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Accessibility testing for web applications using Playwright (@playwright/test) with TypeScript and axe-core. Use when asked to write, run, or debug automated accessibility checks, keyboard navigation tests, focus management, ARIA/semantic validations, screen reader compatibility, or WCAG 2.1 Level AA compliance testing. Covers axe-core integration, POUR principles (perceivable, operable, understandable, robust), color contrast, form labels, landmarks, and accessible names.

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Adds accessibility annotations (ARIA labels, roles, alt text) to make web content accessible. Use when user asks to "add accessibility", "make accessible", "add aria labels", "wcag compliance", or "screen reader support".

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。

Expert in web accessibility (WCAG 2.1/2.2 AA/AAA compliance), ARIA patterns, keyboard navigation, screen reader testing, color contrast, focus management, and automated accessibility testing

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 should be used when the user asks for an accessibility review, accessibility audit, a11y check, WCAG compliance review, screen reader audit, "is this accessible?", "does my site meet WCAG?", "check my component for accessibility", "accessibility overview of this page", "review accessibility of", "check for a11y issues", "fix accessibility issues", or any request to audit a web page, component, or codebase for WCAG 2.2 AA compliance.

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。

Accessibility audit for CSS covering focus styles, color contrast, text sizing, screen reader support, and WCAG compliance. Provides actionable fixes. Use when auditing accessibility or fixing a11y issues.

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。

Capture Unity Editor windows or Game View rendering as PNG. Use for visual checks, debugging, documentation, or annotated UI element coordinates.

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。

Raycast from Camera.main through a Game View coordinate. Use when you need to check what a screenshot coordinate would hit in 3D physics before clicking or long-pressing with simulate-mouse-input.

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。

Simulate PlayMode EventSystem UI mouse actions using top-left Game View coordinates. Use for UI clicks, long-presses, or drags from annotated screenshots.

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。

Reply to an applicant for an employer's Otameshi Tenshoku listing: match the application against the listed requirements item by item on what the applicant actually stated, list the questions still open, and draft an invitation, a request for clarification, a hold notice or a decline that sticks to the listed conditions, keeps other applicants and internal notes out, and presents any new work or dates as a proposal rather than an agreed term. Use when a hiring manager, recruiter or small-business owner has applications to answer, wants to know what to ask before inviting, or has to decline; do not use to judge an applicant's aptitude or predict hiring outcomes, to screen on personal attributes unrelated to the work, to design the work (trial-work-design) or the listing (trial-listing-draft), or to send the message or update applicant records on the user's behalf.

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。

Check, from the employer's side, what has been put in writing before a paid Otameshi Tenshoku assignment starts under gyomu-itaku: item by item whether the deliverable, delivery date, fee and how it is calculated, payment due date, who pays, out-of-scope work, revision limits, company support, rights, confidentiality and termination are stated in a contract, purchase order or email rather than in chat or by word of mouth, count the days from delivery to payment, and find pay tied to satisfaction or hiring, unpaid or unlimited revisions and one-sided termination. Use when a hiring manager, recruiter or small-business owner is about to offer terms to a candidate, has been asked by a candidate what is still unwritten, or needs the conditions written for a platform contract screen; do not use to judge whether the terms are lawful or the fee fair, to compute withholding or consumption tax, for employment contracts, or to sign, send or conclude the contract on the user's behalf.

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。

Assemble a Japanese shokumu-keirekisho (career history document) from an experience inventory, choosing between reverse-chronological, chronological, and skills-based layouts, mapping the target posting's requirements to the experiences that can support them, and drafting the summary, work history, skills, and self-PR so that each sentence stays within what its evidence supports and confidential details are abstracted. Use when a user wants to write or rebuild a whole shokumu-keirekisho for a specific posting or as a general version, or wants to decide which experiences to include and in what order; do not use to invent experience, numbers, titles, or qualifications, to predict whether it will pass screening, to fill in a rirekisho form, or to submit it anywhere.

