wu-yc/LabClaw

gene-database

Query NCBI Gene via E-utilities/Datasets API. Search by symbol/ID, retrieve gene info (RefSeqs, GO, locations, phenotypes), batch lookups, for gene annotation and functional analysis.

原文の言語: 英語

geo-database

Access NCBI GEO for gene expression/genomics data. Search/download microarray and RNA-seq datasets (GSE, GSM, GPL), retrieve SOFT/Matrix files, for transcriptomics and expression analysis.

原文の言語: 英語

gwas-database

Query NHGRI-EBI GWAS Catalog for SNP-trait associations. Search variants by rs ID, disease/trait, gene, retrieve p-values and summary statistics, for genetic epidemiology and polygenic risk scores.

原文の言語: 英語

hmdb-database

Access Human Metabolome Database (220K+ metabolites). Search by name/ID/structure, retrieve chemical properties, biomarker data, NMR/MS spectra, pathways, for metabolomics and identification.

原文の言語: 英語

pdb-database

Access RCSB PDB for 3D protein/nucleic acid structures. Search by text/sequence/structure, download coordinates (PDB/mmCIF), retrieve metadata, for structural biology and drug discovery.

原文の言語: 英語

scikit-survival

Comprehensive toolkit for survival analysis and time-to-event modeling in Python using scikit-survival. Use this skill when working with censored survival data, performing time-to-event analysis, fitting Cox models, Random Survival Forests, Gradient Boosting…

原文の言語: 英語

citation-management

Comprehensive citation management for academic research. Search Google Scholar and PubMed for papers, extract accurate metadata, validate citations, and generate properly formatted BibTeX entries. This skill should be used when you need to find papers, verify…

原文の言語: 英語

scientific-writing

Core skill for the deep research and writing tool. Write scientific manuscripts in full paragraphs (never bullet points). Use two-stage process with (1) section outlines with key points using research-lookup then (2) convert to flowing prose. IMRAD structure,…

原文の言語: 英語

egohos-segmentation

Egocentric Hand-Object Segmentation (EgoHOS) - pixel-level hand and object segmentation in egocentric videos. Outputs fine-grained segmentation masks with hand regions highlighted. Specialized for hand-object interaction scenarios with pixel-accurate masks.…

原文の言語: 英語

hand-tracking-toolkit

Facebook Research Hand Tracking Challenge Toolkit - evaluation and visualization tools for 3D hand tracking. Supports loading HOT3D data, computing metrics (PA-MPJPE, AUC, etc.), visualizing 3D pose projections, and generating tracking evaluation reports.…

原文の言語: 英語

hands-3d-pose

High-quality 3D hand pose estimation for egocentric videos from ECCV 2024 (ap229997/hands). Provides 3D joint keypoints and skeleton visualization projected to 2D. Optimized for daily egocentric activities with state-of-the-art accuracy. Outputs hand skeleton…

原文の言語: 英語

handtracking

Real-time hand detection in egocentric videos using victordibia/handtracking. Outputs bounding boxes for hands, specifically trained on EgoHands dataset. Supports video input/output with labeled hand boxes. Lightweight and fast for egocentric view…

原文の言語: 英語

hot3d

HOT3D (Hand-Object 3D Dataset) by Meta Facebook - multi-view egocentric hand and object 3D tracking for Aria/Quest smart glasses. State-of-the-art multi-view 3D hand pose, object pose, and hand-object interaction tracking. Supports visualization with 3D joint…

原文の言語: 英語

analyze-lab-video-cell-behavior

Automated cell behavior analysis from microscopy or XR lab recordings. Classifies cell motion phenotypes (migration, proliferation, apoptosis, division, quiescence), computes population-level quantitative metrics (growth rate, migration velocity,…

原文の言語: 英語

detect-common-wetlab-errors

Detects common wet-lab procedural and safety errors from XR or fixed-camera lab video. Identifies pipette volume deviations, forgotten reagent additions, uncapped tubes, contamination risks, sample mix-ups, and other observable hazards. Outputs structured…

原文の言語: 英語

egocentric-view-to-structured-log

Converts first-person XR headset video into a structured experiment timeline log. Extracts timestamped events (action, object, location, result) via VLM or action recognition, outputs Markdown or JSON for downstream analysis, reporting, protocol compliance…

原文の言語: 英語

extract-experiment-data-from-video

General-purpose experimental data extractor from lab video streams. Ingests footage from XR headsets or fixed cameras and extracts typed, timestamped measurements — liquid volume levels, color/turbidity shifts, cell and colony counts, pipette readouts,…

原文の言語: 英語

protocol-video-matching

Real-time XR video vs. protocol text matching and deviation detection. Aligns first-person XR headset video streams frame-by-frame against structured protocol steps, flags procedural deviations, scores compliance, and delivers corrective audio/visual overlays…

