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概要と使いどころ
Use when the user says 'convert this html to bricks' or 'paste html into bricks', or has a Webflow, Framer, CodePen, or old static export to bring into a Bricks site. Converts raw HTML and CSS into native Bricks elements, mapping colors, type, and spacing to design tokens, plus ACSS classes when installed.
日本語の概要は準備中です。原文の説明を表示しています。
Use when the user says 'send this to my client', 'get feedback on this page', 'send a review link', 'what did the client say', or 'work through the review notes'. Sends a Murmur review link, reads the notes reviewers pin to the page, replies, applies each change on a duplicate, and resolves every note with the snapshot of the change.
日本語の概要は準備中です。原文の説明を表示しています。
Use when the user says 'optimize my wordpress images with ai', 'compress and optimize all images', 'audit my media library', or 'improve image performance'. Downloads images, compresses, converts to WebP, resizes and renames locally, re-uploads, and updates every content reference.
日本語の概要は準備中です。原文の説明を表示しています。
Use when the user says 'extract my brand voice', 'what is my writing style', or 'analyze my tone', or before any skill that writes copy for the site. Reads 5-10 published posts and persists tone, lexicon, sentence patterns, formality, signature phrases, and phrases the site never uses.
日本語の概要は準備中です。原文の説明を表示しています。
Use when the user says 'migrate oxygen to bricks', 'convert oxygen to bricks', or 'replace oxygen with bricks'. Maps Oxygen Builder component trees to Bricks JSON elements and creates draft duplicates for review.
日本語の概要は準備中です。原文の説明を表示しています。
Use when the user says 'run a sale', 'discount this category', 'set up a coupon', 'reprice these products', 'end the sale', or asks what a discount would cost them. Plans the price change, shows it as a preview before anything moves, writes it with a snapshot, and can put it back.
日本語の概要は準備中です。原文の説明を表示しています。
Use when the user says 'migrate brizy to gutenberg', 'convert brizy to blocks', or 'replace brizy with native wordpress'. Parses the Brizy JSON data, maps elements to core blocks, and creates draft duplicates for review.
日本語の概要は準備中です。原文の説明を表示しています。
Use when the user says 'find old content', 'what is stale', 'what needs refreshing', or 'what should i archive'. Categorizes pages and posts as fresh, aging, stale, or archive-candidate by age, traffic signal, and internal link count, with a refresh, redirect, or archive call per item.
日本語の概要は準備中です。原文の説明を表示しています。
Use when the user asks to create a custom post type, build a portfolio, set up case studies, add team members, events, or podcast episodes, or says 'scaffold a CPT'. Creates the post type plus supporting taxonomies, an ACF field group, sample entries, and a builder-specific single template suggestion.
日本語の概要は準備中です。原文の説明を表示しています。
Use when the user says 'build a design system for my site', 'extract design tokens', 'capture my brand', or 'build my style guide', or after a rebrand. Reads representative pages, theme files, and media to extract logo, colors, typography, spacing, and components, then writes a visible style-guide page.
日本語の概要は準備中です。原文の説明を表示しています。
Analyze web traffic, SEO performance, and search engine trends using Google Search Console, Google Analytics 4, and Bing Webmaster Tools. Use when the user asks to analyze traffic, check SEO, or find keyword opportunities.
日本語の概要は準備中です。原文の説明を表示しています。
Query the Precision Medicine Knowledge Graph (PrimeKG) for multiscale biological relationships across genes and proteins, drugs, diseases, phenotypes, pathways, biological processes, exposures and anatomy. Use this skill to search entities by name, pull direct neighbours and their evidence types, summarise the local network around a disease, and find direct or two-hop drug-disease connections for repurposing hypotheses. Also trigger on PrimeKG, kg.csv, Harvard Dataverse knowledge graph, disease_protein, drug_protein, indication and contraindication edges, or network pharmacology over a biomedical knowledge graph.
