Skills
97,725 packaged capabilities and playbooks for agents. Install any of them with one command.
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Generates WAFFLES Declarations for social media posts — preemptive lists of what a post does NOT say. Use when users mention WAFFLES, ask for clarifications on their post, want to prevent misinterpretation, or request disclaimers for controversial/nuanced takes.
Create, join, and manage teams on OpenAnt. Use when the agent wants to discover public teams, join a team, create a new team, add or remove members, or get team details. Covers "find teams", "join a team", "create team", "team members", "manage my team".
Build, review, debug, migrate, or plan React code with @mantine/hooks only (standalone React hooks package). Use for useDisclosure, useToggle, useListState, useLocalStorage, useSessionStorage, useHotkeys, getHotkeyHandler, useClickOutside, useDebouncedValue, useDebouncedState, useDebouncedCallback, useThrottledValue, useMediaQuery, useColorScheme, useReducedMotion, useMergedRef, useMove, useDrag, useMask, useCollapse, useHorizontalCollapse, useRovingIndex, useSplitter, useFloatingWindow, useUncontrolled, useFocusTrap, useIntersection, useResizeObserver, useFetch, clamp, randomId, and Mantine 8→9 hooks migrations. Do not use for @mantine/core components, @mantine/form, themes, or other Mantine packages.
Add Lens to a live notebook, explain a notebook with a guided Trail, or address a user's selection on a rendered output or configured DOM target. Use when asked to enable Lens, give a walkthrough, explain results in place, or act on selected points, regions, and notes. The address mode handles every open selection. Requires the live marimo notebook kernel, reached through marimo pair or the editor's code-mode sidebar.
Aggregate Shanghai/Shenzhen Stock Exchange market overviews, trading summaries, regional and industry statistics and capital overviews to quickly grasp market conditions. Use when the user asks about market overviews, exchange statistics, trading summaries, market capital overviews, or needs a market dashboard.
Integrate the alpha markstream-angular package into an Angular 20+ app. Use when Codex needs standalone component imports, signal-based examples, CSS wiring, custom HTML tags or customComponents setup, or optional peer integration in an Angular repository.
Use masscan for very large-scale, high-speed port scanning. Use when scanning large CIDR ranges or at whole-internet scale. masscan is an asynchronous, stateless scanner capable of millions of packets per second, suited to large-scale asset discovery. Requires root privileges. Use this skill for any scenario involving large-scale network scanning, high-speed port discovery, or internet-wide mapping
Microstrip, stripline, CPW, differential pairs, and crosstalk analysis for impedance-controlled PCB interconnects. TRIGGER: user asks to design or analyze a transmission line (microstrip, stripline, CPW, coplanar, differential pair), extract RLGC or per-unit-length parameters, compute trace impedance, analyze a PCB trace cross-section, or perform crosstalk/coupling analysis. Invoke BEFORE writing code — preferred over RF Toolbox analytical functions (txlineMicrostrip, txlineStripline, txlineCPW). SKIP: EM simulation/S-parameter extraction of an existing component (use matlab-analyze-em), material/stackup definition only (use matlab-manage-pcb-material), building custom non-catalog geometry (use matlab-assemble-pcb-layout), optimization sweeps (use matlab-optimize-pcb-design).
Identify a linear dynamic model from input-output or time-series data using MATLAB System Identification Toolbox. Use when estimating transfer function, state-space, ARX, ARMAX, BJ, OE polynomial or process models from measurement data
Add OpenTelemetry tracing to MATLAB code. Use when the user asks to "add tracing", "instrument with spans", "add OpenTelemetry", "trace my code", "add observability" (when about tracing), or mentions "spans", "distributed tracing", or "OTel tracing" in the context of MATLAB functions. Covers span creation, parent-child context propagation, error handling, attributes, events, and semantic conventions
Low-level plotting library for full customization. Use when you need fine-grained control over every plot element, creating novel plot types, or integrating with specific scientific workflows. Export to PNG/PDF/SVG for publication. For quick statistical plots use seaborn; for interactive plots use plotly; for publication-ready multi-panel figures with journal styling, use scientific-visualization.
Cordova MatrixScan AR (Barcode AR, BarcodeAr) via the scandit-cordova-datacapture-* plugins — scanning multiple barcodes at once with AR highlights and annotations (info annotations, popovers, status icons) on tracked barcodes. Use for integration, symbology configuration, highlight and annotation providers, BarcodeArView customization, migration from BarcodeBatch/BarcodeTracking, or troubleshooting.
Capacitor MatrixScan Batch (scandit-capacitor-datacapture-barcode) — MatrixScan, BarcodeBatch, legacy BarcodeTracking — tracking and scanning multiple barcodes at once with basic/advanced AR overlays in Capacitor iOS/Android apps (not the plain-web sibling). Use for integration, settings and symbologies, per-barcode brushes, TrackedBarcodeView annotations, lifecycle, SDK version migration, replacing a third-party scanner (@capacitor-mlkit/barcode-scanning), or troubleshooting.
