Skip to main content
skillsmp

메뉴

전산 화학

분자 시뮬레이션, 신약 발견, 화학 분석을 위한 에이전트 스킬 탐색

카테고리, GitHub 별점, 출처의 최근 업데이트 시점은 카탈로그 구성과 정렬에 사용하는 신호입니다. SkillsMP의 추천, 품질 인증, 안전성 검토, 적합성 보증을 뜻하지 않습니다. 사용 전에 skill 상세 페이지와 출처를 확인하세요.

정렬 기준

결과 23,259개

ruview-advanced-sensing

출처ruvnet/RuView/plugins/ruview/skills/ruview-advanced-sensing/SKILL.md
업데이트

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 콘텐츠 언어: 영어

adaptyv

출처K-Dense-AI/scientific-agent-skills/skills/adaptyv/SKILL.md
업데이트

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 콘텐츠 언어: 영어

mageck

출처K-Dense-AI/scientific-agent-skills/skills/mageck/SKILL.md
업데이트

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 콘텐츠 언어: 영어

pkpd-modeling

출처K-Dense-AI/scientific-agent-skills/skills/pkpd-modeling/SKILL.md
업데이트

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 콘텐츠 언어: 영어

primer-design

출처K-Dense-AI/scientific-agent-skills/skills/primer-design/SKILL.md
업데이트

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 콘텐츠 언어: 영어

rowan

출처K-Dense-AI/scientific-agent-skills/skills/rowan/SKILL.md
업데이트

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 콘텐츠 언어: 영어

tellurium

출처K-Dense-AI/scientific-agent-skills/skills/tellurium/SKILL.md
업데이트

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 콘텐츠 언어: 영어

pixijs-scene-particle-container

출처pixijs/pixijs/skills/pixijs-scene-particle-container/SKILL.md
업데이트

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 콘텐츠 언어: 영어

math-olympiad

출처anthropics/claude-plugins-official/plugins/math-olympiad/skills/math-olympiad/SKILL.md
업데이트

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 콘텐츠 언어: 영어

analyzing-campaign-attribution-evidence

출처mukul975/Anthropic-Cybersecurity-Skills/skills/analyzing-campaign-attribution-evidence/SKILL.md
업데이트

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 콘텐츠 언어: 영어