Guide for creating and displaying context menus in Wave Terminal. Use when implementing right-click menus, adding context menu items, creating submenus, or handling menu interactions with checkboxes and separators.
日本語の概要は準備中です。原文の説明を表示しています。
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Guide for creating and displaying context menus in Wave Terminal. Use when implementing right-click menus, adding context menu items, creating submenus, or handling menu interactions with checkboxes and separators.
日本語の概要は準備中です。原文の説明を表示しています。
Step 4 of the PaperOrchestra pipeline (arXiv:2604.05018). ONE single multimodal LLM call that drafts the remaining paper sections (Abstract, Methodology, Experiments, Conclusion), extracts numeric values from experimental_log.md into LaTeX booktabs tables, splices the generated figures from Step 2, and merges everything into the template that already contains Intro + Related Work from Step 3. TRIGGER when the orchestrator delegates Step 4 or when the user asks to "write the methodology and experiments sections" or "fill in the rest of the paper".
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Render Mermaid diagrams as ASCII/Unicode art for terminal display or as SVG files. Use when visualizing flowcharts, state machines, sequence diagrams, class diagrams, or ER diagrams. Supports 17 themes (including vellum, tokyo-night, catppuccin, nord, dracula, github) plus custom colors via --colors JSON.
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Search and download images from 15 APIs in parallel (Openverse, Pexels, Pixabay, Met Museum, Cleveland Museum, Art Institute of Chicago, Getty Museum, NASA, Rijksmuseum, Wikimedia Commons, Unsplash, Smithsonian, Europeana, Iconify, Pollinations AI). Supports license filtering, presets (military, museum, stock), cached results, and bulk download with metadata sidecars. Triggers on find images, stock photos, public domain artwork, museum images, CC-licensed, NASA photos, icons.
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Generate hand-crafted web animation patterns as self-contained HTML: typewriter/rotator effects, progress bar simulations, file review state machines, staggered reveals, terminal status displays, streaming AI-response demos, popover/dialog/toast lifecycles, Lottie orchestration, scroll-driven sequences. Behavioral animations that simulate live software. Triggers on typewriter effect, progress animation, stagger reveal, terminal animation, streaming text, AI demo animation, toast animation, data simulation, product demo animation, loading sequence, conductor motion.
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Expert coding skill for the full MapTiler platform — Cloud REST APIs, MapTiler SDK JS (built on MapLibre GL JS), native mobile SDKs, on-premise infrastructure, and vector tile schemas. USE WHEN the user wants to add a map to a web or mobile app, show locations or routes, display geographic data, build a store locator, create data visualizations on maps, add geocoding or address search, do reverse geocoding, look up elevation, do IP geolocation, fetch weather data, transform coordinates between CRS/EPSG, embed static map images, work with vector tilesets (Planet v4, Buildings, Contours, Outdoor, Ocean, Landcover, Cadastre), write MapLibre style expressions or data-driven styling, render 3D terrain, globe view, or 3D buildings extrusion, use satellite imagery, add markers, popups, heatmaps, or clustering, integrate maps in React, Vue, Svelte, Angular, Next.js, Leaflet, OpenLayers, Cesium, or deck.gl, build native maps on iOS (Swift), Android (Kotlin), Flutter, or React Native, self-host with MapTiler Server or generate tiles with MapTiler Engine CLI, or upload/manage tilesets via the Admin API. Also USE WHEN the user mentions MapLibre GL JS — the SDK extends MapLibre with built-in cloud services, helpers, and session billing.
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Rebalances context across Memory, Custom Instructions, knowledge files, and User Preferences in Claude Projects. Audits Memory for redundancy, staleness, and misplacement; prescribes optimization including Codification of stable Memory patterns into explicit User Preferences or Project CI rules. Use when user says "optimize my memory," "what should be in my memory," "trim my knowledge files," "reduce my context usage," "rebalance my project," "what should be in my preferences vs memory," or asks whether something belongs in Memory, a knowledge file, or User Preferences. Also trigger on symptom-phrased: "my project feels bloated," "Claude keeps forgetting things," "my context window keeps hitting limits." Activate whenever Memory-layer balance is the primary concern. Do NOT use for full Project audits (use rootnode-project-audit if available), single-prompt evaluation (use rootnode-prompt-validation if available), or global-layer audits that don't touch Project Memory (use rootnode-global-audit if available).
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This skill should be used when the user asks to "create a team", "spawn agents", "parallel execution", "delegate tasks to agents", "use a swarm", "team workflow", "split work across agents", "run tasks in parallel", or mentions wanting multiple agents to collaborate on a complex task.
