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Create a new agent definition file following the agent-almanac agent template and registry conventions. Covers persona design, tool selection, skill assignment, model choice, frontmatter schema, required sections, registry integration, and discovery symlink verification. Use when adding a new specialized agent to the library, defining a persona for a Claude Code subagent, or creating a domain-specific assistant with curated skills and tools.

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Build a feature store using Feast for centralized feature management, configure offline and online stores for batch and real-time serving, define feature views with transformations, and implement point-in-time correct joins for ML pipelines. Use when managing features for multiple ML models, ensuring training-serving consistency, serving low-latency features for real-time inference, reusing feature definitions across projects, or building a feature catalog for discovery and governance.

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Build a custom MCP (Model Context Protocol) server that exposes domain-specific tools to AI assistants. Covers server implementation in Node.js or R, tool definitions, the resources and prompts primitives, transport configuration, and testing with Claude Code. Use when you need to expose custom functionality beyond what mcptools provides, when building specialized domain-specific AI integrations, or when wrapping existing APIs or services as MCP tools.

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Si attiva quando l'utente chiede di scrivere, redigere, tradurre o revisionare testi italiani per uso pubblico, editoriale, blog, social, newsletter, marketing, narrativa, saggistica o divulgazione. Si attiva anche quando l'utente chiede di umanizzare testi italiani, eliminare pattern AI, correggere lessico da chatbot, catturare o calibrare la voce di un autore su un corpus di suoi testi, rispondere a un rilevatore automatico che ha segnalato come generato un testo scritto da una persona, o quando rivede testi italiani generati da modelli linguistici.

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Design intentional internal link graphs for SEO, UX, and LLM-readable sites. Use this skill when the user asks for links between short briefs, longform articles, topics, projects, author pages, breadcrumbs, source trails, related stories, orphan-page checks, overlink checks, anchor intent, or validation that a link graph reinforces the canonical content model without creating duplicate story bodies or hidden links.

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Convert a semantic core into crawlable SEO and LLM-friendly information architecture. Use this skill when the user asks for URL structure, page taxonomy, canonical rules, hreflang groups, page roles, pillar/topic/news/article/blog/project separation, multilingual route strategy, or a durable content model such as one short brief plus one longform article. It prevents duplicate public content bodies and hands link-graph work to internal-link-graph-architect.

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Deep, iterative, multi-source research that explores a topic from multiple perspectives, detects contradictions, and produces a comprehensive vault note with fact-level citations and a confidence map. Use this skill whenever the user says /deep-research, "deep research X", "research X thoroughly", "investigate X deeply", "I need to really understand X", or wants research that goes beyond a quick web search. Also use when the user wants to understand a complex, contested, or multi-faceted topic, compare competing approaches with evidence, or build a comprehensive mental model they can trust for decisions. This is heavier than /research — it searches web, academic papers, Reddit, and the vault, then loops through exploration and critique rounds until the evidence base is solid. When in doubt between /research and /deep-research, prefer this one if the topic is complex, contested, or the user wants depth over speed.

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Audits and optimizes the five global Claude layers (User Preferences, Styles, Global Memory, Skills, MCP Connectors) using the Global Layer Scorecard (six dimensions, anchored 1-5 rubrics). Detects eight cross-layer failure modes and produces evolutionary recommendations (Promotion, Demotion, Codification, Skill Extraction). Use when user says "audit my global setup," "optimize my preferences," "review my Claude configuration," "check my cross-project setup," "are my preferences working," "clean up my global memory," or "what should be in my preferences vs my project." Also use when a user has 3+ Projects and wants to improve their shared foundation. Do NOT use for single-Project audits, Project Memory optimization, or full-stack audits (use rootnode-project-audit, rootnode-memory-optimization, or rootnode-full-stack-audit respectively, if available). Run on Opus 5 or Sonnet 5 at `high` effort (both defaults); depth reduces on legacy models.

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Comprehensive audit of a user's entire Claude environment — combines Project audit (six-dimension Project Scorecard, seven anti-patterns) with Global audit (six-dimension Global Layer Scorecard) plus Cross-Layer Alignment Check across all nine layers and Evolutionary Recommendations across four pathways. Produces a unified action plan. Use when user says "full audit of everything," "audit my entire Claude setup," "full stack audit," "comprehensive review of my project and preferences," "check everything," or wants the complete health check of both a specific Project AND their global configuration. Also use when a project audit reveals cross-layer issues that require full-stack visibility. Do NOT use for auditing only a Project (use rootnode-project-audit if available) or only global layers (use rootnode-global-audit if available). Do NOT use for single prompt evaluation (use rootnode-prompt-validation if available). Run on Opus 5 or Sonnet 5 at `high` effort (both defaults); depth reduces on legacy models.

