Skip to main content
skillsmp

メニュー

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

カテゴリ、GitHubスター、配布元の最終更新時刻は、カタログの整理と表示順に使う指標です。SkillsMPによる推奨、品質認定、安全性審査、用途への適合保証ではありません。利用前にskillの詳細ページと配布元を確認してください。

並び順

9,852件の結果

glycoengineering

ソースK-Dense-AI/scientific-agent-skills/skills/glycoengineering/SKILL.md
更新日

Analyzes and engineers protein glycosylation by scanning canonical N-glycosylation sequons, describing S/T-rich regions, checking curated glycan evidence, and preparing NetNGlyc, NetOGlyc and GlycoSHIELD workflows. Use for glycoprotein engineering, antibody Fc glycosylation, glycan shielding, and site-specific glycoproteomics interpretation.

Skill コンテンツの言語： 英語

hugging-science

ソースK-Dense-AI/scientific-agent-skills/skills/hugging-science/SKILL.md
更新日

Discovers and evaluates scientific datasets, models, methodology posts, and Spaces through the Hugging Science catalog. Used when selecting scientific ML resources in biology, chemistry, genomics, materials, climate, physics, astronomy, medicine, mathematics, protein design, single-cell analysis, or PDE modeling, and when checking their actual datasets, Transformers, native-runtime, Inference Providers, or Gradio interfaces.

Skill コンテンツの言語： 英語

marine-carbonate-chemistry

ソースK-Dense-AI/scientific-agent-skills/skills/marine-carbonate-chemistry/SKILL.md
更新日

Solves seawater carbonate chemistry with PyCO2SYS for chemical oceanography, ocean acidification, and marine carbon-cycle research. Use for paired total alkalinity, dissolved inorganic carbon, pH, or seawater pCO2/fCO2 measurements; carbonate speciation; aragonite and calcite saturation; Revelle factors; lab-to-in-situ temperature and pressure corrections; and measurement uncertainty propagation. Applies to carbonate-system calculations, not general aqueous speciation or air-sea gas-flux estimation.

Skill コンテンツの言語： 英語

pyopenms

ソースK-Dense-AI/scientific-agent-skills/skills/pyopenms/SKILL.md
更新日

Processes mass spectrometry data with pyOpenMS. Supports proteomics and metabolomics workflows—feature detection, peptide/protein identification, label-free quantification, adduct/accurate-mass annotation, and complex LC-MS/MS pipelines. Supports extensive file formats and algorithms. For simple spectral comparison and small-molecule library matching use matchms.

Skill コンテンツの言語： 英語

detecting-stuxnet-style-attacks

ソースmukul975/Anthropic-Cybersecurity-Skills/skills/detecting-stuxnet-style-attacks/SKILL.md
更新日

Detects sophisticated cyber-physical attacks that follow the Stuxnet pattern of modifying PLC logic while spoofing sensor readings to hide the manipulation, using PLC logic integrity monitoring (Claroty xDome, Nozomi Guardian) and physics-based process anomaly detection. Use when hunting for IT-to-OT lateral movement or discrepancies between PLC program state and physical process behavior in ICS/SCADA environments.

Skill コンテンツの言語： 英語

detecting-t1055-process-injection-with-sysmon

ソースmukul975/Anthropic-Cybersecurity-Skills/skills/detecting-t1055-process-injection-with-sysmon/SKILL.md
更新日

Detect process injection techniques (T1055) - including DLL injection, process hollowing, and APC injection - by analyzing Sysmon Event IDs 1, 7, 8, 10, and 25 for cross-process memory operations, remote thread creation, and anomalous DLL loads. Use when hunting defense-evasion activity that hides code inside legitimate processes, investigating an EDR alert on suspicious cross-process access, or validating Sysmon coverage for injection detection.

Skill コンテンツの言語： 英語

performing-static-malware-analysis-with-pe-studio

ソースmukul975/Anthropic-Cybersecurity-Skills/skills/performing-static-malware-analysis-with-pe-studio/SKILL.md
更新日

Performs static analysis of Windows PE malware samples using PEStudio to examine file headers, imports, strings, and resources without executing the binary, identifying packing, anti-analysis tricks, and malicious imports. Use for pre-execution triage of a suspicious Windows executable before sandbox detonation.

Skill コンテンツの言語： 英語

exploratory-data-analysis

ソースdavila7/claude-code-templates/cli-tool/components/skills/scientific/exploratory-data-analysis/SKILL.md
更新日

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.

Skill コンテンツの言語： 英語

fluidsim

ソースdavila7/claude-code-templates/cli-tool/components/skills/scientific/fluidsim/SKILL.md
更新日

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.

Skill コンテンツの言語： 英語

neurokit2

ソースdavila7/claude-code-templates/cli-tool/components/skills/scientific/neurokit2/SKILL.md
更新日

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.

Skill コンテンツの言語： 英語

pyopenms

ソースdavila7/claude-code-templates/cli-tool/components/skills/scientific/pyopenms/SKILL.md
更新日

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.

Skill コンテンツの言語： 英語

novelty

ソースdanielmiessler/LifeOS/LifeOS/install/skills/Novelty/SKILL.md
更新日

Evolutionary explanation-discovery engine for hard-to-vary causal theories of unknown phenomena. USE WHEN develop a new theory, explain a mystery, solve an explanatory gap, discover a mechanism, consciousness, origin, or scientific breakthrough. Also use for multi-cycle creative exploration. NOT FOR a single-pass divergent brainstorm (use BeCreative) or delivery validation alone (use Assay).

Skill コンテンツの言語： 英語

mujoco-python

ソースgoogle-deepmind/mujoco/doc/skills/python/SKILL.md
更新日

Python simulation lifecycle (MjSpec, MjModel, MjData), simulation stepping (mj_step, mj_step1, mj_step2, mj_forward), named access (data.body, model.geom), bind(), multi-threaded CPU batch simulation (mujoco.batch.Batch, mujoco.rollout), optimization and system ID (mujoco.minimize, mujoco.sysid, mjd_transitionFD), contact and touch sensors, sensordata, keyframe resets, integrators, mju_ spatial math (quaternions, poses), energy diagnostics. Use for Python physics simulation, state access, CPU batching, stepping loops, sensors. Do NOT use for model building/editing (mujoco-spec-editing), headless rendering (mujoco-rendering), GPU/TPU (mujoco-accelerated), cross-engine parity debugging (mujoco-parity), or GUI/viewers (mujoco-gui, mujoco-studio).

Skill コンテンツの言語： 英語

exploratory-data-analysis

ソースzLanqing/codex-claude-academic-skills/scientific-toolkit-skill/references/scientific-skills/exploratory-data-analysis/SKILL.md
更新日

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.

Skill コンテンツの言語： 英語