「skill discovery」の検索結果

Design human-visible, machine-readable website layers for AI assistants, RAG, and agentic search. Use this skill when the user asks for llms.txt, LLM discovery plans, visible TLDR or direct-answer blocks, source trails, entity pages, markdown alternates, AI crawler policy, topic-to-URL maps, assistant-readable public content, or LLM-friendly architecture that must preserve canonical SEO strategy without hidden bot-only facts or duplicate story bodies.

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Analyze credential-free public website monitoring evidence from server access logs, HTTP fetches, robots/llms/sitemap checks, and exported summaries while protecting raw IP and private query data. Use this skill when the user asks whether crawlers or AI bots reached a site, whether public SEO/LLM discovery files respond, or how to separate observed facts from unknown Search Console, rank, analytics, or citation claims.

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This skill should be used when the user asks to "create a plugin", "scaffold a plugin", "understand plugin structure", "organize plugin components", "set up plugin.json", "use ${CLAUDE_PLUGIN_ROOT}", "add commands/agents/skills/hooks", "configure auto-discovery", or needs guidance on plugin directory layout, manifest configuration, component organization, file naming conventions, or Claude Code plugin architecture best practices.

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Use when the user is working a B2B SaaS deal from qualified opportunity to closed-won — preparing discovery calls, structuring demos or POCs, handling a specific objection, qualifying with MEDDIC/MEDDPICC/SPICED, building mutual action plans, navigating negotiation or procurement, or reviewing pipeline health, forecast accuracy, and deal velocity. Not for finding or prospecting new leads (use prospecting) or upstream positioning (use product-position).

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Install skills, agents, and teams from agent-almanac into any supported agentic framework using the CLI. Covers framework detection, content search, installation with dependency resolution, health auditing, and manifest-based syncing. Use when setting up a new project with agentic capabilities, installing specific skills or entire domains, targeting multiple frameworks simultaneously, or maintaining a declarative manifest of installed content.

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Turn in vitro potency and animal pharmacokinetics into a defensible human dose projection — the arithmetic that decides whether a compound can reach its target concentration safely. Use this skill for non-compartmental analysis of a concentration-time profile (AUC, Cmax, terminal half-life, clearance, volume of distribution), one- and two-compartment simulation of a dosing regimen, interspecies allometric scaling, human-equivalent dose conversion by body surface area, and the exposure margin between a projected therapeutic concentration and a toxicology no-effect level. Also trigger on non-compartmental analysis, AUC, clearance, volume of distribution, allometric scaling, human equivalent dose, first-in-human, NOAEL, therapeutic index, or exposure margin.

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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.

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Design small interfering RNA and antisense oligonucleotide sequences against a transcript, and screen them for the failure modes specific to nucleic-acid drugs. Use this skill to tile a target transcript, apply positional and thermodynamic selection rules including duplex asymmetry and nearest-neighbour melting temperature, scan candidates for seed-region complementarity to off-target transcripts, and lay out a chemical modification pattern — gapmer architecture, 2'-O-methyl and 2'-MOE wings, locked nucleic acid, and phosphorothioate placement. Also trigger on siRNA, antisense oligonucleotide, ASO, gapmer, RNase H, seed region, duplex asymmetry, 2'-MOE, locked nucleic acid, phosphorothioate, or GalNAc conjugate.

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Decide whether a protein has a pocket worth targeting, and where it is, before committing to a docking or design campaign. Use this skill to run fpocket cavity detection, rank cavities by druggability and volume, compare apo and holo conformations to spot induced fit, identify allosteric and cryptic cavities that only open in simulation, and convert a chosen cavity into the search box coordinates a docking run needs. Also trigger on fpocket, cavity detection, druggability score, alpha sphere, cryptic pocket, allosteric site, pocket volume, hotspot mapping, or undruggable target assessment.

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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.

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Design new proteins that bind a chosen surface, using BindCraft's AlphaFold2-guided hallucination or the RFdiffusion backbone plus ProteinMPNN sequence pipeline. Use this skill to specify a target epitope by hotspot residue, trim a receptor to the region worth designing against, set up a design campaign, and filter the output on the in-silico metrics that predict experimental success — interface predicted TM-score, predicted aligned error at the interface, buried surface area, and shape complementarity. Also trigger on BindCraft, RFdiffusion, ProteinMPNN, minibinder, hallucination, inverse folding, hotspot residue, epitope targeting, ipTM, or de novo binder.

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Query the Open Targets Platform GraphQL API for target-disease associations, genetic and clinical evidence, tractability and safety liabilities, target prioritisation metrics, known drugs and mechanisms of action, and disease ontology. Use this skill for target identification and validation, target-disease evidence review, druggability assessment, drug repurposing, and resolving gene, disease, and drug names to Ensembl, MONDO, and ChEMBL identifiers. Also trigger when a query mentions Open Targets, platform.opentargets.org, association scores, tractability buckets, or api.platform.opentargets.org.

