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概要と使いどころ
Implement WCAG 2.2 AA/AAA compliance with automated testing, keyboard navigation, screen reader support, and focus management. Activate on: accessibility audit, WCAG compliance, keyboard navigation, screen reader, aria attributes, axe-core, focus trap. NOT for: design-level accessibility review (use design-accessibility-auditor), color contrast only (use css-in-js-architect).
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。
Design and safely evolve data models, schemas, migrations, queries, indexes, and ORM data-access boundaries. Discover the existing engine and migration source before proposing syntax.
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。
CEO/founder-mode plan review. Rethink the problem, find the 10-star product, challenge premises, expand scope when it creates a better product. Four modes: SCOPE EXPANSION (dream big), SELECTIVE EXPANSION (hold scope + cherry-pick expansions), HOLD SCOPE (maximum rigor), SCOPE REDUCTION (strip to essentials). Use when asked to "think bigger", "expand scope", "strategy review", "rethink this", or "is this ambitious enough". Proactively suggest when the user is questioning scope or ambition of a plan, or when the plan feels like it could be thinking bigger. Use AFTER a plan exists. For validating the idea itself before planning, use /mk:office-hours first.
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。
Design and safely evolve data models, database schemas, migrations, queries, indexes, and ORM data-access boundaries. Use for data-model, migration, query, index, ORM schema, or datastore-selection tasks. Discover the existing engine and migration source before proposing syntax.
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。
Create diagrams and visualizations using Mermaid.js v11 syntax. Use when generating flowcharts, sequence diagrams, class diagrams, state diagrams, ER diagrams, Gantt charts, user journeys, timelines, architecture diagrams, or any of 24+ diagram types. Supports JavaScript API integration, CLI rendering to SVG/PNG/PDF, theming, configuration, and accessibility features. Essential for documentation, technical diagrams, project planning, system architecture, and visual communication.
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。
Structured logging conventions for DraftForge backend (Django/Celery/Daphne) and frontend (React). This skill should be used when adding log statements, creating new modules, debugging with logs, or reviewing logging patterns. Covers structlog usage, system/subsystem taxonomy, required fields, log levels, and Grafana query 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。
Quantitative agent evaluation using 4-metric framework (correctness/step_ratio/tool_call_ratio/latency_ratio) with ideal trajectory annotation and capability-categorical taxonomy. Use when measuring agent efficiency, comparing agent variants, or gating new agents through correctness→efficiency phases. Complements harness-eval (SE benchmarks) and evaluator-optimizer (qualitative rubric).
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。
Pick a visual direction before generating any creative. Produces 3-6 named, distinct visual directions (e.g. premium minimal, bold maximalist, lifestyle/editorial, retro, techno-brutalist) with rationale + sample prompts, then generates one reference image per direction so the user can choose. Use when the user says "explore visual direction", "what style should this be", "give me directions/looks", or before ads/scene/shot work when no style is locked. Part of the Creative Production set.
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。
Apply or adapt a dual-theme soft-neumorphic UI board for dashboards, workspaces, settings panels, admin tools, and design-system previews. Use when Codex should build or restyle interface work in a tactile soft-neumorphic style with both light and dark themes, shared semantic tokens, large radii, raised surfaces, inset fields, restrained accent states, and a complete preview board covering foundations, controls, navigation, data display, and feedback.
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。
Apply or adapt a dual-theme neo-glow control board for UI kits, design-system previews, settings surfaces, admin panels, control rooms, and dense operational interfaces. Use when Codex should build or restyle interface work with bright white or dark control surfaces, compact neumorphic cards, vertical icon rails, blue/cyan halo accents, glowing active states, luminous toggles, and a board focused on control states, inputs, and specification details.
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。
Apply or adapt a complete dual-theme UI specification board for product interfaces. Use when Codex should build or restyle dashboard, admin, workspace, settings, or system UI with both light and dark themes, shared semantic tokens, structured component states, and a full preview board covering foundations, controls, navigation, data display, and feedback 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。
Apply or adapt a dual-theme liquid glass UI board for premium dashboards, workspace launchers, settings surfaces, design-system previews, and glossy interface concepts. Use when Codex should build or restyle interface work with frosted translucent panels, aurora glow accents, pill controls, circular glass actions, blurred color blooms, and a polished light-plus-dark material system.
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。
Look up company/business data through Openapi - Italian companies by VAT/tax code or name, European and worldwide company search, shareholders, UBO, AML, PEC/SDI codes, company monitoring. Use for questions like "find data about company X", "who owns VAT number Y", "search companies in France/EU/worldwide".
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。
Italian electronic invoicing and payments through Openapi - send/receive electronic invoices via SDI (Sistema di Interscambio), digital receipts (scontrino elettronico), legal storage, and bill payments (MAV, RAV, PagoPA, car tax). Use for fatturazione elettronica and bill payment tasks.
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。
Build volcano and MA plots from differential-expression / association results with LFC shrinkage, FDR-adjusted thresholds, sensible label placement, and axis-truncation conventions. Covers EnhancedVolcano, ggplot2, matplotlib, and the apeglm/ashr/normal shrinkage decision. Use when visualizing differential-expression results (RNA-seq, ChIP-seq, ATAC-seq, proteomics) or any per-feature effect-size + p-value table.
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 forest plots (HR, OR, RR, beta-coefficient summaries with CIs) and funnel plots (meta-analysis publication-bias diagnostics) using forestplot, metafor, ggforest, and MendelianRandomization with proper axis-scaling, summary-diamond placement, subgroup nesting, and Egger / trim-and-fill asymmetry tests. Use when summarizing effects across subgroups, trials, or instruments — meta-analysis, Mendelian randomization, subgroup HRs.
