LabClaw

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.

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.

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.

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

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.

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 models, or Survival SVMs, evaluating survival predictions with concordance index or Brier score, handling competing risks, or implementing any survival analysis workflow with the scikit-survival library.

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 citation information, convert DOIs to BibTeX, or ensure reference accuracy in 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, citations (APA/AMA/Vancouver), figures/tables, reporting guidelines (CONSORT/STROBE/PRISMA), for research papers and journal submissions.

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. Ideal for detailed interaction analysis.

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. Essential for benchmarking hand tracking algorithms.

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 overlays on video frames.

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 applications.

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 projections, meshes, and skeletal overlays on video frames.

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, directionality index), and emits structured JSON for downstream reporting, plotting, or ELN integration.

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 JSON with error type, timestamp, severity, and corrective action suggestions for real-time alerts or post-hoc audit.

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 audit, or ELN attachment.

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, instrument display values, gel band intensities — emitting a time-series JSON or CSV table ready for downstream analysis, charting, or ELN attachment.

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 — enabling one-person lab operation with zero-missed-step guarantees.

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, correct deviations, or resume experiments without breaking flow.

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 snippets or JSON instruction lists ready for robot execution or simulation.

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 lab-ready spreadsheets for sharing, archival, or downstream analysis in R, pandas, or Excel.

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 — optimized for VLM or spatial intelligence model consumption in protocol guidance, error detection, or AR overlay generation.

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 distribution charts, MSD plots, wound-closure timeseries, dose-response curves, and 96-well heatmaps — using matplotlib and seaborn. Exports PNG/PDF at configurable DPI for papers, ELN entries, or XR dashboards.

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 placeholders — suitable for internal lab reports, preprint drafts, or journal submission-ready layouts.

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, past-tense, passive-voice Methods prose with full reagent citations, equipment model numbers, and statistical analysis subsections. Outputs LaTeX (\subsection{} / \paragraph{}) or Markdown, ready for direct insertion into a manuscript draft.

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 where researchers control analysis, guidance, and data export by speaking.

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 research planning.

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 optimization. Also use for submitting experiments via API, tracking experiment status, downloading results, optimizing protein sequences for better expression using computational tools (NetSolP, SoluProt, SolubleMPNN, ESM), or managing protein design workflows with wet-lab validation.

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.

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

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 rdkit.Chem.Mol objects. For advanced control or custom parameters, use rdkit directly.

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 models, diverse molecular representations. For graph-first PyTorch workflows use torchdrug; for benchmark datasets use pytdc.

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.

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

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

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 discovery research, pharmacology studies, drug-drug interaction analysis, target identification, chemical similarity searches, ADMET predictions, or any task requiring detailed drug and drug target information from DrugBank.

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

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

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

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