ai-domain-research-lab-orchestration-skill-2026

# Ai Domain Research Lab Orchestration Skill 2026 Skill ## Mission Despliega soluciones de IA para research lab orchestration con arquitectura modular, metricas auditables y decisiones alineadas al contexto del dominio. ## When to use - When the user asks for a repeatable workflow in this domain. - When a specialized checklist improves speed or quality. ## Inputs expected - Task objective and expected output. - Relevant files, paths, or system constraints. - Any non-negotiable requirements (security, style, deadlines). ## Workflow 1. Understand scope, assumptions, and risks. 2. Execute the workflow in a deterministic order. 3. Verify outcomes and report any limitations clearly. ## Output contract Provide results in this order: key outcome, concrete changes, validation status, next steps. ## Guardrails - Never fabricate facts, outputs, or tool results. - Ask for confirmation before destructive operations. - Prefer minimal, reversible changes when uncertain. ## Foundations - `optimization-foundations` - `probability-foundations` - `statistics-inference-foundations` - `testing-verification-foundations` - `security-threat-modeling-foundations` - `debugging-causal-reasoning-foundations` ## Logical reliability checklist - Assumptions are explicit and separated from verified facts. - The solution path is justified with clear reasoning steps. - Edge cases and contradiction checks are included. - Output is testable, auditable, and reversible when possible. ## Example prompts - "Apply the ai-domain-research-lab-orchestration-skill-2026 skill to handle this task end-to-end." - "Run ai-domain-research-lab-orchestration-skill-2026 and produce a production-ready output with validation notes."