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Found 2,940 Skills
Produces a margin-by-product table and three pricing-scenario data views so the owner can see the full financial picture before making a pricing decision. Accepts optional product name argument.
Use this skill when > Conduct a comprehensive plan or design stress-test through systematic questioning. Interviews relentlessly about every decision point, resolves interdependencies progressively, and traverses the complete decision tree. Use when thoroughly vetting a proposal, design, or architecture before committing to it.
Use when testing, reviewing, pressure-testing, refining, packaging, or validating agent skills for academic research workflows before installing or relying on them.
Use when asked to install, deploy, run, validate, troubleshoot, or stop NVIDIA AI-Q Blueprint infrastructure.
Use when the user asks to "improve my agent", "self-improving agent", "auto-tune my agent", "iterate on my agent prompt", "fix my agent based on test results", "close the loop on agent quality", "auto-improve agent prompt", "use eval results to improve agent", "optimize my prompt based on failures", "rewrite my prompt", or describes agent self-improvement, prompt iteration from run results, or automated agent quality loops. Covers the full diagnose → propose → apply → re-validate loop for VAPI agents (squads + tool definitions) and for self-hosted agents (custom websocket servers, including the offline / pasted-prompt degenerate variant).
Validar prompts dirigidos a agentes de IA (Claude Code, Cursor, Copilot, etc.) contra reglas de redacción efectiva. Calcular un porcentaje de efectividad del prompt y devolver sugerencias de mejora concretas, más una propuesta de prompt reescrito. Cubre verbos no imperativos, lenguaje conversacional, acciones vagas, términos subjetivos, alcance difuso, prohibiciones implícitas, intenciones múltiples y nombres genéricos. Las reglas de detalle técnico (alcance, nombres exactos) se aplican solo a prompts de implementación; en prompts funcionales (user stories, descripciones de comportamiento) se marcan N/A. Usar siempre que el usuario pida validar, revisar, auditar, mejorar, corregir o "pulir" un prompt antes de enviarlo a un agente, o cuando pegue un prompt y pida feedback sobre cómo está redactado.
Generate a requirements-quality checklist for an OpenSpec change and run it against the proposal/design/tasks/delta — validating completeness, clarity, consistency, and testability BEFORE you approve the spec. Modeled on GitHub Spec-Kit's /checklist. Use when reviewing a spec at the human approval gate, or before launching implementation, to catch under-specified requirements early.
Verify whether the analysis units, replication levels, statistical methods, and result reports in the research are consistent, and do not treat report review as re-analysis. Use when the user asks for "check statistical reports", "verify n and replicate experiments", "review statistical methods and results", or requests the rw-statistics-audit workflow. Runs without a private local workspace or preset research-lab; use user-provided material and bundled public-source methods.
Use when a Lightning Web Component data need is described in ambiguous natural language — turn "get contact info" or "show account data" into a clear, PRD-ready data-requirements spec. TRIGGER when the user says "define data requirements for this LWC", "turn this PRD data section into validated object/field names", "recommend GraphQL vs UIAPI for this data need", "validate these Salesforce API names", or "spec out the LDS adapter for this component", or references LWC bundle files (`.js`, `.js-meta.xml`) whose data layer is not yet specified. DO NOT TRIGGER when the data layer is already fully specified, when authoring the actual query or adapter code from a known spec, or when implementing an LWC end-to-end (use experience-lwc-generate).
Designs a tailored multi-product agentic data science architecture on Google Cloud that incorporates opinionated best practices. Use when architecting multi-product solutions for agent-based data analytics or ML workloads. Don't use for simple queries, non-agentic pipelines, general cloud reviews, or writing agent code.
Structured guide for setting up A/B tests with mandatory gates for hypothesis, metrics, and execution readiness.
LLM prompt testing, evaluation, and CI/CD quality gates using Promptfoo. Invoke when: - Setting up prompt evaluation or regression testing - Integrating LLM testing into CI/CD pipelines - Configuring security testing (red teaming, jailbreaks) - Comparing prompt or model performance - Building evaluation suites for RAG, factuality, or safety Keywords: promptfoo, llm evaluation, prompt testing, red team, CI/CD, regression testing