'agent-skill' 검색 결과

Analyze drug concentration and response data to calculate exposure, fit models of drug movement and effects, and simulate dosing schedules. Produces exploratory research results with explicit assumptions and diagnostic checks.

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• Calculate exposure and half-life from concentration data
• Compare models of drug concentration over time
• Simulate dosing schedules for research

Analyze biological sequences and microbial communities in Python. Process and align DNA, RNA, or proteins, build evolutionary trees, calculate diversity, visualize sample differences, test group differences, and convert common biological file formats.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Calculate and compare microbiome diversity
• Align DNA, RNA, or protein sequences
• Build and compare evolutionary trees

Validate particle data and run RELION refinement for single-particle cryo-electron microscopy. Check independently refined half maps, calculate diagnostic map correlations, and perform soft-mask postprocessing while preserving execution logs and restart information.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Check particle metadata and image-stack consistency
• Run refinement on a selected particle population
• Inspect agreement between independent half maps

Create an editable, single-slide PowerPoint research poster from author-approved local text and images. Check physical dimensions, image resolution, contrast, reading order, and file security, with reports to support manual review and printing.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Create an editable PowerPoint conference poster
• Check print dimensions and image resolution
• Review contrast, reading order, and alternative text

Organize PyTorch models and data pipelines with PyTorch Lightning, automate training, save checkpoints, and track experiments. Includes templates and guidance for configuring training across multiple GPUs or TPUs.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Restructure PyTorch training code into LightningModules
• Build reusable data preparation and loading pipelines
• Configure checkpoints, early stopping, and experiment logs

Create research presentation slides with clear structure, concise text, figures, and timing guidance. The skill supports PowerPoint, LaTeX Beamer, and image-based PDFs, with workflows for preserving original research charts and reviewing slide layouts.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Prepare slides for a conference research talk
• Build a thesis or dissertation defense presentation
• Adapt paper content for a seminar presentation

Analyze gene-expression differences in bulk RNA-seq data using PyDESeq2. Validate count tables and experimental designs, test explicit comparisons, adjust for multiple testing, shrink effect estimates, and export statistical results with volcano and MA plots.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Compare gene expression between treatment and control groups
• Analyze expression differences while accounting for batch effects
• Validate count tables against sample metadata

Retrieve, validate, and export protocols.io experimental procedures while preserving version and attribution details. Check saved JSON offline, save protocol PDFs, or prepare reviewable plans for updates and publication; bundled scripts do not execute changes.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Retrieve a specific version of an experimental protocol
• Validate saved protocol JSON offline
• Export a protocol as a PDF

Create and review scientific figures with Matplotlib, Seaborn, or Plotly. The skill covers panel layouts, uncertainty, missing data, accessible color choices, and exported file checks, helping prepare figures for papers, presentations, or web publication.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Create multi-panel figures for a paper
• Show uncertainty and missing observations clearly
• Review palette contrast and color-independent cues

Plan and review PufferLib reinforcement learning workflows, where agents learn through rewards. Covers version-specific environment checks, parallel execution, training and evaluation plans, plus checkpoint inspection before loading saved models.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Choose APIs for a specific PufferLib version
• Check environment behavior before training
• Create training and evaluation plans with separate seeds

Inspect and transform local DICOM medical imaging files, including metadata, image frames, and compression formats. Generate technical inventories and preview images, check installed codecs, and review pseudonymized derivatives without treating those checks as proof of de-identification.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Create a technical inventory of local DICOM files
• Check compression support and frame memory needs
• Export a frame as a non-diagnostic preview

Research current web information with Parallel CLI and organize findings with supporting sources. The skill covers search, page extraction, detailed reports, adding sourced fields to existing lists, finding matching entities, and recurring monitoring of web changes.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Research current information and locate academic sources
• Extract content from public webpages and PDFs
• Add sourced information to company or product lists