Capacitor MatrixScan Count (BarcodeCount) — plugin scandit-capacitor-datacapture-barcode. Multi-barcode counting and receiving workflows (scan-and-count, inventory count, capture list, status mode) with BarcodeCountView on a DOM element in Capacitor apps, iOS/Android native only. For React Native use matrixscan-count-rn. Use for integration, symbology configuration, result handling, view customization, or troubleshooting counting workflows.
Guide for implementing deep linking in .NET MAUI apps. Covers Android App Links with intent filters, Digital Asset Links, and AutoVerify; iOS Universal Links with Associated Domains entitlements and Apple App Site Association files; custom URI schemes; and domain verification for both platforms. USE FOR: "deep linking", "app links", "universal links", "custom URI scheme", "intent filter", "Associated Domains", "Digital Asset Links", "open app from URL", "handle incoming URL", "domain verification". DO NOT USE FOR: in-app Shell navigation (use maui-shell-navigation), push notification handling (use maui-push-notifications), or web content embedding (use maui-hybridwebview).
Use this skill when the user asks to “add MCP server”, “integrate MCP”, “configure MCP in plugin”, “use .mcp.json”, “set up Model Context Protocol”, “connect external service”, mentions “${CLAUDE_PLUGIN_ROOT} with MCP”, or discusses MCP server types (SSE, stdio, HTTP, WebSocket). Provides comprehensive guidance for integrating Model Context Protocol servers into Claude Code plugins for external tool and service integration.
Security monitoring agent for Claude Skills and MCP servers. Real-time protection layer (PreToolUse hook, zero LLM cost by default): hard-blocks confirmed-malicious tool calls (known-bad domains from real incidents and an auto-updating URLhaus malware feed) and, for merely suspicious ones (credential exfiltration, reverse shells, curl|bash pipes, raw-IP URLs, cloud-metadata/IMDS, config/persistence writes), asks you to approve or deny at the native prompt instead of blocking outright. Approving a flagged path/domain is remembered so it stops asking (trust builds as you confirm what you use). v3 adds: multi-step attack-chain detection (credential access then egress), cross-server data-flow tracking, a config/MCP scanner + integrity baseline (catches a malicious hook planted in a cloned repo), a shadow/audit-only mode (SENTINEL_SHADOW: never blocks, just tallies what it would have stopped), and an OPTIONAL, off-by-default AI escalation layer (SENTINEL_AI: only for ambiguous cases, token-budgeted, hardened against prompt injection). Localized messages (Spanish/English). Threat data ships base64-at-rest so a host antivirus does not flag the install. The v1 static analysis still runs on demand: scans installed skills/MCPs against vulnerability databases (GitHub Advisory DB, vulnerablemcp.info, CVE feeds, mcpscan.ai, Snyk, ClawHub/VirusTotal) and community alerts, maintains a local threat database, performs coherence and update-diff analysis. Use this skill whenever: the user asks about security of their skills or MCPs, wants to audit installed plugins, enable real-time protection, mentions "vulnerability", "CVE", "malicious skill", "security scan", "threat", "audit", "runtime protection", "block", says "is this skill safe?", asks to check dependencies, or wants ongoing security monitoring. Also trigger proactively when the user is about to install a new skill or MCP server.
Configure and manage MCP (Model Context Protocol) servers in Claude Code CLI. Use this skill when adding, removing, listing, or troubleshooting MCP servers, or when questions arise about MCP configuration, transport types, scopes, or authentication. Essential for connecting Claude Code to external tools, databases, and APIs.
EU MDR 2017/745 regulation specialist and consultant for medical device requirement management. Provides comprehensive MDR compliance expertise, gap analysis, technical documentation guidance, clinical evidence requirements, and post-market surveillance implementation. Use for MDR compliance assessment, classification decisions, technical file preparation, and regulatory requirement interpretation.
Filters and triages small-molecule libraries with the Python medchem library (datamol-io, v2.0.5) on top of RDKit and datamol. Covers drug-likeness rules (Lipinski, Veber, CNS, lead-like) via RuleFilters, structural alerts (ChEMBL-derived sets, NIBR, PAINS, Brenk), chemical group detection, ZINC-based complexity thresholds, scaffold constraints, and the medchem query language (QueryFilter). Use when screening a compound library for drug-likeness, removing PAINS or other structural-alert compounds, prioritizing hits for hit-to-lead or lead optimization, detecting functional groups, or combining criteria in one query. Not for computing general descriptors or fingerprints; use RDKit directly.
