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KalarisLabs/research-agent-skills

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AI アシスタントでインストール

このプロンプトをコピーして、使用中の AI アシスタントに送ってください。

リポジトリの README を読み込んでいます…

Research Agent Skills: choose a research playbook

Kalaris Labs' collection brings together task-specific playbooks for literature review, reference verification, scientific writing and research across

Uses

The repository's task index and field guides help distinguish writing and publication tasks from specialist analysis tools. Each Skill has its own instructions and requirements.

How to use

The README offers requests such as “Verify every reference in refs.bib”. Select citation-verification for that task, then follow that Skill's documented workflow.

sayan chowdhury
sayan chowdhury
@sayan_ai
Skill公開の投稿Skill を探して選ぶ
投稿の要約 · 英語

Research Agent Skills bundles over 280 open-source playbooks for literature reviews, citation verification, scientific writing and ML research, and runs on any agent harness.

このリポジトリの skills

分類待ち

収集済み skill 281 件中 40 件を表示しています。

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Searches OpenAlex, PubMed, and Google Scholar, extracts metadata from DOIs, PMIDs, PMCIDs, arXiv IDs, and URLs via CrossRef, PubMed, and arXiv, then formats, deduplicates, and validates BibTeX files using bundled Python scripts (search_openalex.py,…

原文の言語: 英語

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Estimates intracellular metabolic fluxes from steady-state carbon-13 isotope-tracing measurements using validated atom maps, mfapy isotope simulation, constrained multistart fitting, and flux-profile diagnostics. Use for 13C-MFA, carbon tracing, mass…

原文の言語: 英語

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How to use the Adaptyv Bio Foundry API and Python SDK for protein experiment design, submission, and results retrieval. Use this skill whenever the user mentions Adaptyv, Foundry API, protein binding assays, protein screening experiments, BLI/SPR assays,…

原文の言語: 英語

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This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed…

原文の言語: 英語

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Look up precomputed AlphaGenome Atlas effects for any GRCh38 single-nucleotide variant (AVI score with Phred and 18 SHAP feature attributions, plus raw and quantile scores for RNA-seq, DNase, ATAC, ChIP-TF, ChIP-histone, CAGE, PRO-cap, splicing,…

原文の言語: 英語

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Plans and evaluates analytical method validation, verification, and transfer using Python scripts (plan_validation, check_response, check_accuracy_precision, check_detection_limits, check_bioanalytical_run, compare_methods) under ICH Q2(R2)/Q14, ICH M10, USP…

原文の言語: 英語

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Data structure for annotated matrices in single-cell analysis. Use when working with .h5ad files or integrating with the scverse ecosystem. This is the data format skill—for analysis workflows use scanpy; for probabilistic models use scvi-tools; for…

原文の言語: 英語

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Autonomously improve a real artifact (code, training recipe, agent harness, data pipeline, prompt) against an objective and an evaluator, using Hypothesis Tree Refinement (HTR) from the Arbor paper. Use this whenever someone wants to iteratively optimize…

原文の言語: 英語

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Infer gene regulatory networks (GRNs) from gene expression data using scalable algorithms (GRNBoost2, GENIE3). Use when analyzing transcriptomics data (bulk RNA-seq, single-cell RNA-seq) to identify transcription factor-target gene relationships and…

原文の言語: 英語

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Core Python library for astronomy and astrophysics workflows that need Astropy APIs, including units/quantities, coordinates, FITS I/O, tables, time systems, WCS, and cosmology. Use when implementing or debugging astronomical data analysis code with Astropy.

原文の言語: 英語

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Observe the user's screen via screenpipe, detect repeated research workflows, match them against existing research-agent-skills, and draft new skills (or composition recipes that chain existing ones) for the patterns not yet covered. Use when the user asks to…

原文の言語: 英語

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Benchling Python SDK and REST API integration for registry entities, inventory, ELN entries, workflows, Benchling Apps, and Data Warehouse queries. Use when automating lab data with benchling-sdk or the v2 API.