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Audits and scores Claude Projects using a six-dimension Scorecard with anchored 1-5 rubrics and detects seven structural anti-patterns. When global layer info is provided, adds cross-layer alignment findings. Use when user says "audit my project," "review my custom instructions," "score my project," "evaluate my Claude project," "improve my system prompt," "what's wrong with my project," "why is my project underperforming." Also trigger on symptom-phrased: "my project doesn't work right," "Claude is inconsistent in my project," "my Project used to work and now doesn't." Also use when the user pastes Custom Instructions asking why output is poor or generic. Do NOT use when the user's primary request is a global-layer audit (use rootnode-global-audit if available), single-prompt evaluation (use rootnode-prompt-validation if available), or Memory-only optimization (use rootnode-memory-optimization if available). Run on Opus 5 or Sonnet 5 at `high` effort (both defaults); depth reduces on legacy models.

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Builds complete, optimized Claude prompts and Project scaffolds from task descriptions. Use when building, designing, creating, or structuring a Claude prompt or system prompt. Trigger on: "build me a prompt," "compile a prompt for," "create a system prompt," "write a Claude prompt," "design a prompt for," "help me write a prompt," "scaffold a Claude Project," "design a Project for." Uses a four-stage methodology (Parse, Select, Construct, Validate) and a tested 5-layer architecture (Identity, Objective, Context, Reasoning, Output + Quality Control). In Project Mode, the Compiler is global-layer-aware — checks User Preferences, installed Skills, and MCP Connectors to avoid redundancy and flag gaps. Do NOT use for auditing existing prompts (use rootnode-prompt-validation if available) or diagnosing existing Projects (use rootnode-project-audit if available). Run on Opus 5 or Sonnet 5 at `high` effort (both defaults); depth reduces on legacy models.

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Generalised autonomous optimisation loop — soft RLVR for any artifact a user can measure. Use this skill whenever a user wants to iteratively improve an artifact — code, prompts, documents, configs, designs, content — by running structured experiments, evaluating results against a multi-dimensional rubric, and learning from each attempt. Triggers include: "optimise this", "keep improving until it's good", "run experiments on", "autoresearch", "iterate on this overnight", "try different approaches and pick the best", or any request implying repeated evaluate-and-improve cycles. Also use when the user wants to improve a system prompt, a data pipeline, a writing style, or any artifact where quality can be decomposed into measurable tracks. For inference optimisation tasks (model latency, throughput, quantization, GPU deployment), a* delegates the low-level tuning to AITune while maintaining quality tracking and learning.

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Vue 3 SFC structure, Composition API + <script setup>, file naming, project layout, props/emits/slots typing, defineModel, composables, lifecycle, watchers, UI library detection. Vue 2 fallback notes. Use this skill to: - Structure a new SFC with `<script setup>`, template, scoped styles. - Type props/emits/slots correctly via macros. - Pick `ref` vs `reactive` (prefer `ref`). - Build composables that compose cleanly. - Apply lifecycle hooks correctly. - Detect UI library (Vuetify/Quasar/PrimeVue/Naive UI/Element Plus/shadcn-vue) and mirror its patterns. Do NOT use this skill for: - State management lib choice (see vue-state-management). - Routing (see vue-routing). - Forms (see vue-forms). - Testing (see vue-testing).

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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).

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ASP.NET Core web framework conventions: Minimal API vs MVC controllers, Program.cs composition, DI lifetimes (Scoped/Singleton/Transient), Options pattern with IOptions<T>, middleware ordering (HTTPS redirect → routing → authentication → authorization), model binding and validation (FluentValidation / DataAnnotations), ProblemDetails error handling, structured logging with ILogger<T>, configuration layering (appsettings.json + environment variables + User Secrets), and health checks. Works alongside csharp-foundation:csharp-conventions and aspnet-core-plugin:efcore-patterns. Use this skill to: - Compose Program.cs correctly — register services, configure middleware in the right order, map endpoints. - Apply the Options pattern to avoid passing raw IConfiguration into services. - Write Minimal API endpoint groups with typed results and authorization. - Handle cross-cutting errors uniformly with ProblemDetails. - Configure structured logging and health checks for production readiness. Do NOT use this skill for: - EF Core entity configuration and migrations — see aspnet-core-plugin:efcore-patterns. - C# language idioms — see csharp-foundation:csharp-conventions. - Testing — see csharp-foundation:dotnet-testing.

