Exécutez n'importe quel Skill dans Manus
en un clic

Exécutez n'importe quel Skill dans Manus en un clic

drug-discovery-benchmarks

Étoiles1

Forks0

Mis à jour1 juillet 2026 à 23:14

Plan drug-discovery benchmark workflows with TDC-style datasets, ADMET, virtual screening, molecular property prediction, scaffold splits, leakage checks, and assay provenance. Use when evaluating compound models, benchmarks, or drug-discovery ML results.

Installation

Installer avec Codex ou Claude Copiez ce prompt, collez-le dans Codex, Claude ou un autre assistant, puis laissez-le vérifier la page du skill et l'installer pour vous.

Exécuter dans Manus

Source

BaranziniLab

BaranziniLab/biorouter-skills

Ouvrir le dépôt GitHub Voir les dépôts du créateur

Téléchargement

Exécuter dans Manus

Métiers associésSOC

Basé sur la classification professionnelle SOC

Biologistes, autresSciences de la vie, physiques et sociales·SOC 19-1029

SKILL.md

readonly

name	drug-discovery-benchmarks
description	Plan drug-discovery benchmark workflows with TDC-style datasets, ADMET, virtual screening, molecular property prediction, scaffold splits, leakage checks, and assay provenance. Use when evaluating compound models, benchmarks, or drug-discovery ML results.
user-invocable	true
license	Apache-2.0

Drug Discovery Benchmarks

Source adaptation: BioRouter-original synthesis inspired by remaining Scientific Agent Skills and Auto-Empirical Research Skills capability clusters, rewritten under Apache-2.0. This is not copied upstream text; it is a native BioRouter routing and verification checklist.

When to Use

Use this skill when the user asks about compound benchmarks, ADMET, virtual screening, scaffold splits, assay provenance, or drug-discovery ML. Prefer the most specific BioRouter extension, BioSkill, or language style skill whenever a concrete platform, package, or file type appears.

Intake

Before producing output, capture:

Research or operational question.
Data, platform, package, or source material involved.
Unit of analysis, model system, cohort, sample, or artifact.
Desired deliverable: plan, code, review, table, figure, protocol, or narrative.
Constraints: credentials, privacy, licensing, compute budget, validation standard, or external service availability.

BioRouter Routes

Use these routes when applicable:

Platform/API work: call the relevant BRXT extension first and keep mutation/execution gated.
Biomedical or omics data: load the relevant bio-skills category.
Statistical/causal claims: load causal-identification-gates, statistical-modeling-bayes, or econometrics-toolkit.
Evidence/citations: load citation-temporal-integrity and claim-evidence-integrity.
Reproducibility: load replication-package-audit before treating the result as final.

Quality Gates

Do not present final claims until these are true:

Provenance is explicit: source files, APIs, ids, versions, dates, or citations.
The method matches the data-generating process and the intended claim.
Assumptions and failure modes are listed plainly.
Validation is tied to an actual benchmark, holdout, sensitivity check, audit trail, or source record.
The output can be rerun, reviewed, or traced by another person.
Anything requiring external credentials, proprietary data, wet-lab execution, or live services is marked unverified unless actually checked.

Response Shape

Return:

Recommended route.
Minimal next action.
Verification checklist.
Risks or blockers.
Exact artifact to produce next, if any.

Plus depuis ce dépôt

même dépôt

computational-fluid-dynamics

BaranziniLab/biorouter-skills

Plan and review computational fluid dynamics workflows with meshes, boundary conditions, turbulence models, OpenFOAM/FluidSim-style solvers, validation cases, convergence checks, and reproducible simulation artifacts. Use when users mention CFD, fluid dynamics, Navier-Stokes, meshing, turbulence, OpenFOAM, or flow simulation.

2026-07-011

arbor-optimization

BaranziniLab/biorouter-skills

Run evaluator-driven hypothesis trees for improving research artifacts, prompts, analyses, and designs. Use when a task needs branch-and-prune exploration, rubric scoring, iterative refinement, or explicit comparison of competing scientific/workflow alternatives.

2026-07-011

autoskill-workflow-discovery

BaranziniLab/biorouter-skills

Detect repeated user workflows and draft native BioRouter skills, hooks, or checklist updates from observed work patterns. Use when users ask to turn repeated work, screen-history patterns, project routines, or recurring guardrails into reusable BioRouter skills.

2026-07-011

bids-neuroimaging

BaranziniLab/biorouter-skills

Plan and audit BIDS neuroimaging datasets, derivatives, validation, metadata, and reproducible preprocessing. Use when users mention BIDS, fMRI, MRI, EEG/MEG/iEEG, DICOM conversion, dataset_description.json, participants.tsv, or neuroimaging derivatives.

2026-07-011

clinical-ai-modeling

BaranziniLab/biorouter-skills

Plan and review clinical AI modeling workflows with EHR cohorts, PyHealth-style tasks, validation, bias checks, calibration, safety boundaries, and no-PHI handling. Use when users ask about clinical prediction models, risk scores, treatment-response ML, medical reports, or clinical model evaluation.

2026-07-011

general-time-series-forecasting

BaranziniLab/biorouter-skills

Plan and review general time-series forecasting with classical models, ML, foundation models, backtesting, leakage checks, and uncertainty. Use for non-omics forecasting, sensor streams, operations, finance, clinical vitals, demand, or irregular temporal data.

2026-07-011

name	drug-discovery-benchmarks
description	Plan drug-discovery benchmark workflows with TDC-style datasets, ADMET, virtual screening, molecular property prediction, scaffold splits, leakage checks, and assay provenance. Use when evaluating compound models, benchmarks, or drug-discovery ML results.
user-invocable	true
license	Apache-2.0

Drug Discovery Benchmarks

When to Use

Intake

Before producing output, capture:

Research or operational question.
Data, platform, package, or source material involved.
Unit of analysis, model system, cohort, sample, or artifact.
Desired deliverable: plan, code, review, table, figure, protocol, or narrative.
Constraints: credentials, privacy, licensing, compute budget, validation standard, or external service availability.

BioRouter Routes

Use these routes when applicable:

Platform/API work: call the relevant BRXT extension first and keep mutation/execution gated.
Biomedical or omics data: load the relevant bio-skills category.
Statistical/causal claims: load causal-identification-gates, statistical-modeling-bayes, or econometrics-toolkit.
Evidence/citations: load citation-temporal-integrity and claim-evidence-integrity.
Reproducibility: load replication-package-audit before treating the result as final.

Quality Gates

Do not present final claims until these are true:

Provenance is explicit: source files, APIs, ids, versions, dates, or citations.
The method matches the data-generating process and the intended claim.
Assumptions and failure modes are listed plainly.
Validation is tied to an actual benchmark, holdout, sensitivity check, audit trail, or source record.
The output can be rerun, reviewed, or traced by another person.
Anything requiring external credentials, proprietary data, wet-lab execution, or live services is marked unverified unless actually checked.

Response Shape

Return:

Recommended route.
Minimal next action.
Verification checklist.
Risks or blockers.
Exact artifact to produce next, if any.