科学計算

科学シミュレーション、計算研究、データサイエンス向けエージェントスキルを閲覧。複雑な科学的問題を解決。

ゲノムデータベースへのクイック検索、配列検索、BLAST スタイルの検索、エンリッチメントチェック、および再現可能なバイオインフォマティクス証拠ログのための gget CLI および Python ワークフロー。

gget CLI and Python workflow for quick genomic database queries, sequence lookup, BLAST-style searches, enrichment checks, and reproducible bioinformatics evidence logs.

segment-anything-model

NousResearch/hermes-agent

SAM: zero-shot image segmentation via points, boxes, masks.

Deletes memories by search query or memory ID with confirmation before removal. Use when removing outdated information, incorrect memories, sensitive data, or cleaning up after experiments.

K-Dense-AI/scientific-agent-skills

Comprehensive molecular biology toolkit. Use for sequence manipulation, file parsing (FASTA/GenBank/PDB), phylogenetics, and programmatic NCBI/PubMed access (Bio.Entrez). Best for batch processing, custom bioinformatics pipelines, BLAST automation. For quick lookups use gget; for multi-service integration use bioservices.

K-Dense-AI/scientific-agent-skills

Constraint-based metabolic modeling (COBRA). FBA, FVA, gene knockouts, flux sampling, SBML models, for systems biology and metabolic engineering analysis.

K-Dense-AI/scientific-agent-skills

NGS analysis toolkit. BAM to bigWig conversion, QC (correlation, PCA, fingerprints), heatmaps/profiles (TSS, peaks), for ChIP-seq, RNA-seq, ATAC-seq visualization.

glycoengineering

K-Dense-AI/scientific-agent-skills

Analyze and engineer protein glycosylation. Scan sequences for N-glycosylation sequons (N-X-S/T), predict O-glycosylation hotspots, and access curated glycoengineering tools (NetOGlyc, GlycoShield, GlycoWorkbench). For glycoprotein engineering, therapeutic antibody optimization, and vaccine design.

K-Dense-AI/scientific-agent-skills

Query the Precision Medicine Knowledge Graph (PrimeKG) for multiscale biological data including genes, drugs, diseases, phenotypes, and more.

K-Dense-AI/scientific-agent-skills

Analyze, validate, convert, and transform materials structures and computed materials data with current pymatgen APIs, including local phase diagrams, symmetry sensitivity, electronic-structure I/O, and explicitly bounded Materials Project queries.

K-Dense-AI/scientific-agent-skills

Python/HTSlib workflows for genomic files. Use when reading, querying, filtering, or writing SAM/BAM/CRAM, VCF/BCF, FASTA/FASTQ, or tabix data with pysam, including pileup, coverage, indexing, and CRAM references.

K-Dense-AI/scientific-agent-skills

Build, inspect, test, and analyze bounded process-based discrete-event simulations with SimPy, including events, resources, interrupts, monitoring, replications, warm-up, and reproducible output analysis.

davila7/claude-code-templates

Expert data scientist for advanced analytics, machine learning, and statistical modeling. Handles complex data analysis, predictive modeling, and business intelligence.

davila7/claude-code-templates

Primary Python toolkit for molecular biology. Preferred for Python-based PubMed/NCBI queries (Bio.Entrez), sequence manipulation, file parsing (FASTA, GenBank, FASTQ, PDB), advanced BLAST workflows, structures, phylogenetics. For quick BLAST, use gget. For direct REST API, use pubmed-database.

davila7/claude-code-templates

Constraint-based metabolic modeling (COBRA). FBA, FVA, gene knockouts, flux sampling, SBML models, for systems biology and metabolic engineering analysis.

cosmic-database

davila7/claude-code-templates

Access COSMIC cancer mutation database. Query somatic mutations, Cancer Gene Census, mutational signatures, gene fusions, for cancer research and precision oncology. Requires authentication.