原文の言語: 英語

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Search scientific papers and retrieve structured experimental data extracted from full-text studies via the BGPT MCP server. Returns 25+ fields per paper including methods, results, sample sizes, quality scores, and conclusions. Use for literature reviews,…

原文の言語: 英語

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Organizes, queries, validates, and converts neuroscience and biomedical datasets using the Brain Imaging Data Structure (BIDS) standard, covering MRI, PET, EEG, MEG, iEEG, EMG, NIRS, microscopy, motion capture, and behavioral data. Covers PyBIDS (BIDSLayout),…

原文の言語: 英語

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Provides Biopython (Bio.Seq, Bio.SeqIO, Bio.Align, Bio.Entrez, Bio.Blast, Bio.PDB, Bio.Phylo, Bio.motifs, Bio.SeqUtils, Bio.Restriction) for sequence handling, file parsing, NCBI access, BLAST, structures, and phylogenetic trees in Python. Reads, writes, and…

原文の言語: 英語

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Unified Python interface to 40+ bioinformatics services. Use when querying multiple databases (UniProt, KEGG, ChEMBL, Reactome) in a single workflow with consistent API. Best for cross-database analysis, ID mapping across services. For quick single-database…

原文の言語: 英語

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End-to-end bulk RNA-seq orchestrator — takes raw FASTQ reads through QC and trimming (FastQC, fastp/Trim Galore), alignment and quantification (STAR, Salmon, featureCounts), assembles a gene-level counts matrix, then hands off to differential expression…

原文の言語: 英語

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Query the CZ CELLxGENE Census programmatically for versioned public single-cell and spatial transcriptomics data. Use when you need population-scale cell metadata, gene expression slices, Census summary counts, source H5AD URIs/downloads, embeddings, spatial…

原文の言語: 英語

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Google quantum computing framework. Use when targeting Google Quantum AI hardware, designing noise-aware circuits, or running quantum characterization experiments. Best for Google hardware, noise modeling, and low-level circuit design. For IBM hardware use…

原文の言語: 英語

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Prepare and validate research-only clinical decision-support evaluation, evidence-profile, cohort, survival, biomarker/model, privacy, and governance artifacts. Use for aggregate or synthetic research documentation and traceability—not patient care or live…

原文の言語: 英語

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Generates fail-closed draft JSON templates and runs local deterministic structure and consistency checks for clinical reports: CARE case reports, radiology, pathology and lab scaffolds, CONSORT 2025, SPIRIT 2025, ICH E3 CSRs, aggregate adverse-event tables,…

原文の言語: 英語

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Runs constraint-based metabolic modeling with COBRApy (Python, import cobra) on genome-scale models in SBML, JSON, YAML, or MATLAB format. Covers FBA, pFBA, geometric FBA, FVA, flux sampling, gene and reaction knockouts, production envelopes, growth media,…

原文の言語: 英語

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Run a multi-perspective Mind Council deliberation on any question, decision, or creative challenge. Use this skill whenever the user wants diverse viewpoints, needs help making a tough decision, asks for a council/panel/board discussion, wants to explore a…

原文の言語: 英語

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Distributed computing for larger-than-RAM pandas/NumPy workflows. Use when you need to scale existing pandas/NumPy code beyond memory or across clusters. Best for parallel file processing, distributed ML, integration with existing pandas code. For out-of-core…

原文の言語: 英語

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Query documented public database APIs with explicit endpoints, filters, pagination, and provenance. Use when a scientific, regulatory, financial, or other database-backed fact must be retrieved reproducibly from a named source rather than inferred from…

原文の言語: 英語

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Retrieve, version, and publish scientific datasets with DataLad and git-annex, and capture computational provenance with datalad run, rerun, and containers-run. Use when cloning or fetching data from OpenNeuro, DANDI, datasets.datalad.org, or any DataLad…

