「ref」の検索結果

Delegate coding tasks to OpenCode CLI agent for feature implementation, refactoring, PR review, and long-running autonomous sessions. Requires the opencode CLI installed and authenticated.

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。

Complete guide to using and extending Hermes Agent — CLI usage, setup, configuration, spawning additional agents, gateway platforms, skills, voice, tools, profiles, and a concise contributor reference. Load this skill when helping users configure Hermes, troubleshoot issues, spawn agent instances, or make code contributions.

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 requests to generate, create, or imagine videos. Supports structured prompts and reference image for guided generation.

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 requests to generate, create, imagine, or visualize images including characters, scenes, products, or any visual content. Supports structured prompts and reference images for guided generation.

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 requests to generate, create, or improve documentation for code, APIs, libraries, repositories, or software projects. Supports README generation, API reference documentation, inline code comments, architecture documentation, changelog generation, and developer guides. Trigger on requests like "document this code", "create a README", "generate API docs", "write developer guide", or when analyzing codebases for documentation purposes.

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 large codebase migrations and multi-file refactors. Uses the Composio CLI to coordinate issue tracking, batched PRs, and CI verification while the agent executes the transforms locally across hundreds of files.

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。

Extract structured domain knowledge from AI models in-session or from local open-source models via Ollama. No API key needed.

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。

X-ray any AI model's behavioral patterns — refusal boundaries, hallucination tendencies, reasoning style, formatting defaults. No API key needed.

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。

Azure Communication Services common utilities for Java. Use when working with CommunicationTokenCredential, user identifiers, token refresh, or shared authentication across ACS services.

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。

Centralized configuration management with feature flags and dynamic refresh.

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。

Angular performance optimization and best practices guide. Use when writing, reviewing, or refactoring Angular code for optimal performance, bundle size, and rendering efficiency.

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。

Understand audience demographics, preferences, behavior patterns, and engagement quality across Facebook, Instagram, YouTube, and TikTok.

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。

Assembles a genome de novo from Illumina short reads with SPAdes (isolate/careful/sc/meta/plasmid/rna modes), MEGAHIT (low-memory, huge datasets), Unicycler (bacterial finishing/hybrid), MaSuRCA (large hybrid), ABySS (Bloom-filter), and Platanus (heterozygous diploids), using multi-k de Bruijn graphs. Covers the repeat-resolution limit, why N50 plateaus at the genome not the depth, GenomeScope2 k-mer profiling first, the heterozygosity/haplotig trap, error-correction erasing rare alleles, GC dropout, and NG50/auN/BUSCO reporting. Use when assembling a bacterial isolate, fungal, small-eukaryotic, single-cell, or metagenome genome from Illumina reads, or when deciding whether short reads can even produce the assembly being asked for.

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。

Orders and orients assembled contigs into chromosome-scale scaffolds from long-range linking data, inserting N-gap spacers (adds no sequence). Covers Hi-C/Omni-C scaffolding (YaHS, SALSA2, 3D-DNA/Juicer), Hi-C read-mapping prerequisites (map each end separately, no mate rescue, dedup, enzyme-aware), reading the contact map for misjoins/inversions/false-duplications, manual curation in Juicebox/PretextView (the VGP/DToL standard), reference-guided scaffolding (RagTag) and its karyotype-erasure hazard, genetic-map (ALLMAPS) and Bionano optical-map integration, chimera-breaking before scaffolding, gap-filling, and telomere/contig-vs-scaffold-N50 QC (tidk). Use when turning contigs into chromosomes with Hi-C, integrating a linkage map or optical map, choosing a scaffolder by available linking data, or judging whether a chromosome-scale assembly is trustworthy.

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。

Evaluates genome assembly quality across the three orthogonal axes - contiguity (QUAST auN/NG50/NGx, not bare N50), completeness (BUSCO/compleasm gene-space plus Merqury k-mer completeness), and correctness (reference-free Merqury QV, Inspector/CRAQ structural errors, asmgene false-duplication/collapse). Covers why N50 is the most-gamed metric, why QV measured on the polishing reads is circular, distinguishing uncollapsed haplotigs from real WGD, and the EBP/VGP 6.C.Q40 standard. Use when judging whether an assembly is good enough to annotate or publish, comparing assemblers, diagnosing a fragmented or duplicated assembly, or assessing a phased diploid assembly.

