jonatangs777/ai-skill-agent-control-deck-2026/mathematics-convex-optimization-toolkit-skill-2026

Mathematics Convex Optimization Toolkit Skill 2026 Skill

Mission

Desarrolla soluciones matematicas avanzadas para convex optimization toolkit con rigor formal, validacion reproducible y decisiones auditables.

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

• logic-propositional-reasoning
• logic-proof-strategies
• set-theory-foundations
• relations-functions-foundations
• complexity-analysis-foundations
• testing-verification-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 mathematics-convex-optimization-toolkit-skill-2026 skill to handle this task end-to-end."
• "Run mathematics-convex-optimization-toolkit-skill-2026 and produce a production-ready output with validation notes."