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Deploy ShinyProxy for hosting multiple containerized Shiny applications. Covers ShinyProxy Docker deployment, application.yml configuration, Shiny app Docker images, authentication, container backends, usage tracking, and scaling. Use when hosting multiple Shiny apps behind a single entry point, needing per-app authentication and access control, deploying Shiny apps as isolated Docker containers, or scaling beyond single-app deployment with usage analytics and audit logging.

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Triage maintenance problems by severity, document findings with context, route to appropriate specialist agent or human, and create actionable issue reports. Use when a maintenance task encounters problems beyond automated cleanup: code that is unsafe to delete, configuration changes requiring domain expertise, breaking changes detected during cleanup, complex refactoring needed, or security-sensitive findings such as hardcoded secrets or vulnerabilities.

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Scaffold a new R package with complete structure including DESCRIPTION, NAMESPACE, testthat, roxygen2, renv, Git, GitHub Actions CI, and development configuration files (.Rprofile, .Renviron.example, CLAUDE.md). Follows usethis conventions and tidyverse style. Use when starting a new R package from scratch, converting loose R scripts into a structured package, or setting up a package skeleton for collaborative development.

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Scaffold a new puzzle type across all 10+ pipeline integration points in jigsawR. Creates the core puzzle module, wires it into the unified pipeline (generation, positioning, rendering, adjacency), adds ggpuzzle geom/stat layers, updates DESCRIPTION and config.yml, extends the Shiny app, and creates a comprehensive test suite. Use when adding a completely new puzzle type to the package or following the 10-point integration checklist to ensure nothing is missed end-to-end.

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Generate and optimise novel small molecules with REINVENT 4 — de novo sampling from a chemical language model, scaffold decoration with LibInvent, fragment linking with LinkInvent, and similarity-constrained analogue generation with Mol2Mol. Use this skill to set up reinforcement-learning or curriculum runs, compose a multi-parameter scoring function from docking scores, predictive models, and physicochemical desirability, and read the resulting sampled sets. Also trigger on REINVENT, LibInvent, LinkInvent, Mol2Mol, scaffold hopping, R-group replacement, linker design, chemical language model, or reinforcement-learning molecule optimisation.

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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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Turn in vitro potency and animal pharmacokinetics into a defensible human dose projection — the arithmetic that decides whether a compound can reach its target concentration safely. Use this skill for non-compartmental analysis of a concentration-time profile (AUC, Cmax, terminal half-life, clearance, volume of distribution), one- and two-compartment simulation of a dosing regimen, interspecies allometric scaling, human-equivalent dose conversion by body surface area, and the exposure margin between a projected therapeutic concentration and a toxicology no-effect level. Also trigger on non-compartmental analysis, AUC, clearance, volume of distribution, allometric scaling, human equivalent dose, first-in-human, NOAEL, therapeutic index, or exposure margin.

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Use Therapeutics Data Commons through the PyTDC Python package for registry discovery, approved dataset access, task-aware splits (scaffold, cold-start, temporal, combination), evaluator metrics, benchmark groups, and bounded molecular-oracle workflows. Use this skill to find which TDC datasets exist for a therapeutic task, load them with a split that does not leak, score predictions with the task's own official metric rather than a generic one, and run benchmark groups reproducibly. Also trigger on PyTDC, Therapeutics Data Commons, tdc.single_pred, tdc.multi_pred, ADMET Benchmark Group, scaffold split, get_split, or molecular oracles such as GSK3B, JNK3 and DRD2.