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。

Triage a GitHub issue that already has the cc-issue-created label and is assumed ready to start. Inspect the comment history first to decide whether human confirmation is needed (cc-need-human-check, highest priority — reserved for issue-level deadlock, or for confirmation items answer-issue-questions already investigated and explicitly marked unanswerable); otherwise close the issue as not needed, delegate unanswered confirmation items in the last comment or the description to answer-issue-questions (cc-answer-issue-questions), refresh a description stale relative to settled comment-history content (cc-update-issue), or move to execution (cc-exec-issue). Any UI-changing issue is routed to the design-first flow (cc-create-ui-design) before execution; the exceptions are an issue whose description already names the .pen design to implement against, and a minor UI change that introduces no new visual decision (no new screen/section, no layout change, no new reusable component, no visual expression outside existing design tokens/components). Design files are never edited in the implementation PR. Dependency checks are out of scope.

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。

Spawn a new Claude Code session with remote control enabled (default: a detached background session — via screen on macOS/Linux, via a minimized PowerShell Start-Process on Windows — fully remote-safe, no approvals/windows), so it appears in the mobile Claude Code session picker. Takes an optional directory path; defaults to the current working directory. Use when the user runs /rc-new.

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。

Read the network instead of the DOM. Snoop JSON API responses, wait for a request instead of sleeping, collect items while scrolling, record a QA flight trace (jsonl, HAR, screenshots), and intercept requests with routes.

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 whether two genomic interval sets overlap (colocalize) more than expected by chance using a permutation test against a structured-genome null model. Covers bedtools fisher (analytic 2x2 screen), bedtools shuffle + jaccard permutation, GAT (isochore/GC-conditioned simulation with FDR), regioneR (flexible permutation, randomizeRegions vs circularRandomizeRegions, localZScore), LOLA (universe-relative Fisher against a region database), and GREAT/rGREAT (regulatory-domain binomial + hypergeometric for ontology-from-regions). Stresses the universe/background choice, matched background, blacklist exclusion, and multiple-testing control. Use when asking whether peaks/regions are enriched at enhancers/TFBS/features, scoring region-set colocalization or region-set enrichment, comparing CNV/SV concordance, or turning an overlap count into a defensible p-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。

Detects acquired antimicrobial-resistance determinants and chromosomal point-mutation resistance in bacterial assemblies using AMRFinderPlus, ResFinder 4.0 (acquired + PointFinder), CARD-RGI, abritAMR, staramr, and species-specific callers (TB-Profiler, Mykrobe). Harmonises cross-tool output via hAMRonization, contextualises determinants with mobile-genetic-element annotation (MOB-suite, PlasmidFinder, MobileElementFinder, ICEberg), predicts phenotype against EUCAST or CLSI breakpoints, and translates calls into WHO GLASS reporting categories. Use when screening clinical or surveillance isolates for AMR, distinguishing acquired vs intrinsic vs point-mutation resistance, calling rpoB / katG / pncA / gyrA / mgrB mutations, reconciling AMRFinderPlus vs RGI vs ResFinder disagreement, contextualising carbapenemases or mcr alleles on plasmids, predicting susceptibility from genotype against the WHO Mtb 2nd-edition catalogue, or building a hAMRonized multi-lab AMR surveillance pipeline.

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。

Processes eDNA metabarcoding from raw paired-end reads to species tables, navigating ASV (DADA2, UNOISE3) vs OTU (swarm v2) decision (Callahan 2017 vs Schloss multi-copy-16S critique), marker/primer choice (Leray COI, MiFish 12S, 515F/806R 16S, ITS2) with primer-specific bias, OBITools3 v3 command-name break (obi stats plural; .tar.gz taxonomy), tag-jumping with dual-indexing (Schnell 2015; NovaSeq 10x MiSeq), decontam as screening-not-classifier (Davis 2018), read-counts-not-abundance critique (Lamb 2019), site-occupancy modeling (Ficetola 2015), Naive-Bayes calibration limits (Bokulich 2018), and eDNA decay (Strickler 2015). Use when going from raw eDNA FASTQ to species tables, picking marker + denoising pipeline, deciding whether read counts represent abundance, applying occupancy modeling, configuring OBITools3 v3, or interpreting decontam output. Not for clinical 16S microbiome (see microbiome/amplicon-processing).

