cx-modeling-problem-execution

Drive a concrete scientific modeling problem from supplied inputs through model selection, falsifiable plan, one-time approval, environment setup, smoke test, full execution, downstream analysis, provenance, and review. Use when the user provides sequences, structures, molecules, omics data, trajectories, or a specific modeling objective and expects an actual result rather than instructions.

Ir para a instalação

Repositório: eightmm/codex-science
Última atividade na origem: 10 de julho de 2026 às 13:08
Idioma detectado do SKILL.md: inglês
Estrelas: 2
Forks: 0

Opções de instalação

Por padrão, está selecionado o prompt que primeiro revisa a origem. Você pode mudar para um comando direto ou baixar uma cópia local.

Instalar com Codex ou Claude Copie este prompt, cole no Codex, Claude ou outro assistente e deixe que ele revise a página da skill e instale para você.

Um comando direto ignora o prompt de revisão. Verifique a origem antes de executá-lo.

npx skills add https://github.com/eightmm/codex-science --skill cx-modeling-problem-execution

O comando permanece em uma só linha. Role horizontalmente para revisá-lo antes de copiar.

Prefere uma cópia local? Baixe os arquivos disponíveis atualmente no SkillsMP.

Revise os arquivos de origem

Leia o SKILL.md e os arquivos complementares exibidos pelo SkillsMP antes de decidir se vai instalar.

Explorador de arquivos

2 arquivos

SKILL.md

Instruções da origem · Visualização somente leitura

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quantitative-research

Execute auditable quantitative research across preregistered study design, statistical inference, numerical verification, dimensional analysis, uncertainty propagation, counterexample search, and formal proof. Use when a scientific or mathematical question needs actual computation and machine-readable evidence rather than only explanation.

2026-08-04

statistical-decision-analysis

Compare finite actions under uncertainty with explicit states, loss, utility or risk-neutral payoff, priors, experiment likelihoods, and a declared decision criterion. Use for Bayes risk, expected utility or payoff, pure-action minimax, minimax regret, dominance, posterior decision policies, EVPI/EVSI, experiment-cost decisions, and prior-threshold sensitivity; do not use for ordinary statistical testing, causal identification, strategic games, or MDP/POMDP/RL planning.

2026-08-04

cx-statistical-decision-analysis

2026-08-04

Ver todas deste repositório

Cx Modeling Problem Execution

Use this Codex adapter for the pinned upstream scientific skill.

Catalog status

Status: active

Source: cx

Audit reasons: none

Upstream license: MIT

Inventory: ../../inventory.json

Upstream instructions: ../../../authored-skills/modeling-problem-execution/SKILL.md

Procedure

Resolve the plugin root as three directories above this skill directory.

Read the matching inventory record and verify its status, reasons, source path, and pinned revision.

Read the upstream instructions only after confirming this record is still active in the current inventory.

Treat upstream frontmatter as source metadata, not as Codex configuration. Follow the instruction body only where it is compatible with available Codex tools and the current user-approved plan.

Treat tools named for another agent runtime as capability requests. Map them to an available Codex tool only when the behavior is equivalent; otherwise report the unavailable capability.

Ask before credentials, package installation, new network hosts, remote compute, writes, paid services, destructive actions, or imported executable code.

Use $science-provenance for produced scientific outputs and $science-review before presenting scientific claims.

Boundaries

This wrapper does not grant credentials, network access, package installation, or script execution.

Do not change an inactive record to active from inside a research task.

Preserve failures, uncertainty, source identifiers, and licensing constraints.

name	cx-modeling-problem-execution
description	Drive a concrete scientific modeling problem from supplied inputs through model selection, falsifiable plan, one-time approval, environment setup, smoke test, full execution, downstream analysis, provenance, and review. Use when the user provides sequences, structures, molecules, omics data, trajectories, or a specific modeling objective and expects an actual result rather than instructions.