Build Bayesian models in PyMC to estimate effects and quantify uncertainty. Fit regression or hierarchical models, check sampling diagnostics, assess predictions against observed data, predict new groups, and compare models using predictive performance.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Estimate regression effects with uncertainty
• Model differences across patients or groups
• Inspect sampling diagnostics and predictive checks

Validate and convert crystal and molecular structures, compare symmetry assignments, and build phase diagrams showing relative stability from local calculation data. Preserve sources and parameters while analyzing materials data or planning limited Materials Project queries.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Validating crystal structures in CIF files
• Comparing symmetry assignments across tolerances
• Converting structure files and checking information loss

Draft and revise scientific manuscripts while linking claims to verified evidence. The skill organizes source records, checks consistency across text and results, tracks reporting requirements, and reviews references and disclosures, with human verification and final approval.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Drafting manuscript sections from verified evidence
• Checking consistency across text, tables, and results
• Reviewing references and reporting checklist coverage

Plan and write literature reviews using multiple scholarly databases. Document searches, screen papers, synthesize findings, and check citations, then prepare Markdown or PDF reports with transparent selection counts, evidence limitations, and reproducible search records.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Prepare a literature review for a thesis or research paper
• Record reproducible searches across scholarly databases
• Track screening decisions and exclusion reasons

Turn supplied data or optional research into infographic drafts for reports, presentations, or social posts. Choose layouts and visual styles, generate PNG images, and use automated reviews to guide revisions while retaining review logs.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Turning report statistics into an infographic
• Creating a timeline of project milestones
• Comparing product features visually

Create, review, migrate, and simulate Python liquid-handling protocols for Opentrons Flex and OT-2 robots. Covers deck layout, labware, pipettes, modules, volumes, and tip policies, with checks for Opentrons App analysis and operator review before physical runs.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Draft liquid-transfer or serial-dilution protocols
• Review deck layout, volumes, and tip changes
• Update older protocols for supported API versions

Prepare and analyze genomic intervals, regions defined by chromosome coordinates, with Gtars. Validate BED files, compare overlaps and coverage, combine region sets, and plan tokenization or reference storage while tracking data provenance and resource limits.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Validating BED coordinates and reference assemblies
• Counting overlaps and comparing genomic region sets
• Converting genomic intervals into token IDs

Finds papers, preprints, citation links, and openly available full text through 18 scholarly APIs. Selects databases for the task and returns relevant records with search parameters, identifiers, and access dates so the retrieval can be repeated.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Find papers and preprints on a research topic
• Look up a paper by DOI, PMID, or arXiv ID
• Find openly available full text or PDFs

Look up biomedical relationships in ARAX using explicit entity types and tightly bounded paths. Inspect direct links or paths through one intermediate entity, along with returned publications and source records, to identify candidates for further research.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Checking recorded drug–gene relationships
• Exploring paths between a drug and a disease
• Inspecting publications and original data sources

Process satellite imagery and map data for geospatial research. Helps align coordinate systems and image grids, calculate vegetation and terrain measures, summarize data by area, and analyze road networks while preserving masks and source information.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Align coordinate systems and raster grids
• Calculate vegetation indices from satellite imagery
• Analyze slope and aspect from elevation data

Build Python workloads on Modal for cloud execution, including GPU model training and inference, web APIs, scheduled jobs, and parallel batch processing. Configure container environments, persistent storage, and automatic scaling through the Modal SDK.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Serving an AI model through a web API
• Running model training or fine-tuning on cloud GPUs
• Processing independent batch inputs in parallel

Optimize scientific Python workloads for NVIDIA GPUs by choosing suitable libraries and reducing bottlenecks. Compare outputs with the CPU version and measure complete processing time, including data transfers, to determine whether the GPU implementation provides a useful improvement.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Assess whether slow Python processing would benefit from a GPU
• Move NumPy or pandas workloads to suitable GPU libraries
• Validate GPU results against CPU outputs