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Synthesize target, ligand, mechanism, exposure, safety, pharmacogenomic, trial, and regulatory evidence for translational pharmacology questions. Use for target-drug-indication landscapes, not prescribing.
デフォルトでは、最初にソースを確認する Prompt が選択されています。直接コマンドに切り替えるか、ローカルコピーをダウンロードすることもできます。
インストールを決める前に、SKILL.md と SkillsMP に表示されている付属ファイルをお読みください。
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
直接コマンドでは確認用 Prompt が省略されます。実行前にソースを確認してください。
npx skills add https://github.com/eightmm/codex-science --skill translational-pharmacology-evidenceコマンドは1行のまま表示されます。コピー前に横へスクロールして全体を確認してください。
ローカルで確認しますか？SkillsMP が現在取得できるファイルをダウンロードできます。
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.
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.
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.
SOC 職業分類に基づく
SKILL.md を表示中
| name | translational-pharmacology-evidence |
| description | Synthesize target, ligand, mechanism, exposure, safety, pharmacogenomic, trial, and regulatory evidence for translational pharmacology questions. Use for target-drug-indication landscapes, not prescribing. |
| license | MIT |
Normalize target, compound form, mechanism, indication, population, route, regimen context, development stage, comparator, evidence cutoff, and the exact go or no-go decision; separate scientific, clinical, safety, and regulatory claims.
Build independent lanes for target-disease rationale, biochemical and cellular pharmacology, selectivity and off-targets, ADME and exposure, in-vivo translation, biomarkers and pharmacogenomics, human efficacy, safety, trials, and regulatory status; preserve assay endpoint, units, species or system, free concentration, exposure, uncertainty, result availability, and source date. Reconcile molecule identity and mechanism, distinguish primary results from registry or portal summaries, compare dose and exposure across systems only with justified scaling, and explicitly trace failures, terminated programs, class effects, and evidence dependence.
Return a claim-evidence matrix, target-compound-indication map, exposure-to-effect chain, competitor and trial table, safety liabilities, contradictions, confidence by claim, go or no-go criteria, and falsifiable evidence gaps.
Binding is not efficacy, potency is not exposure, preclinical activity is not human benefit, registration is not a result, and approval in another indication is not transferability; never give patient-specific advice, link every claim and registry state to $science-provenance, and run $science-review before a translational conclusion.