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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.

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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.

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Assemble the human genetic evidence for and against a target before a programme commits to it — the evidence class that most improves the odds of surviving clinical development. Use this skill to pull gnomAD constraint metrics (LOEUF, pLI, observed/expected) that show whether loss of function is tolerated in people, retrieve GWAS Catalog associations and fine-mapped credible sets for a gene, and read a natural human knockout as a safety readout. Also trigger on gnomAD, LOEUF, pLI, loss-of-function intolerance, mutational constraint, GWAS Catalog, credible set, human knockout, genetic support, or target safety dossier.

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Cofold protein-ligand, protein-protein, and nucleic-acid complexes with Boltz-2, and predict binding affinity with its trained affinity head. Use this skill to build Boltz input YAML, run structure prediction with MSAs, pocket constraints, templates, and modified residues, screen compound libraries by cofolding, and interpret confidence scores (pLDDT, pTM, ipTM, PDE) and affinity output (binder probability and log10 IC50). Also trigger on Boltz, Boltz-1, Boltz-2, cofolding, boltz predict, affinity_pred_value, affinity_probability_binary, ipTM, or open-weights AlphaFold3 alternatives.

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Query the ChEMBL database web services for measured bioactivity data, compound records and calculated properties, targets, assays, mechanisms of action, drug indications and warnings. Use this skill to build curated SAR or QSAR datasets for a target, look compounds up by SMILES, InChIKey, name, or ChEMBL id, run similarity and substructure searches, and check what chemistry is already known against a protein. Also trigger when a query mentions ChEMBL ids (CHEMBL...), pChEMBL values, IC50/Ki/Kd/EC50 retrieval, assay confidence scores, or ebi.ac.uk/chembl.

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Number antibody variable domains, annotate CDRs, and assess developability from sequence. Use this skill to apply IMGT, Kabat, Chothia, Martin, or AHo numbering with ANARCI, delimit CDRs and framework regions, scan for chemical liabilities (N-glycosylation sequons, deamidation NG, isomerisation DG, oxidation, unpaired cysteine, fragmentation), compute pI, net charge, extinction coefficient and hydrophobicity, and plan humanisation by CDR grafting. Also trigger on antibody, nanobody, VHH, scFv, Fab, CDR, framework, ANARCI, abnumber, IgBLAST, OAS, SAbDab, humanization, Vernier residues, or developability.

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Query the FDA's public openFDA APIs for post-market drug data — FAERS adverse-event reports, Drugs@FDA approval and submission history, Structured Product Labels including boxed warnings, the National Drug Code directory, recall enforcement reports, and drug shortages. Use this skill to check what a regulator has already concluded about a molecule or its class, to date an approval and count its efficacy supplements, to read an approved indication or boxed warning, and to score a drug-event pair for disproportionate reporting with PRR, ROR, and chi-squared. Also trigger on openFDA, api.fda.gov, FAERS, Drugs@FDA, SPL, NDC, pharmacovigilance, boxed warning, adverse event report, drug recall, or safety signal.

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Retrieve protein sequences, annotation, and structures from UniProtKB, the RCSB PDB, and AlphaFold DB. Use this skill to resolve a gene or protein name to a UniProt accession, pull sequences and FASTA files, find binding sites and domains, search the PDB by UniProt accession, sequence, ligand, or text, download mmCIF/PDB coordinates and biological assemblies, fetch AlphaFold models with their pLDDT confidence, and check whether a structure is actually usable before docking or simulating it. Also trigger on UniProt accessions, PDB ids, rest.uniprot.org, search.rcsb.org, files.rcsb.org, alphafold.ebi.ac.uk, id mapping, SEQRES, or missing residues.

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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.

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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.

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Structure-based docking with AutoDock Vina, Vinardo, and AutoDock4 through the Meeko toolchain. Use this skill to define a docking box, prepare receptors and ligands as PDBQT, run single or batch docking, rescore, and interpret affinities, poses, and ligand efficiency. Covers box definition from a reference ligand or pocket residues, protonation and tautomer decisions, flexible side chains, exhaustiveness and seeds, redocking validation, and virtual screening over compound libraries. Also trigger on vina, smina, gnina, mk_prepare_ligand, mk_prepare_receptor, mk_export, scrub.py, PDBQT, autogrid4, docking box, or binding-pose prediction.

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Turn a set of structures into absorption, distribution, metabolism, excretion, and toxicity estimates with ADMET-AI, and read them as a developability verdict rather than a table of numbers. Use this skill to run batch prediction over a library, interpret each endpoint against its DrugBank-approved percentile, and flag the liabilities that stop a series — hERG blockade, CYP inhibition, poor Caco-2 permeability, high clearance, and plasma protein binding. Also trigger on ADMET-AI, admet_ai, Chemprop-RDKit, hERG liability, CYP3A4 inhibition, Caco-2, bioavailability prediction, or developability triage.

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。