davila7/claude-code-templates

NGS analysis toolkit. BAM to bigWig conversion, QC (correlation, PCA, fingerprints), heatmaps/profiles (TSS, peaks), for ChIP-seq, RNA-seq, ATAC-seq visualization.

davila7/claude-code-templates

Phylogenetic tree toolkit (ETE). Tree manipulation (Newick/NHX), evolutionary event detection, orthology/paralogy, NCBI taxonomy, visualization (PDF/SVG), for phylogenomics.

exploratory-data-analysis

davila7/claude-code-templates

Perform comprehensive exploratory data analysis on scientific data files across 200+ file formats. This skill should be used when analyzing any scientific data file to understand its structure, content, quality, and characteristics. Automatically detects file type and generates detailed markdown reports with format-specific analysis, quality metrics, and downstream analysis recommendations. Covers chemistry, bioinformatics, microscopy, spectroscopy, proteomics, metabolomics, and general scientific data formats.

davila7/claude-code-templates

Parse FCS (Flow Cytometry Standard) files v2.0-3.1. Extract events as NumPy arrays, read metadata/channels, convert to CSV/DataFrame, for flow cytometry data preprocessing.

davila7/claude-code-templates

Framework for computational fluid dynamics simulations using Python. Use when running fluid dynamics simulations including Navier-Stokes equations (2D/3D), shallow water equations, stratified flows, or when analyzing turbulence, vortex dynamics, or geophysical flows. Provides pseudospectral methods with FFT, HPC support, and comprehensive output analysis.

davila7/claude-code-templates

Access Human Metabolome Database (220K+ metabolites). Search by name/ID/structure, retrieve chemical properties, biomarker data, NMR/MS spectra, pathways, for metabolomics and identification.

davila7/claude-code-templates

Mass spectrometry analysis. Process mzML/MGF/MSP, spectral similarity (cosine, modified cosine), metadata harmonization, compound ID, for metabolomics and MS data processing.

davila7/claude-code-templates

Comprehensive biosignal processing toolkit for analyzing physiological data including ECG, EEG, EDA, RSP, PPG, EMG, and EOG signals. Use this skill when processing cardiovascular signals, brain activity, electrodermal responses, respiratory patterns, muscle activity, or eye movements. Applicable for heart rate variability analysis, event-related potentials, complexity measures, autonomic nervous system assessment, psychophysiology research, and multi-modal physiological signal integration.

davila7/claude-code-templates

Materials science toolkit. Crystal structures (CIF, POSCAR), phase diagrams, band structure, DOS, Materials Project integration, format conversion, for computational materials science.

davila7/claude-code-templates

Python interface to OpenMS for mass spectrometry data analysis. Use for LC-MS/MS proteomics and metabolomics workflows including file handling (mzML, mzXML, mzTab, FASTA, pepXML, protXML, mzIdentML), signal processing, feature detection, peptide identification, and quantitative analysis. Apply when working with mass spectrometry data, analyzing proteomics experiments, or processing metabolomics datasets.

Read Solid-backed table atoms inside JSX, createMemo, createEffect, or table.Subscribe; own slices with Solid signals or external TanStack Store atoms; and apply value-or-updater callbacks correctly. Load for Solid tracking and controlled-state bugs.

detecting-t1055-process-injection-with-sysmon

mukul975/Anthropic-Cybersecurity-Skills

Detect process injection techniques (T1055) including classic DLL injection, process hollowing, and APC injection by analyzing Sysmon events for cross-process memory operations, remote thread creation, and anomalous DLL loading patterns.

implementing-ot-network-traffic-analysis-with-nozomi

mukul975/Anthropic-Cybersecurity-Skills

Deploy Nozomi Networks Guardian sensors for passive OT network traffic analysis to achieve comprehensive asset visibility, real-time threat detection, and vulnerability assessment across industrial control systems without disrupting operations, leveraging behavioral anomaly detection and protocol-aware monitoring.