原文の言語: 英語

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Wraps RDKit through the datamol Python library (import datamol as dm) for molecular cheminformatics, returning native rdkit.Chem.Mol objects. Covers SMILES/SELFIES/InChI conversion, sanitization and standardization, descriptors, fingerprints and similarity,…

原文の言語: 英語

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Molecular ML with diverse featurizers and pre-built datasets. Use for property prediction (ADMET, toxicity) with traditional ML or GNNs when you want extensive featurization options and MoleculeNet benchmarks. Best for quick experiments with pre-trained…

原文の言語: 英語

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Runs deepTools command-line programs on NGS alignment data: bamCoverage and bamCompare for BAM to bigWig/bedGraph with RPGC, CPM, RPKM or BPM normalization, multiBamSummary with plotCorrelation and plotPCA, plotFingerprint, computeMatrix with plotHeatmap and…

原文の言語: 英語

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Query the Cancer Dependency Map (DepMap) for cancer cell line gene dependency scores (CRISPR Chronos), drug sensitivity data, and gene effect profiles. Use for identifying cancer-specific vulnerabilities, synthetic lethal interactions, and validating oncology…

原文の言語: 英語

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Extract cognitive patterns and thinking fingerprints from any text. Use this skill when the user wants to analyze how someone thinks, understand cognitive style, profile writing or speech patterns, compare thinking styles between people, asks "what's my…

原文の言語: 英語

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DiffDock and DiffDock-L molecular docking. Use for protein-small-molecule pose prediction from PDB or sequence plus SMILES/SDF/MOL2, batch docking, virtual screening, and pose-confidence interpretation. Not for binding affinity prediction.

原文の言語: 英語

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Build and operate reproducible genomics workloads on DNAnexus with the dx CLI, dxpy, apps/applets, native workflows, dxCompiler, and Nextflow. Use for DNAnexus data transfers, dxapp.json development, execution monitoring, workflow import, and project…

原文の言語: 英語

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Covers the EvolutionaryScale/Biohub `esm` Python SDK: ESM3 generative protein design (sequence, structure and function tracks, chain-of-thought), ESMC embeddings, inverse folding, ESMFold2 structure prediction, and hosted Forge/Biohub inference with…

原文の言語: 英語

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Analyze, manipulate, compare, annotate, and visualize phylogenetic or other hierarchical trees with ETE 4. Use for Newick/Nexus tree I/O, topology edits and pattern matching, Robinson-Foulds comparisons, gene-tree evolutionary events and reconciliation,…

原文の言語: 英語

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Web toolkit powered by Exa, tuned for scientific and technical content. Use this skill when the user needs to search the web or fetch/extract URL content. Covers: web search (semantic lookups, research, current info — with optional research-paper category and…

原文の言語: 英語

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Design experiments and studies BEFORE data is collected — choosing a design, randomizing, blocking, and laying out treatment combinations so results are interpretable. Use whenever someone is planning a study, asks how to assign subjects/samples to groups,…

原文の言語: 英語

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Perform bounded, local exploratory analysis of explicitly supported scientific files. Use for redacted CSV/TSV/JSON profiles; optional NumPy, HDF5, FASTA/FASTQ, and basic image metadata inspection; missingness/leakage audits; outlier and transformation…

原文の言語: 英語

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Drives the Fictiv on-demand manufacturing web app (app.fictiv.com) in the user's browser, since there is no public API. Covers uploading CAD files (STEP, SLDPRT, STL), configuring process, material, finish, threads and tolerances, reading DFM feedback,…

原文の言語: 英語

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Read, inspect, and write Flow Cytometry Standard (FCS) 2.0, 3.0, and 3.1 files with FlowIO. Use for low-level FCS metadata and channel inspection, NumPy event extraction, multi-dataset files, table export, and FCS 3.1 creation; use FlowKit for compensation,…

原文の言語: 英語

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収集済み skill 281 件中 40 件を表示しています。