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概要と使いどころ
Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Codex's capabilities with specialized knowledge, workflows, or tool integrations.
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。
A skill that creates new Claude skills and automatically shares them on Slack using Rube for seamless team collaboration and skill discovery.
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。
A set of resources to help me write all kinds of internal communications, using the formats that my company likes to use. Claude should use this skill whenever asked to write some sort of internal communications (status reports, leadership updates, 3P updates, company newsletters, FAQs, incident reports, project updates, etc.).
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。
Analyzes your recent Codex chat history to identify coding patterns, development gaps, and areas for improvement, curates relevant learning resources from HackerNews, and automatically sends a personalized growth report to your Slack DMs.
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。
This skill ensures all code follows security best practices and identifies potential vulnerabilities. Use when implementing authentication or authorization, handling user input or file uploads, or creating new API endpoints.
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。
Turn a one-line objective into a step-by-step construction plan any coding agent can execute cold. Each step has a self-contained context brief — a fresh agent in a new session can pick up any step without reading prior steps.
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。
⚠️ DEPRECATED: This SDK has been renamed to Call Automation. For new projects, use azure-communication-callautomation instead. This skill is for maintaining legacy code only.
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。
Annotates bacterial and archaeal genomes (isolates, MAGs, plasmids) with Bakta (active versioned databases, NCBI-compliant output) or Prokka (legacy), producing GFF3/GenBank/EMBL/FASTA with INSDC locus tags. Covers Bakta-vs-Prokka-vs-PGAP-vs-DFAST choice, light-vs-full database tiers, translation-table selection (11/4/25), archaeal and leaderless-gene caveats, the small-ORF blind spot, pseudogene-vs-phase-variation, the pangenome re-annotation trap, and submission compliance. Use when annotating a newly assembled prokaryotic genome, choosing an annotation tool, re-annotating a collection for pangenomics, or preparing annotations for NCBI/DDBJ submission.
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。
Predicts protein-coding gene structures (exons, introns, UTRs) in eukaryotic genomes with BRAKER3 (RNA-seq + protein evidence), BRAKER1/BRAKER2, GALBA (protein-only), Funannotate (fungi), GeMoMa (homology projection), or Helixer/Tiberius (deep-learning ab initio). Covers the evidence-first tool decision, mandatory soft-masking, the training-set-quality-dominates principle, OrthoDB clade-partition selection, the one-isoform-per-locus and missing-UTR traps, merge/split errors, and reference bias against orphan genes. Use when annotating a newly assembled eukaryotic genome, choosing a gene-prediction pipeline based on available evidence, or diagnosing a poor annotation.
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。
Transfers gene annotations between genome assemblies via coordinate liftover (UCSC liftOver, CrossMap for same-species version updates) or feature/sequence projection (Liftoff for same/close species, miniprot for protein-level cross-species, TOGA/GeMoMa/CAT for distant clades). Covers the coordinate-vs-projection decision by divergence, why a successful lift is not biological confirmation, reference bias, the silent-dropping of unmapped features, build/PAR/MHC/inversion hazards, and transfer-vs-de-novo validation. Use when annotating a new assembly of a species with an existing reference, harmonizing coordinates across builds, or mapping annotations across related species.
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 UniProt's REST API (post-2022 endpoint at rest.uniprot.org) for protein sequences, annotations, GO terms, cross-references, ID mappings, and proteomes. Use when fetching UniProtKB entries, navigating the JSON schema, choosing between UniProtKB/UniRef/UniParc/Proteomes resources, deciding stream vs search endpoint for batch retrieval, running ID-mapping jobs with the async pattern, handling isoform suffixes, or filtering reviewed Swiss-Prot vs auto-annotated TrEMBL. Encodes the legacy URL migration (2022), the new JSON schema layout, and bulk-pull patterns.
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。
Trains and applies base-resolution deep learning models on ChIP-seq / ChIP-nexus / CUT&RUN data. Uses BPNet (Avsec 2021 Nat Genet 53:354; soft motif syntax from ChIP-nexus), chromBPNet (Pampari A et al 2025 Nat Genet; bias-factorized base-resolution profiles), EnFormer (Avsec 2021 Nat Methods 18:1196; 196 kb input, ~100 kb effective receptive field), DeepSEA (Zhou 2015; multi-task CNN), and JASPAR 2026 deep-learning collection (1259 BPNet ChIP models). Performs in silico mutagenesis for variant-effect prediction, DeepLIFT/Grad attribution, and TF-MoDISco motif discovery from attribution scores. Use when predicting variant effects on TF binding, discovering soft motif syntax / cooperativity, integrating ChIP-seq with sequence-only predictions, or applying precomputed JASPAR Deep Learning models to new variants.