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。
Retrieve records from NCBI databases using Biopython Bio.Entrez (EFetch, ESummary). Use when downloading sequences, fetching GenBank/GenPept records, getting document summaries, parsing nested XML, navigating GI deprecation, choosing between rettype+retmode combinations, and parsing into Biopython SeqRecord/SwissProt objects. Covers nucleotide, protein, gene, pubmed, sra, gds, taxonomy, snp, clinvar.
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。
Delimits putative species boundaries from molecular data within the de Queiroz 2007 unified-lineage framework using ASAP (Puillandre 2021 successor to ABGD), mPTP C++ (Kapli 2017 successor to bPTP; bPTP is Python NOT R), GMYC single/multi-threshold (Pons 2006; Fujisawa 2013), multilocus BPP v4 with prior calibration from data (NOT defaults; Yang 2015), SNAPP + BFD* for SNP delimitation, DELINEATE (Sukumaran 2021) speciation-process modeling to address Sukumaran & Knowles 2017 PNAS critique that MSC delimits structure not species, integrative-taxonomy congruence (Padial 2010; Carstens 2013), Dsuite for introgression testing before sister claims (Malinsky 2021), and Meyer & Paulay 2005 barcoding-gap-absence caveat. Use when delineating species from DNA barcoding data, resolving cryptic complexes, choosing among ASAP/mPTP/BPP/DELINEATE, calibrating BPP priors, distinguishing introgression from ILS, or applying the Sukumaran-Knowles oversplitting correction.
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 species-environment relationships with constrained ordination (CCA, RDA, db-RDA), variance partitioning, indicator species (indicspecies IndVal.g group-equalized), PERMANOVA paired MANDATORILY with PERMDISP (Anderson & Walsh 2013; dispersion confounds centroid tests), Joint Species Distribution Models (HMSC, sjSDM, gjam) with explicit rejection of "residual covariance equals biotic interaction", phylogenetic community ecology (SES_MPD/MNTD), trait-environment via RLQ + fourth-corner with corrected modeltype=6 (Dray 2014), bipartite network metrics (NODF, modularity) with curveball null (Strona 2014), and Mantel-test replacements (dbRDA, GDM) for spatial data. Use when testing how environmental gradients structure communities, identifying habitat indicator taxa, partitioning variance among predictors, deciding whether PERMANOVA significance is location vs dispersion, picking among HMSC/sjSDM/gjam, or replacing Mantel tests for landscape data.
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 and analyzes pooled prime-editor (PE) screens for installing precise genetic variants without bystander confounding. Covers pegRNA design with PRIDICT and PRIDICT2 for predicting per-pegRNA editing efficiency, pegRNA architecture (spacer + scaffold + PBS + RTT), PE2/PE3/PE3b/PEmax variants, MOSAIC in situ saturation mutagenesis, the PRIME pooled-screen methodology (Ren 2023; ~3,699 ClinVar variant screens), chromatin context as a major locus-level determinant of PE efficiency, scaffold-incorporation and indel byproduct quantification with CRISPResso2, and the cross-modal validation strategy of PE + base-editor screens for variant function. Use when designing a pegRNA library for variant installation, choosing between BE and PE for a specific edit, predicting pegRNA efficiency before library synthesis, analyzing PE screen output, distinguishing intended-edit from scaffold-incorporation, or scaling PE screens to thousands of 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。
Model gene-family birth-death dynamics across a species tree using CAFE5 (Mendes et al 2020 Bioinformatics 36:5516 gamma-distributed rate categories), CAFE5-error (annotation-error-aware), Count (Csurös 2010 ancestral state reconstruction), BadiRate (Librado 2012 likelihood + parsimony), DupliPHY-Family, and ALE/AleRax (for per-family DTL; see [[gene-tree-species-tree-reconciliation]]). Test lineage-specific gene-family expansions and contractions, distinguish biological dynamics from annotation artifacts, account for assembly fragmentation, identify functional enrichment in expanded / contracted families. Use when correlating gene-family changes with phenotype evolution, ranking lineages by adaptive gene-family-rate shifts, post-WGD dosage-balance analysis, or building Birth-death models from OrthoFinder presence/absence matrices.
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 codon usage and calculate CAI (Codon Adaptation Index), RSCU, and Nc with Biopython, and produce naive max-CAI codon-optimized sequences. Use when scoring a gene's codon bias against a host, optimizing a CDS for heterologous expression, or studying synonymous codon 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。
Detect positive (diversifying / episodic / pervasive) selection using codon dN/dS frameworks. Implements PAML codeml site models (M0/M1a/M2a/M7/M8/M8a), branch models, branch-site model A (Zhang 2005), and HyPhy methods (BUSTED, BUSTED-S, BUSTED-MH, BUSTED-PH, MEME, FEL, FUBAR, aBSREL, SLAC, RELAX, GARD, FUBAR-MH). Includes McDonald-Kreitman framework (asymptotic alpha, impMKT, polyDFE, DFE-alpha, GRAPES) for within-species + divergence inference, RERconverge for trait-correlated rate shifts, CSUBST for convergent substitution, and PhyloAcc for accelerated noncoding evolution. Use when testing adaptive evolution at codons, branches, or full gene; running GARD recombination pre-screen; controlling alignment-error and gBGC false positives; reconciling PAML vs HyPhy results; or performing genome-scale selection scans.
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。