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5つの専門エージェントがディスカッション型で競合のビジネスモデル・機能・価格・弱点を網羅的に分析し、 差別化ポイントと参入戦略を導出するスキル。 エージェント同士が対話・批判し合いながら調査し、ca-data-critic がライブモデレーターとして矛盾を検出・収束を判定する。 Phase 1 で競合プロファイル・レビュー・機能をディスカッション型で調査し、 Phase 2 でポジショニング戦略家が統合。 Gate 1（中間合意形成）→ Gate 2（最終検証 + Independent Validation + Devil's Advocate）の2段階ゲートで品質を担保。 Use when: 競合を分析したい、差別化ポイントを見つけたい、 市場のプレイヤーを調べたい、ポジショニングを考えたい。 Triggers: "競合分析", "competitive analysis", "競合調査", "差別化", "ポジショニング", "competitor", "競合", "市場プレイヤー", "ライバル調査", "competitive landscape"

Produce a visible, evidence-backed privacy and security report for a local iOS app by scanning permission declarations, network code, dependencies, persistence, and risky APIs, while complementing the official Claude Code security-guidance plugin. Use after implementation, in a live security lane, or before claiming that an app is offline and account-free.

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Use when the user needs to deploy, operate, secure, or scale running infrastructure — cloud architecture (multi-region, VPC, FinOps), CI/CD and deployment strategies, secrets/IAM/supply-chain security, observability and SLO alerting, SRE and incident response, Kubernetes vs ECS and autoscaling, Terraform/Pulumi/CDK, or platform engineering and DORA metrics. Also for generating runbooks, pipeline configs, Terraform modules, or Kubernetes manifests. For choosing the system's architecture or stack before it runs (monolith vs microservices, which database), use software-architect.

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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.

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Framework-agnostic SQLAlchemy 2.0 core shared by fastapi-plugin and flask-plugin: declarative mapped classes with Mapped/mapped_column, column type selection, relationships with explicit lazy loading and cascades, 2.0-style select() querying, transaction/flush discipline, and Alembic-agnostic migration metadata rules. Framework plugins layer their delta skills (async sessions for FastAPI, Flask-SQLAlchemy integration for Flask) on top of this skill. Use this skill to: - Write SQLAlchemy 2.0 declarative models with Mapped[T] annotations and mapped_column(). - Pick correct column types (String(N), Numeric, DateTime(timezone=True), Uuid, Enum). - Define relationships with explicit lazy loading strategy and cascade settings. - Query with 2.0-style select() statements and manage flush vs commit boundaries. - Keep model metadata visible to Alembic autogenerate. Do NOT use this skill for: - FastAPI async engine/session lifecycle — see fastapi-plugin:sqlalchemy-patterns. - Flask-SQLAlchemy extension setup and Flask-Migrate — see flask-plugin:sqlalchemy-patterns. - Python idioms — see python-foundation:python-conventions.

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Generate a complete "constitution + documentation system" for a new autonomous, agent-run project — a content channel, an ebook press, an SEO tool-site, a web product, a casual game, or anything that should run itself with minimal human babysitting. Use when starting a brand-new self-running project and you want a CEO-orchestrated architecture (main agent → sub-agents → skills → CLI/MCP) with a named document system, a session lifecycle (start → work → finalize), and a growing identity/soul — scaffolded from a short structured interview. Triggers - "start a new autonomous project", "scaffold a project constitution", "set up an agent-run project", "generate a CLAUDE.md / AGENTS.md for a new project", "design the workflow for X", "bootstrap a self-running pipeline".

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Use when a user wants agent setup graded from conversation history. Not for skill fixing: use agent-surface-forge. Not for security scanning: use skill-scanner.

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Turn uploaded photos into full-canvas 3:4 impasto miniature-landscape posters. Use for sculptural oil-paint dioramas, tactile impasto scenes, or when this skill is invoked.

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Turn uploaded travel photos into full-canvas 3:4 pixel-dissolve landscapes with abundant paper white. Use for curated limited-palette pixel studies or when this skill is invoked.

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Initialize a local custom Elementor Widget plugin, design handoff workspace, and pending site-style context, choosing a Flat or Grouped plugin structure from expected Widget scale. Use once before the first custom Widget when no plugin scaffold exists; theme context is optional and read-only. Do not map Elementor Site Settings, design visuals, implement Widgets, deploy, or inspect a live WordPress installation.

