, Next.js, Vue, Svelte, SwiftUI, React Native, Flutter, Tailwind, shadcn/ui, and HTML/CSS). Actions: plan, build, create, design, implement, review, fix, improve, optimize, enhance, refactor, and check UI/UX code. Projects: website, landing page, dashboard, admin panel, e-commerce, SaaS, portfolio, blog, and mobile app. Elements: button, modal, navbar, sidebar, card, table, form, and chart. Styles: glassmorphism, claymorphism, minimalism, brutalism, neumorphism, bento grid, dark mode, responsive, skeuomorphism, and flat design. Topics: color systems, accessibility, animation, layout, typography, font pairing, spacing, interaction states, shadow, and gradient. Integrations: shadcn/ui MCP for component search and examples.
UiPath Test Manager (`uip tm`) — test projects, requirements, cases, sets, executions, logs, defects; recurring test set schedules; AI test case generation, requirement evaluation, obsolete test case search; CI run of every test in an uploaded package; performance (load) test scenarios; test reports; package/run external Playwright suites. Orchestrator→uipath-platform. Studio/RPA test authoring→uipath-rpa.
UMAP dimensionality reduction for visualization, clustering prep, and feature engineering. Fast nonlinear manifold learning preserving local and global structure. Standard UMAP (fit/transform, sklearn-compatible), supervised/semi-supervised, Parametric UMAP (NN encoder/decoder, TensorFlow), DensMAP (density), AlignedUMAP (temporal/batch). 15+ distance metrics, custom Numba metrics, precomputed distances. For linear reduction use PCA; for neighborhood graphs use sklearn NearestNeighbors.
Explains PostHog billing usage and spend from the customer's visible Billing MCP tools. Use when the user asks why usage or spend is high, which product or project is driving usage, what a usage type means, how to reduce usage, what changed over time, why they got a usage change alert, or whether a spike/drop alert was real or noisy. Also use before product-specific analytics skills when the user names a billable PostHog product metric such as events, recordings, feature flag requests, exceptions, survey responses, synced rows, logs, AI events, AI credits, or Inbox credits. Starts from Billing usage/spend tools, then routes to customer-visible product MCP surfaces for deeper investigation.
Unity 6 AI and navigation guide. Use when working with NavMesh, pathfinding, NavMeshAgent, NavMeshSurface, NavMeshObstacle, off-mesh links, or Unity Sentis (ML model inference). Covers AI navigation package, runtime NavMesh baking, and common AI patterns like state machines and behavior trees. Based on Unity 6.3 LTS documentation.
Scan React codebase for useEffect anti-patterns (derived state, event logic in effects, effect chains, prop-driven resets, missing cleanup, parent notifications, etc.) and propose idiomatic fixes. Based on React's "You Might Not Need an Effect" guide. Use when asked to audit, review, or clean up useEffect usage.
Analyze and synthesize user research findings into structured, actionable insights. Use when given user research data, interview transcripts, survey results, or user feedback that needs to be analyzed and summarised. Produces a themed synthesis with prevalence data, supporting quotes, pain points analysis, feature request prioritisation, and recommended next steps. For interview transcripts specifically use user-interview-synthesis instead.
Implement or revise exactly one canonical GitHub Issue, satisfy and verify all acceptance criteria, and create or update its focused Pull Request. Use for a specific story or issue, requested changes on its PR, or as the implementation worker in user-story-delivery.
Out-of-core DataFrame for billion-row data via lazy evaluation and memory-mapped files. Use when data exceeds RAM (10 GB–TB) for fast aggregation, filtering, virtual columns, and visualization without loading. Supports HDF5, Arrow, Parquet, CSV with cloud (S3, GCS, Azure). Built-in ML transformers (scaling, PCA, K-means). In-memory: polars; distributed: dask.
Validate and publish a canvas source project safely: the source-project shape, declared capabilities, reading the current version pointer, iterating on validation diagnostics, guarded publishing with expected_current_version_id, staging a draft build and promoting it, waiting out the queued build, and recovering from a 409 version_conflict or a 429 capacity limit without overwriting concurrent work. Use whenever a canvas edit is ready to save, a draft build is wanted, a canvas publish or build returns diagnostics or a conflict, or a task needs to understand canvas version history.
React and Next.js performance optimization guidelines from Vercel Engineering. This skill should be used when writing, reviewing, or refactoring React/Next.js code to ensure optimal performance patterns. Triggers on tasks involving React components, Next.js pages, data fetching, bundle optimization, or performance improvements.
React and Next.js performance optimization guidelines from Vercel Engineering. This skill should be used when writing, reviewing, or refactoring React/Next.js code to ensure optimal performance patterns. Triggers on tasks involving React components, Next.js pages, data fetching, bundle optimization, or performance improvements.
Video compression and silent-segment cutting tool. Compresses video with FFmpeg CRF mode, suited to screen recordings and course material; detects and removes silent, static segments. Automatically detects hardware and source bitrate and picks the best encoding approach (hardware-accelerated VideoToolbox for high-bitrate sources, x264 CRF adaptive encoding for low-bitrate screen recordings). Use when the user needs to compress videos, reduce video size, or remove dead air. Not for: video editing, audio extraction, format conversion.
Turn a brand angle into a finished Vox-style paper-collage explainer ad on the Advibly MCP. Approve a narrative beat map and visual theme, generate layered collage-poster keyframes, animate living paper motion with Gemini Omni Flash, then compose narration, music, and SFX. Supports baked headlines, photoreal product cutouts, assemble-from-empty reveals, confetti, impact shake, and editorial collage motion. Trigger for "Vox style video", "paper collage animation", "motion collage", "collage explainer", "collage ad", "paper collage explainer", "make a collage ad", "explainer video for my product", or a reference with the editorial torn-paper look. Use even without the word "skill" when that style is clearly requested.
Expert guidance for fetching and parsing web content from URLs, handling various content sizes, JavaScript-rendered sites, and working around tool limitations. Use when fetching articles, documentation, social media posts, or any web content for translation, analysis, or archiving.
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.