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SkillsMP는 leonardodalinky/SciDER에서 32개의 skill을 수집했습니다. skill을 열어 소스와 세부 정보를 확인하세요.
수집된 skill 32개 중 32개를 표시합니다.
Step 4 of the PaperOrchestra pipeline (arXiv:2604.05018). ONE single multimodal LLM call that drafts the remaining paper sections (Abstract, Methodology, Experiments, Conclusion), extracts numeric values from experimental_log.md into LaTeX booktabs tables,…
원문 언어: 영어
Bioinformatics workflows — RNA-seq and scRNA-seq analysis pipelines, enrichment analysis (GO/KEGG/GSEA), variant interpretation, protein structure analysis, and key database queries. Use when analyzing genomic, transcriptomic, or proteomic data.
원문 언어: 영어
Experimental and ecological biology — experimental design with controls/replicates, biology-specific statistical tests, diversity indices, cell biology assays (IC50, ELISA, flow cytometry), imaging analysis, and survival analysis. Use when working with…
원문 언어: 영어
Causal inference methods — DAG-based causal thinking, distinguishing observational from experimental data, IV, DiD, RDD, propensity score matching, and sensitivity analysis. Use when making causal claims from data.
원문 언어: 영어
Cheminformatics and computational chemistry — SMILES/InChI parsing, molecular property prediction, spectroscopy interpretation, DFT workflow, materials characterization (XRD, SAXS), and key chemistry databases. Use when analyzing chemical or materials data.
원문 언어: 영어
CS theory for research — algorithm complexity analysis, data structure selection, rigorous benchmarking discipline, distributed systems fundamentals, and formal verification concepts. Use when reasoning about algorithmic correctness, efficiency, or system…
원문 언어: 영어
Computer vision workflows — image data characterization, preprocessing and augmentation, architecture selection (CNN vs ViT), and evaluation metrics (mAP, IoU, FID, SSIM). Use when working with image or video data.
원문 언어: 영어
Engineering systems analysis — control theory (PID, transfer functions, Bode plots), signal processing, reliability engineering, engineering optimization (LP/MIP), sensor data processing, and FEA concepts. Use when working with engineering, control, or sensor…
원문 언어: 영어
Controlled experiment design — hypothesis formation, statistical power and sample size estimation, confound control, ablation structure, baseline selection, and reproducibility requirements. Use when designing research experiments to ensure valid,…
원문 언어: 영어
Materials science analysis — crystal structure, phase diagrams, mechanical and electronic properties, characterization techniques (XRD, SEM/TEM, AFM, XPS), and high-throughput computational workflows with pymatgen. Use when working with materials data.
원문 언어: 영어
Formal mathematics for scientific computing — symbolic computation (sympy), numerical linear algebra, optimization (convex/non-convex), information theory, and numerical precision issues. Use when working with mathematical derivations, proofs, or rigorous…
원문 언어: 영어
Model evaluation framework — cross-validation strategies, metric selection by task type, baseline requirements, ablation design, and train/val/test discipline. Use for all ML experiments before reporting results.
원문 언어: 영어
Graph and network analysis — graph construction, centrality measures, community detection, graph ML basics, and visualization. Use when data has relational/network structure.
원문 언어: 영어
Text data analysis for NLP research — dataset characterization, preprocessing decision guide, tokenization choices, embedding selection, and evaluation metrics (BLEU, ROUGE, BERTScore, perplexity). Use when analyzing or processing text datasets.
원문 언어: 영어
Physics simulation and computational analysis — ODE/PDE solvers, molecular dynamics, Monte Carlo methods, signal processing, error propagation, and unit management. Use when working with physical simulation data or running computational physics experiments.
원문 언어: 영어
Publication-quality figure guidelines for scientific papers — DPI, font sizes, colorblind-friendly palettes, plot type selection, multi-panel layout, uncertainty representation, and statistical annotation. Use when creating figures for papers or reports.
원문 언어: 영어
Social science and economics methods — survey analysis (Cronbach's alpha, factor analysis), econometrics (OLS diagnostics, robust SEs), panel data, behavioral experiments, administrative data linkage, and program evaluation (ITT vs LATE). Use when analyzing…
원문 언어: 영어
Advanced statistical methods — Bayesian inference with PyMC/MCMC, mixed-effects models, resampling (bootstrap, permutation), spatial statistics, and missing data handling. Use when standard frequentist tests are insufficient or you need full uncertainty…
원문 언어: 영어
Time series data analysis — stationarity testing, decomposition, autocorrelation, forecasting method selection, temporal cross-validation, and evaluation metrics (MAPE, RMSE, MASE). Use when working with sequential temporal data.
원문 언어: 영어
Find pretrained models on HuggingFace Hub by task, keyword, size, or downloads. Use when you need a base checkpoint for fine-tuning (LoRA/PEFT/AFT) or a zero-shot baseline. Returns model IDs ready for `AutoModel.from_pretrained(...)`.
원문 언어: 영어
Step 5 of the PaperOrchestra pipeline (arXiv:2604.05018). Iteratively refine drafts/paper.tex by simulating peer review and applying targeted revisions, with strict accept/revert halt rules. Maintains a worklog and snapshots each iteration so revert is real,…
원문 언어: 영어
Step 3 of the PaperOrchestra pipeline (arXiv:2604.05018). Execute the literature search strategy from outline.json — discover candidate papers via web search, verify them through Semantic Scholar (Levenshtein > 70 fuzzy title match, temporal cutoff, dedup by…
원문 언어: 영어
Step 1 of the PaperOrchestra pipeline (arXiv:2604.05018). Convert (idea.md, experimental_log.md, template.tex, conference_guidelines.md) into a strict JSON outline containing a plotting plan, literature search plan (Intro + Related Work), and section-level…
원문 언어: 영어
Orchestrate the full PaperOrchestra (Song et al., 2026, arXiv:2604.05018) five-agent pipeline to turn unstructured research materials (idea, experimental log, LaTeX template, conference guidelines, optional figures) into a submission-ready LaTeX manuscript…
원문 언어: 영어
Step 2 of the PaperOrchestra pipeline (arXiv:2604.05018). Execute the visualization plan from outline.json — render plots and conceptual diagrams from experimental_log.md and idea.md, optionally refine via VLM critique loop, and produce context-aware…
원문 언어: 영어
Comprehensive EDA on scientific data files — structure, content, quality, and characteristics analysis across 200+ formats. Use when analyzing any data file to understand its structure, quality, and downstream analysis recommendations.
원문 언어: 영어
Low-level plotting library for full customization. Use when you need fine-grained control over every plot element, novel plot types, or publication-quality PNG/PDF/SVG export. For quick statistical plots use seaborn.
원문 언어: 영어
Creative research ideation and exploration for open-ended brainstorming, interdisciplinary connections, challenging assumptions, and identifying research gaps. Best for early-stage research planning without specific observations yet.
원문 언어: 영어
Evaluate scientific claims and evidence quality, assess experimental design validity, identify biases and confounders, and apply evidence grading frameworks (GRADE, Cochrane Risk of Bias).
원문 언어: 영어
Statistical visualization with pandas integration. Use for quick exploration of distributions, relationships, and categorical comparisons. Best for box plots, violin plots, pair plots, heatmaps. Built on matplotlib.
원문 언어: 영어
Guided statistical analysis with test selection, assumption checking, power analysis, and APA-formatted reporting. Use when choosing appropriate tests or formatting results for academic reporting.
원문 언어: 영어
UV package manager for fast Python dependency management and virtual environments. Use when installing packages, managing dependencies, or setting up virtual environments.
원문 언어: 영어