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计算化学

浏览分子模拟、药物发现和化学分析的 agent skills

分类、GitHub Stars 和来源更新时间只用于组织与排序目录，不代表 SkillsMP 背书、质量认证、安全审核或适用性保证。使用前请进入详情页检查来源。

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ruview-advanced-sensing

来源ruvnet/RuView/plugins/ruview/skills/ruview-advanced-sensing/SKILL.md
更新时间

Advanced RuView capabilities — RuvSense multistatic sensing (attention-weighted fusion, geometric diversity, persistent field model), cross-viewpoint fusion across multiple nodes, RF tomography (ISTA L1 solver, voxel grids), longitudinal biomechanics drift, pre-movement intention signals, adversarial signal detection, and multistatic mesh security hardening. Use for research-grade or multi-node deployments.

Skill 内容语言： 英语

adaptyv

来源K-Dense-AI/scientific-agent-skills/skills/adaptyv/SKILL.md
更新时间

Uses the Adaptyv Bio Foundry API and Python SDK to design protein characterization experiments, estimate costs, submit sequences, monitor laboratory progress, and retrieve results. Applies to Adaptyv Foundry, its target catalog, binding screening and affinity assays, thermostability, expression, fluorescence, epitope binning, and enzyme activity workflows, including code using adaptyv or FoundryClient.

Skill 内容语言： 英语

mageck

来源K-Dense-AI/scientific-agent-skills/skills/mageck/SKILL.md
更新时间

Analyzes pooled CRISPR screen FASTQ reads and guide-count matrices with MAGeCK, validates guide libraries and contrasts, measures replicate and library QC, and produces gene hit rankings with effect sizes and FDR. Use for new knockout, CRISPRi, or CRISPRa screen analysis, enrichment or depletion contrasts, and MAGeCK count/test workflows; existing public dependency-score lookup belongs to DepMap.

Skill 内容语言： 英语

pkpd-modeling

来源K-Dense-AI/scientific-agent-skills/skills/pkpd-modeling/SKILL.md
更新时间

Pharmacokinetic and pharmacodynamic modelling and simulation - non-compartmental analysis, compartmental and population PK, PK/PD and exposure-response, TMDD, PBPK orientation, bioequivalence, allometric scaling and first-in-human dose, drug interaction prediction, and Bayesian therapeutic drug monitoring. Use when analysing concentration-time data, deriving exposure metrics, fitting PK or PD models, or evaluating dosing regimens. Triggers include "pharmacokinetics", "pharmacodynamics", "PK/PD", "NCA", "non-compartmental", "AUC", "Cmax", "lambda z", "half-life", "clearance", "volume of distribution", "compartmental model", "population PK", "popPK", "NONMEM", "nlmixr2", "Pharmpy", "Monolix", "exposure-response", "Emax", "EC50", "indirect response", "effect compartment", "TMDD", "PBPK", "bioequivalence", "RSABE", "ABEL", "allometric scaling", "first-in-human", "MABEL", "drug-drug interaction", "DDI", "ICH M12", "concentration-QTc", "therapeutic drug monitoring", "MIPD", and "dosing regimen".

Skill 内容语言： 英语

primer-design

来源K-Dense-AI/scientific-agent-skills/skills/primer-design/SKILL.md
更新时间

Designs and audits PCR and RT-qPCR primers with Primer3, explicit thermodynamic conditions, reference-based off-target amplification searches, and traceable sequence coordinates. Use for designing primer pairs, checking existing primers, exon-junction or isoform-specific assays, variant masking, cloning tails, multiplex compatibility, and interpreting Primer-BLAST results. Includes bounded local in-silico PCR and BLAST screening; distinguishes computational candidates from experimentally validated assays.

Skill 内容语言： 英语

rowan

来源K-Dense-AI/scientific-agent-skills/skills/rowan/SKILL.md
更新时间

Rowan is a cloud-native molecular modeling and medicinal-chemistry workflow platform with a Python API. Use for pKa and macropKa prediction, conformer and tautomer ensembles, docking and analogue docking, protein-ligand cofolding, MSA generation, molecular dynamics, permeability, descriptor workflows, and related small-molecule or protein modeling tasks. Ideal for programmatic batch screening, multi-step chemistry pipelines, and workflows that would otherwise require maintaining local HPC/GPU infrastructure.

Skill 内容语言： 英语

tellurium

来源K-Dense-AI/scientific-agent-skills/skills/tellurium/SKILL.md
更新时间

Simulates biochemical kinetic models from SBML or Antimony with Tellurium and libRoadRunner, checks model units, compares deterministic parameter perturbations, and exports and replays SBML plus SED-ML COMBINE archives. Use for reaction-network time courses, kinetic parameters, concentration dynamics and reproducible simulation experiments; steady-state constraint-based metabolic flux analysis belongs to cobrapy.

Skill 内容语言： 英语

pixijs-scene-particle-container

来源pixijs/pixijs/skills/pixijs-scene-particle-container/SKILL.md
更新时间

Use this skill when rendering thousands of lightweight sprites in PixiJS v8. Covers ParticleContainer with Particle instances, addParticle/removeParticle, particleChildren array, dynamicProperties (vertex, position, rotation, uvs, color), boundsArea, roundPixels, update, inherited alpha/tint/blendMode from ancestors. Triggers on: ParticleContainer, Particle, IParticle, addParticle, particleChildren, dynamicProperties, boundsArea, particle effects, particle alpha, particle tint, fade particles, constructor options, ParticleContainerOptions, ParticleOptions.

Skill 内容语言： 英语

math-olympiad

来源anthropics/claude-plugins-official/plugins/math-olympiad/skills/math-olympiad/SKILL.md
更新时间

Solve competition math problems (IMO, Putnam, USAMO, AIME) with adversarial verification that catches the errors self-verification misses. Activates when asked to 'solve this IMO problem', 'prove this olympiad inequality', 'verify this competition proof', 'find a counterexample', 'is this proof correct', or for any problem with 'IMO', 'Putnam', 'USAMO', 'olympiad', or 'competition math' in it. Uses pure reasoning (no tools) — then a fresh-context adversarial verifier attacks the proof using specific failure patterns, not generic 'check logic'. Outputs calibrated confidence — will say 'no confident solution' rather than bluff. If LaTeX is available, produces a clean PDF after verification passes.

Skill 内容语言： 英语

analyzing-campaign-attribution-evidence

来源mukul975/Anthropic-Cybersecurity-Skills/skills/analyzing-campaign-attribution-evidence/SKILL.md
更新时间

Systematically evaluate cyber-campaign evidence to attribute an operation to a threat actor, using the Diamond Model and Analysis of Competing Hypotheses (ACH) to weigh infrastructure overlaps, TTP consistency, malware code similarity, and timing/language artifacts into confidence-weighted attribution assessments. Use when an incident investigation needs a defensible attribution confidence level.

Skill 内容语言： 英语