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。
Tests associations between categorical variables in clinical data using chi-square, Fisher's exact, Boschloo, Cochran-Mantel-Haenszel, and modern McNemar variants with calibrated confidence intervals (Wilson, Newcombe, Miettinen-Nurminen). Use when analyzing categorical outcomes, paired binary endpoints, or testing treatment-outcome independence in confirmatory or exploratory clinical trials.
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。
Designs novel molecules using REINVENT 4 (de novo, scaffold decoration, linker design, R-group, molecular optimization), MolMIM, Diffusion-based generators (DiGress, DiffSMol), and JT-VAE with explicit handling of multi-parameter optimization (MPO), goal-directed scoring functions, transfer/reinforcement/curriculum learning, synthetic accessibility scoring, and chemical space exploration vs exploitation. Use when designing new chemical matter against a target, decorating a scaffold, linking fragments, or optimizing a hit for multiple ADMET / activity properties simultaneously.
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。
Computes and interprets treatment effect measures (OR, RR, RD, HR, NNT) with calibrated confidence intervals (Wilson, Newcombe, Miettinen-Nurminen, MOVER, profile likelihood, Bender NNT) and reports marginal vs conditional estimands per FDA 2023 covariate adjustment guidance. Use when reporting treatment effects in confirmatory trials, comparing effect sizes across studies, or constructing forest plots.
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 a carrier asks how to spec a new tractor — engine choice (X15 vs DD16 vs PACCAR MX), transmission (manual vs automated manual vs automatic), axle ratio, sleeper size, fuel tanks, aerodynamic options, electronic features, warranty, fleet-spec vs premium-spec tradeoffs, and how spec choices affect resale value.
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 this skill when the user asks about state-specific trucking taxes BEYOND IFTA — NY Highway Use Tax (HUT), KY Kentucky Intrastate Tax (KIT), Oregon Weight-Mile Tax, New Mexico Weight-Distance Tax, Connecticut Highway Use Fee, or other state-level road-use taxes for commercial vehicles. Covers thresholds, rates, registration, filing cadence, and how these differ from IFTA. Reference state DOT/DOR resources.
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 this skill when the user asks about getting/maintaining motor carrier operating authority — USDOT number, MC number, BOC-3 process agent, insurance filing (MCS-90, BMC-91/91X), Unified Carrier Registration (UCR), International Registration Plan (IRP), International Fuel Tax Agreement (IFTA), state-level permits (NY HUT, KY weight distance, OR weight-mile, NM weight distance), new entrant safety audit, intra vs interstate, for-hire vs private. Cite 49 CFR 365, 387, 390.
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 a carrier asks about the full pre-employment investigation process for new CDL drivers under 49 CFR 391.23, 391.25, 391.27, 382.413, and 391.21 — the complete file build required before a driver operates a CMV. Covers application, MVR, employment history, drug/alcohol history, PSP, Clearinghouse, road test, and documentation requirements.
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 a carrier asks how to improve MPG, lower fuel cost per mile, manage fuel surcharge, set up a fuel-purchase strategy (in-network vs out-of-network), train drivers on fuel-efficient driving, spec new tractors for fuel economy, or benchmark fleet MPG against industry averages.
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 this skill when managing post-SAP follow-up testing for a driver. Covers the testing cadence, recordkeeping, what happens if a test is missed, and how to transition the schedule to a new employer.
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 this skill when the user asks about DOT audits, FMCSA compliance reviews, new entrant safety audits (NESA), focused investigations, what auditors actually ask for, audit document checklists, common audit failures, the audit ratings (Satisfactory / Conditional / Unsatisfactory), or how to prepare for an upcoming audit. Cite 49 CFR 385.
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 this skill when implementing or renewing supervisor reasonable-suspicion training. Covers the 60-minute alcohol + 60-minute drug training requirement, recordkeeping, and refresher cadence.
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 a carrier needs to build an internal dispatcher training program, onboard new dispatchers, or certify dispatcher competency. Covers job description, required skills, training modules, certification frameworks (CDS, CTP), shadowing, evaluation, and career path.
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。