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Modern Java idioms for any JVM project (Java 17+): records, sealed types, text blocks, pattern matching, Optional discipline, streams, immutability, null safety, package layout, var usage, and code organisation. Apply when the project is a Java 17+ project. Stack-agnostic — referenced by every Java plugin in the marketplace. Use this skill to: - Write self-documenting, immutable-by-default value types with records. - Model closed type hierarchies with sealed classes and interfaces. - Handle optionality explicitly with Optional instead of returning null. - Write expressive pipelines with Stream API without performance footguns. - Lay out packages consistently (feature-first or layer-first) and keep class responsibilities tight. Do NOT use this skill for: - Framework-specific idioms (Spring annotations, JPA mappings — those live in framework plugin skills). - Build tooling (Maven/Gradle) — see java-foundation:build-tooling. - Testing patterns — see java-foundation:jvm-testing.

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Testing strategies for Next.js: Server Components, Client Components, Server Actions, Route Handlers, end-to-end with Playwright. Vitest/Jest configuration, React Testing Library patterns, msw for network mocks. Use this skill to: - Pick the right test layer for what you're testing. - Configure Vitest or Jest for Next.js. - Test Server Components without running them in isolation. - Test Server Actions and Route Handlers as pure functions. - Set up Playwright for e2e and integration of RSC. Do NOT use this skill for: - General Next.js conventions (see nextjs-conventions). - RSC vs Client model (see server-component-patterns). - Plain Node.js test patterns (see nodejs-plugin equivalents).

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Angular forms: Reactive Forms (preferred — typed FormGroup/FormControl since Angular 14, FormBuilder, custom + async validators, FormArray, multi-step) and Template-driven (`[(ngModel)]` + FormsModule). Validation strategies, server error mapping, accessibility. Use this skill to: - Build Reactive Forms with typed FormGroup/FormControl. - Use FormBuilder для concise syntax. - Implement custom synchronous and async validators. - Wire FormArray for dynamic field lists. - Map server errors back to form fields. - Pick Reactive vs Template-driven (prefer Reactive). Do NOT use this skill for: - General conventions (see angular-conventions). - State management beyond forms (see angular-state-and-rx). - Routing (see angular-routing). - Testing forms (see angular-testing).

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React Server Components vs Client Components in Next.js: the boundary discipline, "use client" / "use server" directives, Server Actions, what serializes across the boundary, common pitfalls. Use this skill to: - Decide whether a component should be RSC or Client. - Push the "use client" boundary as deep as possible. - Implement Server Actions correctly (auth, validation, revalidation). - Compose RSC and Client components without breaking the model. - Pass data across the boundary safely. Do NOT use this skill for: - General Next.js conventions (see nextjs-conventions). - Specific data-fetching APIs and caching (see nextjs-data-fetching). - Routing (see nextjs-routing).

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Flask web framework conventions: app factory pattern with create_app(), Blueprint registration with url_prefix, MethodView for class-based API views, Marshmallow schema validation, WTForms for HTML forms, Flask-Login for session auth, flask-jwt-extended for stateless API auth, Jinja2 template conventions, error handlers, and extension initialization. Activated automatically by flask-plugin/stack.md. Use this skill to: - Structure Flask applications with the app factory and per-feature Blueprints. - Validate JSON request data with Marshmallow and HTML forms with WTForms. - Implement session-based auth with Flask-Login or token auth with flask-jwt-extended. - Render Jinja2 templates safely or return JSON responses for API mode. - Register global error handlers for consistent error responses. Do NOT use this skill for: - SQLAlchemy ORM model patterns and Flask-Migrate — see flask-plugin:sqlalchemy-patterns. - Python language idioms — see python-foundation:python-conventions. - Testing patterns — see python-foundation:pytest-testing.

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Database conventions and patterns. Use when working on database schemas, migrations, or data modeling.

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Comprehensive primary skill for agents working with Weights & Biases. Covers both the W&B SDK (training runs, metrics, artifacts, sweeps) and the Weave SDK (GenAI traces, evaluations, scorers). Includes helper libraries, gotcha tables, and data analysis patterns. Use this skill whenever the user asks about W&B runs, Weave traces, evaluations, training metrics, loss curves, model comparisons, or any Weights & Biases data — even if they don't say "W&B" explicitly.

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Choose, configure, launch, and collect bounded subagents for research and engineering decisions. Root agents and delegation-capable subagents should read this before delegating: every task requires an explicit model tier, and high-leverage work such as research ideation, round planning, plateau pivots, large research reviews, hard optimization, disputed evidence, and expensive experiment portfolios requires frontier judgment.

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This skill retrieves upcoming earnings announcements for US stocks using the Financial Modeling Prep (FMP) API. Use this when the user requests earnings calendar data, wants to know which companies are reporting earnings in the upcoming week, or needs a weekly earnings review. The skill focuses on mid-cap and above companies (over $2B market cap) that have significant market impact, organizing the data by date and timing in a clean markdown table format. Supports multiple environments (CLI, Desktop, Web) with flexible API key management.

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