日本語の概要は準備中です。原文の説明を表示しています。
Molecular featurization hub with one consistent interface over 100+ featurizers. Fingerprints (ECFP/Morgan, MACCS, atom pair, topological torsion, Avalon, RDKit, ERG), RDKit and Mordred descriptor sets, pharmacophore and 3D shape descriptors, scaffold keys, and pretrained embeddings (ChemBERTa, ChemGPT, MolT5, GIN, Graphormer) through a common transformer API with caching and parallelism. Use this skill to convert SMILES into model-ready feature matrices for QSAR, virtual screening, and molecular ML, and to choose between featurizer families. Also trigger on molfeat, MoleculeTransformer, FPVecTransformer, PretrainedHFTransformer, molfeat model store, or featurizer selection.
日本語の概要は準備中です。原文の説明を表示しています。
Run and analyze molecular dynamics simulations with OpenMM and MDAnalysis. Set up protein and protein-ligand systems with PDBFixer, choose force fields and water models (AMBER14, CHARMM36m, ff19SB, GAFF2, TIP3P), solvate and add ions, run energy minimization, NVT/NPT equilibration and production MD on GPU, then analyze trajectories for RMSD, RMSF, radius of gyration, hydrogen bonds, native contacts, PCA and free energy surfaces. Use this skill for protein stability under mutation, ligand binding-mode and residence-time questions, conformational sampling, membrane proteins, and disordered ensembles. Also trigger on OpenMM, MDAnalysis, mdtraj, Simulation.step, LangevinMiddleIntegrator, PDBFixer, DCD or XTC trajectory, RMSD analysis, or production MD.
日本語の概要は準備中です。原文の説明を表示しています。
Medicinal chemistry filters for compound triage. Apply drug-likeness rules (Lipinski rule of five, Veber, Oprea, CNS, lead-like, rule of three), structural alert catalogs (PAINS a/b/c, NIBR screening-deck severity, Brenk, BMS, Glaxo, Dundee, ChEMBL common alerts), ZINC-15 percentile complexity metrics (Bertz, SAscore, QED, Whitlock, Barone), chemical-group detection, Lilly demerits, and the medchem query language (MATCHRULE, HASALERT, HASPROP, HASGROUP) for filtering a library at scale. Also trigger on medchem, `import medchem as mc`, RuleFilters, NIBRFilters, CommonAlertsFilters, NamedCatalogs, QueryFilter, PAINS filtering, or structural alerts.
日本語の概要は準備中です。原文の説明を表示しています。
Estimate how likely a protein therapeutic is to provoke an anti-drug antibody response, and locate the sequence regions responsible. Use this skill to tile a sequence into peptides, predict class II MHC presentation across a population-representative allele panel, aggregate predicted binders into a per-region and whole-molecule risk score, compare a candidate against its closest human germline, and decide which liabilities are worth deimmunising. Also trigger on immunogenicity, anti-drug antibody, ADA, T-cell epitope, MHC class II, HLA-DRB1, NetMHCIIpan, NetMHCpan, deimmunisation, tregitope, or population coverage.
日本語の概要は準備中です。原文の説明を表示しています。
Analyze and engineer protein glycosylation. Scan sequences for canonical N-glycosylation sequons (N-X-S/T with X not proline, including overlapping sites), predict O-GalNAc hotspots, read glycan notation, and reach the curated external tooling (NetNGlyc, NetOGlyc, GlycoShield, GlycoWorkbench, GlyTouCan, GlyConnect). Use this skill for therapeutic antibody glycoengineering and afucosylation for ADCC, Fc glycan control, glycan shielding in vaccine immunogen design, sequon removal or insertion, and half-life engineering through sialylation. Also trigger on N-glycosylation, sequon, NXS/NXT, O-glycosylation, glycoform heterogeneity, afucosylation, high-mannose, GlyTouCan, or WURCS.