日本語の概要は準備中です。原文の説明を表示しています。
Restore legibility to an auto-generated graph diagram that has outgrown its canvas - rendering at single-digit percent scale with sub-pixel labels. Covers the levers that measurably reduce layout units (source-level label wrapping, invisible chain links so unconnected nodes stop spreading into one row, per-cluster splitting, layout direction picked by rendering both and measuring) and the rejected ones (raising the render font is a no-op because layout engines measure in text units; a topology heuristic for direction regressed 13 of 38 diagrams). Use when a generated Mermaid/Graphviz/DOT diagram is too small to read; not for hand-authoring a small diagram.
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Refactor an over-long or poorly structured SKILL.md by extracting examples to references/EXAMPLES.md, splitting compound procedures, and reorganizing sections for progressive disclosure. Use when a skill exceeds the 500-line CI limit, when code blocks dominate the skill body, when a procedure step contains multiple unrelated operations, or after a content update pushed the skill over the line limit.
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Redact reverse-engineering findings for public disclosure while preserving methodology, generalizable patterns, and teaching value. Covers the private-vs-public repo split, deny-list pattern maintenance, orphan-commit publish pattern that prevents `git log` leaks, category-based redaction calibration (methodology/pattern/version-finding/internal), and the `check-redaction.sh`-style CI gate that blocks merges when a deny-listed pattern appears. Use when publishing findings about a CLI harness you don't own, when preparing upstream proposals to an unrelated project, or when archiving a private research repo for public reference.
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Evaluate and compare levitation mechanisms for a given application through a structured trade study. Covers magnetic (passive diamagnetic, active feedback, superconducting), acoustic (standing wave, phased array), aerodynamic (hovercraft, air bearings, Coanda effect), and electrostatic (Coulomb suspension, ion traps) mechanisms. Use when selecting the most appropriate levitation approach for transport, sample handling, display, bearings, or precision measurement applications.
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Evolve an existing team composition by refining its structure in-place or creating a specialized variant. Covers assessing the current team against template and coordination patterns, gathering evolution requirements, choosing scope (adjust members, change coordination pattern, split/merge teams), applying changes to the team file and CONFIG block, updating version metadata, and synchronizing the registry and cross-references. Use when a team's member roster is outdated, coordination pattern no longer fits, user feedback reveals workflow gaps, a specialized variant is needed alongside the original, or agents have been added or removed from the library affecting team composition.
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Configure Kubernetes Ingress networking with NGINX Ingress Controller, cert-manager for automated TLS certificate management, path-based routing, rate limiting, and multi-domain hosting with SSL termination and load balancing. Use when exposing multiple Kubernetes services via a single load balancer, implementing path-based or host-based routing, automating TLS certificate issuance with Let's Encrypt, or setting up blue-green and canary deployments with traffic splitting.
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Vue Router v4 (Vue 3 default) — route configuration, nested routes, dynamic segments, navigation guards, lazy loading, typed routes, route meta, programmatic navigation. Vue Router v3 (Vue 2) pointer only. Use this skill to: - Configure routes with createRouter + createWebHistory. - Use useRoute / useRouter composables in components. - Lazy-load route components for code splitting. - Implement auth guards via meta + beforeEach. - Type route params for safer access. Do NOT use this skill for: - General Vue conventions (see vue-conventions). - State management (see vue-state-management). - Forms (see vue-forms). - Testing routes (see vue-testing).
日本語の概要は準備中です。原文の説明を表示しています。
Entity Framework Core patterns for ASP.NET Core projects: DbContext design, code-first entity configuration (Fluent API via IEntityTypeConfiguration<T>), relations (HasOne/HasMany, cascade/restrict/set-null), indexes and unique constraints, projection to DTOs (Select + AsNoTracking), avoiding N+1 (Include, AsSplitQuery), transactions, parameterized raw SQL (FromSql with FormattableString), and connection string from IConfiguration. Works alongside aspnet-core-plugin:aspnet-conventions. Use this skill to: - Design a clean DbContext with ApplyConfigurationsFromAssembly for scalable entity registration. - Configure entity properties (column types, max length, precision, nullability) via Fluent API. - Avoid N+1 query problems with explicit Include / projection to DTOs. - Use transactions correctly for multi-entity operations. - Write parameterized raw SQL safely when LINQ is not expressive enough. Do NOT use this skill for: - Migration generation commands (that is efcore-specialist's job in the database extra phase). - ASP.NET Core middleware, DI, or validation — see aspnet-core-plugin:aspnet-conventions. - C# language idioms — see csharp-foundation:csharp-conventions.