performing-malware-persistence-investigation

mukul975/Anthropic-Cybersecurity-Skills

Systematically investigate all persistence mechanisms on Windows and Linux systems to identify how malware survives reboots and maintains access.

phuryn/pm-skills

Generate realistic dummy datasets for testing with customizable columns, constraints, and output formats (CSV, JSON, SQL, Python script). Use when creating test data, building mock datasets, or generating sample data for development and demos.

danielmiessler/LifeOS

The scientific method as a universal problem-solving algorithm — goal-first, plural falsifiable hypotheses, designed experiments, and honest measurement, scaling from TDD to feature validation to MVP launch. USE WHEN think about, figure out, experiment, iterate, optimize, hypothesis, science, full cycle, quick diagnosis, structured investigation, how do we test, analyze results. NOT FOR multi-angle lens passes (use IterativeDepth).

aiming-lab/AutoResearchClaw

Validate a COBRApy genome-scale metabolic model for mass/charge balance, stoichiometric consistency, biomass producibility, dead-end metabolites, thermodynamic loops, and GPR rule formatting. Outputs a structured validation report with errors and warnings.

Parse and interpret Z3 output for human consumption. Handles models, unsat cores, proofs, statistics, and error messages. Translates solver internals into plain-language explanations.

companion-inc/feynman

Use Borzoi-style regulatory genomics models for sequence-to-expression or variant-effect analysis. Use when the task asks for noncoding variant impact, regulatory sequence design, or expression prediction.

ncbi-datasets-skill

Submit compact NCBI Datasets v2 requests for assembly, genome, taxonomy, and related metadata endpoints. Use when a user wants concise NCBI Datasets summaries; save raw JSON or text only on request.

proteomexchange-skill

Submit compact ProteomeXchange PROXI requests for datasets, libraries, peptidoforms, proteins, PSMs, spectra, and USI examples. Use when a user wants concise PROXI summaries

parcadei/Continuous-Claude-v3

Problem-solving strategies for interpolation in numerical methods

lakesoul-vector

lakesoul-io/LakeSoul

LakeSoul 向量索引模块 —— IVF+RaBitQ 索引构建、增量插入、ANN 检索。涉及 lakesoul-vector, lakesoul-io, python 三层。当用户询问向量索引、相似度检索、IVF、RaBitQ 相关问题时使用。

brycewang-stanford/Auto-Empirical-Research-Skills

Data science methodology for Python research: EDA, validation, causal inference (IV, DiD, RD, synthetic control), clustering/PCA/UMAP, supervised ML, geospatial, visualization. Method selection guidance. For syntax, load tool-specific skills.

brycewang-stanford/Auto-Empirical-Research-Skills

Statistical modeling: OLS/WLS/GLS, GLM (logit, probit, Poisson), time series (ARIMA, VAR), mixed effects, diagnostics. Formula API. Use for regressions without fixed effects, GLMs, or time series. For FE/DiD use pyfixest; panel/IV use linearmodels.

statistical-software-qa

brycewang-stanford/Auto-Empirical-Research-Skills

Quality assurance and testing protocols for statistical software

ncbi-datasets-api

brycewang-stanford/Auto-Empirical-Research-Skills

Access genomes, genes, and taxonomy data via NCBI Datasets v2 API

retrosynthesis-guide

brycewang-stanford/Auto-Empirical-Research-Skills

Retrosynthetic analysis and computational reaction prediction

symbolic-computation-guide

brycewang-stanford/Auto-Empirical-Research-Skills

Computer algebra systems: SymPy, SageMath, and Mathematica for research

computational-physics-guide

brycewang-stanford/Auto-Empirical-Research-Skills

Computational physics methods, simulations, and research tools

brycewang-stanford/Auto-Empirical-Research-Skills

PubMed Central OAI-PMH metadata harvesting

meta-synthesis-guide

brycewang-stanford/Auto-Empirical-Research-Skills

Conduct qualitative meta-synthesis and evidence synthesis methods