原文の言語: 英語

realtime-protocol-guidance-prompts

Generates short, imperative guidance prompts for the next experimental step from current video frame and protocol context. Output is optimized for voice broadcast (TTS) or AR overlay — concise, actionable, command-style — to guide researchers in real time,…

原文の言語: 英語

robot-protocol-step-generator

Converts natural language or PDF protocol text into executable step sequences for Opentrons or PyLabRobot. Parses protocol descriptions to extract pipette volumes, well positions, temperatures, incubation times, and transfer patterns; outputs Python code…

原文の言語: 英語

export-experiment-data-to-excel

Exports any structured experimental data (JSON, tables, time series) to well-formatted Excel (.xlsx) files. Auto-names sheets (Raw Data, Growth Curves, Cell Counts, etc.), adds unit headers and annotation rows, applies consistent styling, and produces…

原文の言語: 英語

gaussian-splatting-scene-description

Generates natural language scene descriptions from 3D Gaussian Splatting reconstructions built from lab photos or short video clips. Outputs structured text with instrument placement, sample positions, spatial layout keywords, and relational predicates —…

原文の言語: 英語

generate-cell-analysis-charts

Domain-specialized chart generator for cell biology video analysis outputs. Consumes structured JSON from analyze_lab_video_cell_behavior or compatible sources and produces publication-ready figures — growth curves, cell trajectory maps, phenotype…

原文の言語: 英語

generate-double-column-pdf-report

Assembles experimental data, figures, methods, and results into a journal-style double-column PDF report. Uses reportlab or PyMuPDF for programmatic generation with title page, embedded figures/tables, section headings, body text flow, and reference…

原文の言語: 英語

generate-scientific-method-section

Automated SCI-standard Methods section generator from experiment execution records. Parses LabOS skill call chains, structured JSON logs (extract_experiment_data_from_video, analyze_lab_video_cell_behavior), protocol text, and ELN entries to produce flowing,…

原文の言語: 英語

voice-command-to-skill

Maps natural language voice commands to concrete LabClaw skill invocations. Parses ASR output, identifies intent, selects target skill, fills parameters from context, and provides prompt templates — enabling hands-free, voice-driven anywhere-lab experiences…

原文の言語: 英語

literature-to-hypothesis

Extracts falsifiable scientific hypotheses (if-then form) from multiple PubMed articles, abstracts, or full texts. Synthesizes supporting evidence, contradictions, and experimental validation suggestions into a structured Markdown report for hypothesis-driven…

原文の言語: 英語

adaptyv

Cloud laboratory platform for automated protein testing and validation. Use when designing proteins and needing experimental validation including binding assays, expression testing, thermostability measurements, enzyme activity assays, or protein sequence…

原文の言語: 英語

chembl-database

Query ChEMBL bioactive molecules and drug discovery data. Search compounds by structure/properties, retrieve bioactivity data (IC50, Ki), find inhibitors, perform SAR studies, for medicinal chemistry.

原文の言語: 英語

chembl-search

Search ChEMBL bioactive molecules database with natural language queries. Find compounds and assay data with Valyu semantic search.

原文の言語: 英語

datamol

Pythonic wrapper around RDKit with simplified interface and sensible defaults. Preferred for standard drug discovery including SMILES parsing, standardization, descriptors, fingerprints, clustering, 3D conformers, parallel processing. Returns native…

原文の言語: 英語

deepchem

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

diffdock

Diffusion-based molecular docking. Predict protein-ligand binding poses from PDB/SMILES, confidence scores, virtual screening, for structure-based drug design. Not for affinity prediction.

原文の言語: 英語

drug-discovery-search

End-to-end drug discovery platform combining ChEMBL compounds, DrugBank, targets, and FDA labels. Natural language powered by Valyu.

原文の言語: 英語

drug-labels-search

Search FDA drug labels with natural language queries. Official drug information, indications, and safety data via Valyu.

原文の言語: 英語

drugbank-database

Access and analyze comprehensive drug information from the DrugBank database including drug properties, interactions, targets, pathways, chemical structures, and pharmacology data. This skill should be used when working with pharmaceutical data, drug…

原文の言語: 英語

drugbank-search

Search DrugBank comprehensive drug database with natural language queries. Drug mechanisms, interactions, and safety data powered by Valyu.

原文の言語: 英語

fda-database

Query openFDA API for drugs, devices, adverse events, recalls, regulatory submissions (510k, PMA), substance identification (UNII), for FDA regulatory data analysis and safety research.

原文の言語: 英語

medchem

Medicinal chemistry filters. Apply drug-likeness rules (Lipinski, Veber), PAINS filters, structural alerts, complexity metrics, for compound prioritization and library filtering.

原文の言語: 英語

molfeat

Molecular featurization for ML (100+ featurizers). ECFP, MACCS, descriptors, pretrained models (ChemBERTa), convert SMILES to features, for QSAR and molecular ML.

原文の言語: 英語