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。

Build genome-browser-style multi-track figures with pyGenomeTracks (config-driven), Gviz (R), and IGV batch screenshotting. Covers BigWig coverage tracks, BED/peak overlays, gene-model rendering, Hi-C matrix tracks, BedPE link arcs, spike-in-aware normalization, and the bamCoverage --normalizeUsing trap. Use when producing publication figures of genomic loci with stacked aligned tracks (coverage, peaks, genes, interactions) for ChIP-seq, ATAC-seq, RNA-seq, Hi-C, or generic locus visualization.

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 CRISPR editing outcomes with CRISPResso2 (Clement 2019 Nat Biotechnol) across Cas9-nuclease (indels, HDR), CBE and ABE base editors (target conversion + bystander), and prime editor (pegRNA-templated) modes. Covers single-amplicon (CRISPResso), multi-sample batch (CRISPRessoBatch), pooled-amplicon (CRISPRessoPooled), WGS off-target (CRISPRessoWGS), and sample-comparison (CRISPRessoCompare) workflows; quantification-window math that controls what is called edited; substitution-vs-indel diagnostic to distinguish BE from Cas9 contamination; MMEJ deletion pattern interpretation; allele-frequency tables; and failure modes from amplicon misalignment or contamination. Use when quantifying editing from amplicon sequencing, choosing CRISPResso mode by design, distinguishing intended edits from bystanders and indel byproducts, debugging low-alignment runs, or generating publication-grade editing reports.

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。

Detect horizontal gene transfer (HGT / LGT) using compositional methods (GC%, codon usage, tetranucleotide z-scores via SIGI-HMM, AlienHunter, IslandViewer 4, IslandPath-DIMOB), phylogenetic-incongruence methods (AvP, HGTphyloDetect, ALE / GeneRax / AleRax reconciliation, RANGER-DTL), and BLAST-distribution methods (HGTector v2, DarkHorse, Alien Index). Use when screening prokaryote genomes for genomic islands and HGT events, distinguishing HGT from incomplete lineage sorting / differential gene loss / hybridization, mapping donor lineages via phylogenetic placement, separating eukaryotic HGT from contamination, ruling out gBGC as a false signal, or quantifying DTL rates with ALE/GeneRax on bacterial trees.

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 genome-to-genome distances (ANI, AAI, dDDH, k-mer Mash) and assign taxonomic classifications using skani (Shaw 2023), FastANI (Jain 2018), pyani / pyANI ANIb / ANIm, OrthoANI (Lee 2016), AAI (amino-acid identity), dDDH via TYGS / GGDC, GTDB-Tk (Chaumeil 2020 standard prokaryote taxonomy), and Mash MinHash (Ondov 2016). Use when delineating prokaryote species (95% ANI threshold; Jain 2018 Nat Commun 9:5114), assigning genomes to GTDB taxonomy with ANI radius, computing genome similarity matrices for clustering, classifying archaea, evaluating MAG (metagenome-assembled genome) species assignment, applying skani for fast metagenomic ANI screening, or reconciling 16S rRNA-based taxonomy with whole-genome ANI.

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。

Prioritizes rare-disease variants from trio/quad WES/WGS with de novo (DeNovoGear, Triodenovo), compound-heterozygous phasing (WhatsHap), mosaic VAF tiering, phenotype-driven ranking (Exomiser, Phen2Gene, AMELIE), ClinGen gene-disease validity gating, and ACMG SF v3.2 secondary findings reporting. Use when running diagnostic exome / genome pipelines, identifying candidate Mendelian disease genes, screening for incidental findings, or auditing VUS reclassification cycles. The ACMG/AMP classification framework (PVS1 decision tree, Pejaver PP3/BP4 calibration, Tavtigian point system) is in clinical-databases/acmg-classification.

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。

Queries PharmGKB / CPIC / DPWG for drug-gene interactions; calls CYP2D6/CYP2C9/CYP2C19/DPYD/TPMT/NUDT15/UGT1A1/SLCO1B1 star alleles and phenotype with PharmCAT, Cyrius (CYP2D6 structural variants), Aldy, Stargazer; applies Caudle 2020 activity-score translation. Use when implementing pharmacogenomic-guided prescribing, applying CPIC vs DPWG guidance, screening HLA risk alleles for ICI / antiepileptics / abacavir, or interpreting compound TPMT+NUDT15 thiopurine risk.

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。