| name | ai-domain-biotech-research-acceleration-skill-2026 |
| description | Despliega soluciones de IA para biotech research acceleration con arquitectura modular, metricas auditables y decisiones alineadas al contexto del dominio. |
| version | 1.0.0 |
| domain | domain-ai |
| quality_tier | advanced |
| compatibility | ["claude-code","codex"] |
| owner | yonatanguerrerosoriano |
| tags | ["domain-ai","industry-ai","automation","decision-systems","2026"] |
| foundation_skills | ["optimization-foundations","probability-foundations","statistics-inference-foundations","testing-verification-foundations","security-threat-modeling-foundations","debugging-causal-reasoning-foundations"] |
Ai Domain Biotech Research Acceleration Skill 2026 Skill
Mission
Despliega soluciones de IA para biotech research acceleration 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
- Understand scope, assumptions, and risks.
- Execute the workflow in a deterministic order.
- 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-foundationsprobability-foundationsstatistics-inference-foundationstesting-verification-foundationssecurity-threat-modeling-foundationsdebugging-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-biotech-research-acceleration-skill-2026 skill to handle this task end-to-end."
- "Run ai-domain-biotech-research-acceleration-skill-2026 and produce a production-ready output with validation notes."