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。

Assigns pathogen lineages (SARS-CoV-2 Pangolin via UShER mode; Nextclade clade + QC; pango-designation alias_key.json resolution) and tracks variant frequencies over time using Nextstrain (Augur + Auspice), wastewater deconvolution (Freyja, COJAC, alcov, lineagespot), lineage fitness modelling (Wenseleers / Bedford-Figgins multinomial logistic), and recombinant detection (3SEQ, RDP4, Bolotie). Covers Pangolin pangolin-data version pinning (mandatory for reproducibility), Nextclade dataset versioning (lineage-defining mutations change with dataset), Freyja barcode forward-only date constraint, ARTIC primer scheme version churn (V3 / V4 / V4.1 / V5.3.2 / Midnight 1200) with documented dropout regions, recombinant X-prefix Pango designation lag, GISAID vs INSDC dual-deposition tensions, and the Karthikeyan 2022 wastewater early-detection signal with explicit reproducibility caveats. Use when assigning Pango lineages and Nextclade clades to viral consensus sequences, building Nextstrain Augur surveillance pipelines, deconvolving wastewater pooled samples into lineage frequencies with Freyja, tracking lineage frequencies and growth advantages over time, pinning pangolin-data / Nextclade dataset versions for reproducibility, handling ARTIC primer dropouts (V4.1 amplicons 64 / 76 / 88-90), or running variant surveillance for SARS-CoV-2 / influenza / Mpox / RSV / H5N1 / measles.

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。

Aligns sample metadata with count matrices and constructs design matrices for downstream DE, handling the alphabetical-reference-level trap (relevel BEFORE DESeq), LRT reduced-model rules, the interaction-term resultsNames trap, continuous-covariate scaling and splines, repeated measures via duplicateCorrelation or dream, high-cardinality categorical pseudo-singular designs, sample swap detection via XIST/RPS4Y1 expression and somalier/NGSCheckMate genotypes, SABV (sex-as-biological-variable) mandate, Simpson's-paradox collapsing of technical replicates, and the `~ 0 + group` parameterization for clean contrasts. Use when building a design matrix, troubleshooting reversed fold-change direction, encoding paired or repeated-measures designs, detecting sample swaps, deciding sex-as-covariate, or aggregating technical replicates.

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。

Maps between gene identifier systems (Ensembl, Entrez, HGNC symbol, UniProt, RefSeq, MANE) using AnnotationDbi, biomaRt, mygene, pyensembl, and Ensembl REST. Encodes Ensembl version stripping with GENCODE _PAR_Y preservation, the Ziemann 2016 Excel autocorrect debacle and Bruford 2020 HGNC renames (SEPT*->SEPTIN*, MARCH*->MARCHF*, MARC*->MTARC*, DEC1->DELEC1), OCT4/POU5F1 alias resolution, biomaRt archive endpoints for release pinning, the `filters` (plural) gotcha, MANE Select for clinical reporting, cross-species orthology via Ensembl Compara / OMA / OrthoDB, and tx2gene construction for tximport. Use when converting gene IDs across systems, handling renamed symbols, building tx2gene, pinning to a specific Ensembl release for reproducibility, or mapping cross-species orthologs.

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 genomics experiments so technical nuisance variation (batch, lane, plate, flow cell, operator, reagent lot, processing day) is balanced against the biological variable of interest and therefore estimable rather than confounded, using constrained sample-to-batch assignment (designit, OSAT), the confounder/mediator/collider distinction, and the principle that no post-hoc correction recovers a fully confounded design. Covers detecting hidden batches with surrogate variable analysis, a decision table for downstream correction (ComBat-seq, RUVSeq, SVA) whose execution is deferred to differential-expression/batch-correction, and reproducibility metadata. Use when assigning samples to sequencing batches/lanes/plates, avoiding batch-condition confounding, deciding whether a design is salvageable by correction, choosing a correction method, or estimating the number of hidden batches. For the experimental unit, randomization, and blocking concepts see experimental-design/randomization-blocking.