日本語の概要は準備中です。原文の説明を表示しています。
Routing libraries for React SPAs: React Router v6/v7 (most common), TanStack Router (typed, modern), wouter (minimal). Detect what's installed and apply matching patterns. Lazy loading, navigation guards, typed params. Use this skill to: - Configure routes (declarative or file-based). - Use navigation hooks (useNavigate, useParams, useSearchParams). - Lazy-load routes for code splitting. - Implement protected routes / auth guards. - Type-safe params via Zod or framework's built-ins. Do NOT use this skill for: - General React conventions (see react-conventions). - State management (see react-state-management). - Form handling (see react-forms). - Next.js routing (different model — see nextjs-plugin).
日本語の概要は準備中です。原文の説明を表示しています。
React Native project structure, Expo vs bare workflow detection, app.json/app.config.js patterns, asset handling, fonts, styling approaches, hot-reload-friendly idioms. Use this skill to: - Detect Expo (managed/dev-client/EAS/ejected) vs bare RN workflow. - Pick correct project layout for each workflow. - Configure app.json / app.config.js with bundle ID, splash, icons, deep link schemes. - Handle assets, fonts, images correctly. - Pick a styling approach (StyleSheet / NativeWind / restyle / styled-components). Do NOT use this skill for: - Platform-specific branching (see rn-platform-specific). - Navigation (see rn-navigation). - Storage (see rn-state-and-storage). - Testing (see rn-testing).
日本語の概要は準備中です。原文の説明を表示しています。
State management and storage choice in React Native. State libs work mostly the same as web (Zustand, Jotai, Redux Toolkit, TanStack Query, Context); storage choice is RN-specific — AsyncStorage, MMKV, SecureStore, Keychain. Hydration patterns, splash screen handling, secrets discipline. Use this skill to: - Pick state management lib (matches web React intuition). - Pick storage by sensitivity (AsyncStorage / MMKV / SecureStore / Keychain). - Hydrate state on app start without UI flicker. - Persist Zustand store across app restarts. - Avoid storing secrets in plaintext storage. Do NOT use this skill for: - General project conventions (see rn-conventions). - Platform-specific code (see rn-platform-specific). - Navigation (see rn-navigation). - Testing state/storage (see rn-testing).
日本語の概要は準備中です。原文の説明を表示しています。
Spawn agents and verify their output — route work, never do it. Use when the work splits into pieces agents can carry and the session should route and check, not execute.
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Pythonic wrapper around RDKit with a simplified interface and sensible defaults. Preferred for standard drug discovery work — SMILES/SELFIES/InChI conversion, molecule standardization and sanitization, descriptors, ECFP and other fingerprints, Tanimoto distance matrices, Butina clustering and diverse subset picking, Bemis-Murcko scaffolds and scaffold splits, BRICS/RECAP fragmentation, 3D conformer generation, SDF/CSV/Excel and cloud I/O, and parallel processing via n_jobs. Returns native rdkit.Chem.Mol objects, so it composes with RDKit throughout. Also trigger on datamol, `import datamol as dm`, dm.to_mol, dm.standardize_mol, dm.cluster_mols, or dm.pick_diverse. For advanced control or custom parameters, use the rdkit skill directly.
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Query the FDA's public openFDA APIs for post-market drug data — FAERS adverse-event reports, Drugs@FDA approval and submission history, Structured Product Labels including boxed warnings, the National Drug Code directory, recall enforcement reports, and drug shortages. Use this skill to check what a regulator has already concluded about a molecule or its class, to date an approval and count its efficacy supplements, to read an approved indication or boxed warning, and to score a drug-event pair for disproportionate reporting with PRR, ROR, and chi-squared. Also trigger on openFDA, api.fda.gov, FAERS, Drugs@FDA, SPL, NDC, pharmacovigilance, boxed warning, adverse event report, drug recall, or safety signal.
日本語の概要は準備中です。原文の説明を表示しています。
Molecular ML with diverse featurizers and pre-built datasets. Use for property prediction (ADMET, toxicity) with traditional ML or GNNs when you want extensive featurization options and MoleculeNet benchmarks. Best for quick experiments with pre-trained models, diverse molecular representations. For ready-made ADMET numbers without training a model use admet-prediction; for benchmark datasets and task-aware splits use pytdc.
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Use when inspecting .debug_info or .debug_line sections with dwarfdump or readelf, working with split-DWARF .dwo files, setting up debuginfod, or checking how LTO and stripping affect debug info.
日本語の概要は準備中です。原文の説明を表示しています。