日本語の概要は準備中です。原文の説明を表示しています。
Generate and optimise novel small molecules with REINVENT 4 — de novo sampling from a chemical language model, scaffold decoration with LibInvent, fragment linking with LinkInvent, and similarity-constrained analogue generation with Mol2Mol. Use this skill to set up reinforcement-learning or curriculum runs, compose a multi-parameter scoring function from docking scores, predictive models, and physicochemical desirability, and read the resulting sampled sets. Also trigger on REINVENT, LibInvent, LinkInvent, Mol2Mol, scaffold hopping, R-group replacement, linker design, chemical language model, or reinforcement-learning molecule optimisation.
日本語の概要は準備中です。原文の説明を表示しています。
Compute relative and absolute binding free energies with the Open Free Energy toolkit — the rigorous alchemical alternative to docking scores when a congeneric series needs reliable potency ranking. Use this skill to plan a perturbation network over a ligand set, choose atom mappings, run hybrid-topology or separated-topology protocols, and analyse the result — per-edge ΔΔG with uncertainty, cycle-closure error, and mean unsigned error against measured affinities. Also trigger on OpenFE, alchemical transformation, thermodynamic cycle, RBFE, ABFE, SepTop, lambda window, MBAR, cycle closure, or perturbation map.
日本語の概要は準備中です。原文の説明を表示しています。
Protein language models through the EvolutionaryScale `esm` Python SDK. Generate and embed sequences with ESM3 (multimodal sequence, structure and function prompting), extract per-residue and mean-pooled embeddings with ESM C, fold sequences with ESMFold2, and run inference locally or against the Forge and Biohub hosted clients. Use this skill for protein representation learning, variant effect and mutational scanning from likelihoods, sequence generation and inpainting, structure prediction from sequence alone, and embedding features for downstream models. Also trigger on esm, ESM3, ESMC, ESM Cambrian, ESMFold2, `from esm.models`, ESMProtein, GenerationConfig, forge.evolutionaryscale.ai, biohub.ai, or ESM_API_KEY.
日本語の概要は準備中です。原文の説明を表示しています。
DiffDock and DiffDock-L diffusion-based molecular docking. Use for blind protein-small-molecule pose prediction from a PDB file or sequence plus SMILES/SDF/MOL2, batch docking over a CSV of complexes, virtual screening triage, sampling multiple poses per complex, and reading the confidence score correctly. Also trigger on DiffDock, DiffDock-L, inference.py, confidence_model, samples_per_complex, ESM embedding preparation for docking, or blind docking without a defined box. Not for binding affinity prediction — the confidence score ranks pose plausibility, not potency.
日本語の概要は準備中です。原文の説明を表示しています。
Query the Cancer Dependency Map (DepMap) for cancer cell line gene dependency scores (CRISPR Chronos), RNAi DEMETER2 scores, PRISM compound sensitivity, and gene effect profiles across the cell-line panel. Use for identifying cancer-selective vulnerabilities, separating pan-essential genes from selective ones, finding synthetic lethal interactions, correlating dependency with mutation, expression and copy number, and validating oncology drug targets. Also trigger on DepMap, Chronos gene effect, CRISPRGeneEffect.csv, DEMETER2, PRISM repurposing, co-essentiality, pan-essential, or ACH- cell line identifiers.
日本語の概要は準備中です。原文の説明を表示しています。
Work on bifunctional degraders and molecular glues, where potency comes from a ternary complex rather than occupancy. Use this skill to apply the property rules that govern this beyond-rule-of-five space, reason about linker length, attachment vector and E3 ligase choice, prepare inputs for ternary complex structure prediction, and interpret degradation readouts — DC50, Dmax, cooperativity, and the hook effect that makes a dose-response curve turn over. Also trigger on PROTAC, molecular glue, targeted protein degradation, E3 ligase, cereblon, VHL, ternary complex, DC50, Dmax, hook effect, cooperativity, or PROTAC-DB.
日本語の概要は準備中です。原文の説明を表示しています。
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 ready-made ADMET numbers without training a model use admet-prediction; for benchmark datasets and task-aware splits use pytdc.
日本語の概要は準備中です。原文の説明を表示しています。