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Research and analyze competitors to find positioning gaps and strategic opportunities. Use this skill whenever the user mentions competitors, competitive analysis, competitor teardown, market landscape, 'who else does this', 'how are we different', or competitor research. Also trigger when the user is entering a new market, when positioning feels weak or generic, when preparing for a launch, when any downstream skill needs competitors.md but it doesn't exist, or when the user asks about differentiation or market gaps. Even if the user just names a competitor casually ('what does X do?'), this skill likely applies. Three modes: Quick Scan, Deep Teardown, Gap Finder.

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Map who is winning an SEO market with measured data — recurring SERP domains, organic footprints, winning content themes, backlink authority, and the gaps worth attacking. Use this skill for market-level questions ("who ranks for X", "how competitive is this space", "where are the SEO openings") before committing to keyword targets or content themes. For one named competitor, use openseo-competitor-analysis instead. Triggers: "competitive landscape seo", "who ranks for", "seo market map", "how competitive is", "serp competitors".

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Brand-grounded Remotion video pipeline. Take a brief, ground it in `brand/` (voice, audience, positioning, creative-kit), write a beat sheet, scaffold a fresh Remotion project, generate any required assets, compose with frame-driven animations, and bake a final MP4. Use this skill whenever the user wants to build a NEW Remotion video from scratch — product films, motion graphics, code-driven shaders, CRT/glitch effects, HTML-in-canvas demos, React video. Triggers on requests like "make a remotion video", CRT/glitch/scanline effects, html-in-canvas demos, video shaders, WebGL video, and frame-driven React video. This is the mktg-native sibling of the personal `/cmo-video` skill: `/cmo-video` recreates the *style* of an existing reference video; `/cmo-remotion` builds something new from a brief. Always load `remotion-best-practices` before stage 5 (compose).

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Expert guidance for working with Wear OS Compose Material3. Use this skill when creating, updating or migrating Wear OS projects. This includes the androidx.wear.compose.material3, androidx.wear.compose.foundation and androidx.wear.compose.navigation3 libraries. Also working with core components such as AppScaffold, ScreenScaffold and TransformingLazyColumn. Migration from earlier versions such as Material 2.5 and Horologist.

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Review the UI and style files changed in the working tree against this repo's own design system, before they are saved or committed. Runs the same deterministic check as the CLI's --check flag (no tokens, about a second): hardcoded colours where a token exists, near-identical colour twins, off-scale spacing, radii, font sizes and shadows, typefaces outside the system, arbitrary Tailwind values, static inline styles, !important, duplicate component definitions and imports of the copy to avoid, new tokens that copy an existing one, chart colours written by hand where the repo keeps a chart palette (and one calm warning asking for a palette where it keeps none), on a repo built on MUI, Mantine, Chakra or Ant Design, colours or pixel sizes written onto kit components where the theme has a value, and on a Tailwind theme or a shadcn repo, palette classes such as text-gray-500 where the theme names a colour of that kind. Findings name the file, the line and the fix. This is the small, fast check for what just changed; the roast skill is the full scan of the whole repo with a report.

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Use this skill when the user wants a Chinese adult-oriented turn-based text GalGame GM with harem formation, freeform narrative roleplay, natural-escalation yellow-fiction text adventure, multi-character relationship drama, story memory, or novel export. Trigger on requests for 后宫养成, 后宫, 养成, 多女主, 多男后宫, 黄色爽文, 自然流YY文字游戏, 成人向文字冒险, 回合制文字冒险, GalGame, 后宫互动, 逐步升温剧情, 暧昧开局, 自定义身份卡, 文档记忆, 小说导出, 女性向剧情, or any Chinese freeform narrative game where the user wants adult consenting characters, gradual escalation, strong character routes, and long-running continuity.

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