SciDER

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, splices the generated figures from Step 2, and merges everything into the template that already contains Intro + Related Work from Step 3. TRIGGER when the orchestrator delegates Step 4 or when the user asks to "write the methodology and experiments sections" or "fill in the rest of the paper".

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.

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 biological experimental data.

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.

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.

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 design.

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.

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 data.

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, reproducible results.

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.

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 numerical analysis.

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.

Graph and network analysis — graph construction, centrality measures, community detection, graph ML basics, and visualization. Use when data has relational/network structure.

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.

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.

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.

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 social, behavioral, or economic data.

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 quantification.

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.

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(...)`.

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, not symbolic. TRIGGER when the orchestrator delegates Step 5 or when the user asks to "refine the draft", "iterate on the paper", or "run peer review on this paper".

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 paperId), build a BibTeX file, and draft Introduction + Related Work using ≥90% of the verified pool. Runs in parallel with the plotting-agent. TRIGGER when the orchestrator delegates Step 3 or when the user asks to "find citations for my paper", "draft the related work", or "build the bibliography".

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 writing plan with citation hints. TRIGGER when the orchestrator delegates Step 1 or when the user asks to "outline a paper from raw materials" or "generate the paper structure".

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 and compiled PDF. TRIGGER when the user asks to "write a paper from my experiments", "turn this idea and these results into a paper", "generate a conference submission", "run paper-orchestra on X", or otherwise wants the end-to-end paper-writing pipeline. Coordinates the outline-agent, plotting-agent, literature-review-agent, section-writing-agent, and content-refinement-agent skills.

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 captions. Runs in parallel with the literature-review-agent. TRIGGER when the orchestrator delegates Step 2 or when the user asks to "generate the figures for my paper" or "render the plots from this experiment log".

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.

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.

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.

Evaluate scientific claims and evidence quality, assess experimental design validity, identify biases and confounders, and apply evidence grading frameworks (GRADE, Cochrane Risk of Bias).

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.

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.

UV package manager for fast Python dependency management and virtual environments. Use when installing packages, managing dependencies, or setting up virtual environments.