Write a media pitch or press outreach email for any story or announcement. Use when asked to write a media pitch, journalist outreach email, press pitch, or story angle for PR. Produces a concise pitch with a compelling news angle, journalist-specific hook, and clear call to action.
Use when running the zetic.ai `melange` CLI: uploading/importing on-device AI models, monitoring conversion status, reporting Melange benchmarks, generating deployment guides, browsing repos and the public library. Covers auth, choosing between importing a Hugging Face repo id and uploading exported artifacts, JSON output contract, exit-code branching, upload/resume, non-blocking conversion monitoring, model report templates, and the raw API escape hatch. Also points at the `melange mcp` MCP server alternative and when to prefer its tools over shelling out. Do NOT trigger for general ML work — training, local inference, or other model registries — unless the `melange` CLI is involved.
Dedicated Skill for the doors, windows and window sealing pitfall guide. Covers the full process including window selection, entry doors and interior doors, window sealing and floor-to-ceiling windows, curtains and shading, installation sealing and hardware, and door-and-window selection overviews, integrating the highlights of the Xi'an Renovation Classroom members' edition knowledge base in reverse order, helping users renovate with less hassle and fewer pitfalls in doors, windows and window sealing.
Merge open Dependabot pull requests in a GitHub repository using the `gh` CLI, filtered by semver bump type and CI status. Use this skill whenever the user asks to merge, clear, batch, or clean up Dependabot PRs — "merge all dependabot PRs", "clear the dependabot backlog", "merge the dependency bumps", "land the safe dependabot updates" — or invokes it as a slash command, including when they ask to include majors, merge despite failing checks, or squash instead of rebase. Not for reviewing or merging human-authored PRs, and not for writing dependabot.yml — that is dependabot-config.
Use when working with low-level Cardano utilities via MeshJS core-cst package. Covers CBOR serialization and deserialization, Plutus data conversion, address resolution and parsing, CIP-8 message signing and verification, script parameterization with applyParamsToScript, native script hashing, and direct access to cardano-sdk types.
Integrating TanStack DB with meta-frameworks (TanStack Start, Next.js, Remix, Nuxt, SvelteKit). Client-side only: SSR is NOT supported — routes must disable SSR. Preloading eager collections in route loaders with collection.preload(). On-demand Query Collections require preloading the live query because source collection preload is a no-op. Multiple collection preloading with Promise.all. Framework-specific loader APIs.
Migrate an existing repository from the legacy planned-features.md plus per-feature design.md/tasks.md workflow to dstack's Copier- and Beads-based workflow. Use when asked to adopt dstack, convert legacy workflow state, or resume an interrupted workflow migration.
Computes FAAB (Free Agent Acquisition Budget) recommended and maximum bids for Yahoo fantasy baseball waiver targets. Implements the baseball-specific layering of the faab-bid-framework (positional_need_fit, role_certainty, urgency, season_pace, league-inflation calibration) and DELEGATES the game-theoretic primitives -- first-price shading and winner's-curse haircut -- to the sibling skills `auction-first-price-shading` and `auction-winners-curse-haircut`. Produces a recommended bid, a hard ceiling, a rationale with the full delegation chain, and guardrail flags. Use when the user asks "how much should I bid on X", mentions FAAB bid, waiver bid amount, blind bid, Yahoo waiver claim sizing, or when mlb-waiver-analyst needs a bid amount for an identified target.
Weekly refresh of per-opponent archetype + behavioral profiles for the 11 opposing teams in the user's Yahoo Fantasy Baseball league (ID 23756). Thin baseball-specific wrapper around the domain-neutral `opponent-archetype-classifier` -- provides the 10-archetype MLB taxonomy (balanced, stars_and_scrubs, punt_sv, punt_sb, punt_wins_qs, hitter_heavy, pitcher_heavy, inactive, frustrated_active, unknown), extracts MLB features from Yahoo pages (draft distribution, FAAB spend, waiver pattern, roster composition, lineup consistency, trade activity, recent record, activity recency), invokes the classifier, and writes/updates `context/opponents/<team-slug>.md` files per `opponent-profile-schema.md`. Read-modify-write preserves manual notes. Emits a weekly summary signal at `signals/wkNN-opponent-profiles.md`. Use when user says "opponent profiling", "classify opposing manager", "update opponent profiles", "refresh opponents", "weekly opponent scout", or "MLB fantasy opponent archetype".
For a given fantasy week (Monday-Sunday), identifies every starting pitcher scheduled to start twice, validates both probable starts, grades each matchup against the league's Quality Starts (QS) scoring rules, and ranks the list by streamability_score. Flags bullpen-game and opener risks that nearly never produce QS. Use when user mentions "two-start pitchers", "weekly streaming", "Monday-Sunday pitcher plan", "double start", "2-start SP", or preparing the weekly streaming plan on Sunday nights.
