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Ai Domain Biotech Research Acceleration Skill 2026

skill-jonatangs777-ai-skill-agent-control-deck-2026-ai-domain-biotech-research-acceleration-skill-2026 · by JonatanGS777

Despliega soluciones de IA para biotech research acceleration con arquitectura modular, metricas auditables y decisiones alineadas al contexto del dominio.

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Install

$ agentstack add skill-jonatangs777-ai-skill-agent-control-deck-2026-ai-domain-biotech-research-acceleration-skill-2026

✓ scanned · ✓ verified — works with Claude Code, Cursor, and more.

Security review

✓ Passed

No issues found. Passed automated security review. · v0.1.0 How review works →

✓ Prompt-injection patterns
✓ Secret / credential exfiltration
✓ Dangerous shell & filesystem operations
✓ Untrusted network calls
✓ Known-malicious package signatures

What it can access

✓ Network access No
✓ Filesystem access No
✓ Shell / process execution No
✓ Environment & secrets No
✓ Dynamic code execution No

From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.

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About

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-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-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."

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

Author: JonatanGS777
Source: JonatanGS777/ai-skill-agent-control-deck-2026
License: MIT
Homepage: https://github.com/JonatanGS777/ai-skill-agent-control-deck-2026

Install and usage instructions live in the source repository linked above.

Reviews

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Versions

v0.1.0 Imported from the upstream source.