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。

Detects m6A modifications from Oxford Nanopore direct-RNA-sequencing (ONT DRS) signal data using m6Anet (Hendra 2022 *Nat Methods* 19:1590; multiple-instance-learning neural network over DRACH 5-mer signal). Covers the required upstream pipeline (Dorado / Guppy basecalling -> minimap2 transcriptome alignment with `-ax map-ont -uf -k14 --secondary=no` -> nanopolish eventalign with `--scale-events --signal-index` (m6Anet-required) plus `--summary` / `--threads` housekeeping -> m6anet dataprep -> m6anet inference), per-site vs per-read probability interpretation including the `mod_ratio` per-site stoichiometry column, the DRACH-only modeling constraint, minimum-coverage thresholds (20-50 reads per site for stable probability estimates), multi-condition comparison via xPore (Pratanwanich 2021 *Nat Biotechnol* 39:1394), Nanocompore (Leger 2021 *Nat Commun* 12:7198), ELIGOS (Jenjaroenpun 2021 *NAR* 49:e7), and Dorado native modification calling (RNA004 chemistry, 2024+), reference-transcriptome version pinning, the cDNA-vs-DRS chemistry distinction (cDNA-Nanopore CANNOT be used for modification detection), and orthogonal validation against MeRIP / GLORI. Use when calling m6A from ONT DRS without immunoprecipitation, choosing m6Anet vs xPore vs Nanocompore vs ELIGOS vs Dorado native, interpreting per-site `probability_modified` vs `mod_ratio` vs per-read modification probabilities, comparing methylation between conditions from ONT data, deciding between m6Anet for known DRACH sites and Dorado/Remora for genome-wide screening, pinning RNA002 vs RNA004 chemistry and basecaller model versions, or troubleshooting eventalign / dataprep failures.

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。

Estimates time-scaled phylogenies, molecular clock rates, effective reproduction number R_e (or R_t), and population dynamics from dated pathogen genomes using TreeTime (maximum-likelihood) and BEAST2 (Bayesian; strict / uncorrelated lognormal / ORC clocks; constant / exponential / Bayesian Skyline / Skygrid / BICEPS / Birth-Death-Skyline / sampled-ancestor BDSKY priors; structured coalescent via MASCOT). Covers root-to-tip clock signal QC via TempEst, date-randomisation tests (Ramsden 2009; Duchêne 2015), recombination masking via Gubbins and ClonalFrameML before clock inference for recombining bacteria, BDSKY origin-vs-rootHeight pitfalls, sampling-bias correction (Volz & Frost 2014; preferential-sampling extensions), MASCOT structured coalescent for migration, BICEPS-vs-BSP skyline choice, multi-chain BEAST2 convergence diagnostics, and reconciliation between phylodynamic R_e and case-based R_t. Use when dating outbreak origins, estimating substitution rates, inferring R_e through time, building time-calibrated Nextstrain Augur trees, choosing between strict and relaxed clocks, fitting Birth-Death-Skyline (Stadler 2013) models, diagnosing temporal-signal failure, masking recombination before clock inference for *Streptococcus pneumoniae* / *Neisseria gonorrhoeae* / *Klebsiella* / *E. coli* phylodynamics, running MASCOT for structured-population analyses, or using UShER for pandemic-scale placement.

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。

Quantifies biodiversity from species abundance/incidence tables using Hill numbers (iNEXT) with coverage-based rarefaction-extrapolation (Chao & Jost 2012), asymptotic richness via Chao1/ACE/jackknife as a lower bound, Baselga turnover/nestedness partition with the Podani alternative as sensitivity check, mandatory Hellinger transformation before ordination (Legendre & Gallagher 2001), Faith PD and SES_MPD/SES_MNTD with explicit null-model choice, and Maire 2015 functional-diversity dimensionality optimization. Use when comparing diversity across sites with unequal sampling effort, picking the right richness estimator for singleton-heavy amplicon data, partitioning beta diversity into turnover vs nestedness, reporting Hill-number effective species counts rather than raw entropies, computing SES_MPD with explicit null-model justification, or deciding whether to apply standard metrics to compositional amplicon data. Not for clinical 16S microbiome diversity (see microbiome/diversity-analysis).

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。

Bulk-query Ensembl BioMart (and other BioMart instances) for cross-database ID mapping, gene/transcript/exon coordinates, and ortholog tables. Use when batch-converting Ensembl IDs to other namespaces (HGNC, RefSeq, UniProt, Entrez), pulling gene coordinate tables for thousands of genes, building ortholog wide-tables across species, or replacing slow Ensembl REST loops with one-shot bulk export. Encodes BioMart's XML query format, R biomaRt vs Python pybiomart trade-off, mart-vs-dataset hierarchy, and the URL endpoint that's BioMart-specific (separate from rest.ensembl.org).

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。