Cross-platform and native mobile development. Frameworks: React Native, Flutter, Swift/SwiftUI, Kotlin/Jetpack Compose. Capabilities: mobile UI, offline-first architecture, push notifications, deep linking, biometrics, app store deployment. Actions: build, create, implement, optimize, test, deploy mobile apps. Keywords: iOS, Android, React Native, Flutter, Swift, Kotlin, mobile app, offline sync, push notification, deep link, biometric auth, App Store, Play Store, iOS HIG, Material Design, battery optimization, memory management, mobile performance. Use when: building mobile apps, implementing mobile-first UX, choosing native vs cross-platform, optimizing battery/memory/network, deploying to app stores, handling mobile-specific features.
Dress a model in a real garment or accessory from a flat-lay, hanger or mannequin photo (on-model / virtual try-on) while preserving fabric, colour, fit, prints and logos. Supports pose references, multiple body types and ghost-mannequin output. Use for 模特图, 上身图, 试穿, try-on, on-model photo, apparel and jewellery.
Run and analyze molecular dynamics simulations with OpenMM and MDAnalysis. Set up protein/small molecule systems, define force fields, run energy minimization and production MD, analyze trajectories (RMSD, RMSF, contact maps, free energy surfaces). For structural biology, drug binding, and biophysics.
Converts SMILES strings or RDKit/datamol molecules into numerical features using molfeat (0.11.0), which provides calculators, scikit-learn compatible transformers, and pretrained embedding models. Covers fingerprints (ECFP, MACCS, MAP4), RDKit and Mordred descriptors, pharmacophore descriptors, and pretrained models such as ChemBERTa and GIN, with parallel processing and caching. Use when building QSAR or QSPR models from SMILES. Use when choosing among molecular featurizers for a property prediction task. Use when generating embeddings for virtual screening, similarity search, or chemical space clustering. Use when adding a featurizer to a scikit-learn pipeline. Not for general cheminformatics tasks such as reading or editing structures, which belong to RDKit itself.
Molten OS Pro — adversarially validate a product or business idea (quick score, full 5-round interview, or re-validate). 10-criterion rubric (/50), red flags, falsifiable experiment; writes molten-docs/validate/product-score-N.md. Use when the user pitches an idea, asks if it is a good business/product, or wants a sanity check. Do NOT use when the idea is already validated and the user only needs implementation. Requires molten-os-core (molten-brand, molten-design, molten-landing) after a strong score.
Use the "demand-signal backward inference" method to find part-time or side-hustle opportunities in the AI era — don't search for "teach you to make money" posts (those are polluted sources from course sellers fishing for followers), but instead look at interest-neutral demand signals such as industry reports, hiring data and procurement data, infer opportunities an individual could supply, and cross-validate with multiple independent signals; each opportunity then goes through an anti-scam + timeliness rubric. Trigger words: "help me find opportunities" / "find AI part-time work" / "what can make money" / "money find" / "find side hustles". Prerequisite: requires .money-state.json (if absent, first route to money-init).
Status dashboard for the cheat-on-money skill. Shows the user profile, candidate opportunities and their anti-scam verdicts, currently executing opportunities and progress, review records, and suggested next steps. No side effects; can be called at any time. Trigger words: "money status" / "my current progress" / "dashboard" / "money status" / "what should I do now".
Run Monte Carlo simulations for Finance Guru portfolio strategy. USE WHEN user mentions monte carlo OR run simulation OR stress test portfolio OR probability analysis OR income projections OR margin safety analysis. Supports 4-layer portfolio (Growth, Income, Hedge, GOOGL) with auto-detection of current values from Fidelity CSV.
Expand an image-generation request into a detailed MOSS-Image caption or spatial layout, and compile a local JSON draft into a ready-to-paste prompt. Use for prompt enhancement, image composition and text placement; this skill does not generate images.
(NS) Interview users through focused technical questions to recommend LangGraph vs CrewAI, propose multi-agent architecture, and design agent personas with tools and models. Use whenever the user wants to build a multi-agent system, choose between LangGraph and CrewAI, design agent crews or graphs, plan human-in-the-loop workflows, or asks how to orchestrate AI agents — even if they only say "I want a bot for my company" or "agents working together". Do NOT use for general app requirements (use pm-clarify-requirements) or implementation coding without architecture intent.
Design and implement multi-agent LLM architectures using the orchestrator-subagent pattern. Use when: (1) Deciding whether to use multi-agent vs single-agent systems, (2) Implementing context isolation for high-volume operations, (3) Parallelizing independent research tasks, (4) Creating specialized agents with focused tool sets, (5) Building verification subagents for quality assurance, or (6) Analyzing context-centric decomposition boundaries.
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.