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概要と使いどころ
Implements sub-tasks from a task list at .ai/[feature]/tasks.md strictly one at a time, stopping after each to ask the user before starting the next, and keeping the task list and Relevant Files section current. Use when the user wants to review or approve each sub-task before work continues, rather than letting the agent run the list unattended with task-orchestration.
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Drafts a formal Bylaw Amendment Resolution for U.S. corporations, LLCs, or non-profits. Invoke when a board, shareholders, or members approve bylaw changes and need an auditable WHEREAS/RESOLVED record with amendment text, vote tallies, effective date, and secretary certification for minute books, bank submissions, or state filings.
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。
Produces a structured, objective summary of corporate board meetings covering metadata, quorum, attendance, conflicts, resolutions with vote counts, committee reports, and significant corporate actions. Use when summarizing board minutes, creating a corporate governance record, or preparing a post-meeting recap for officers, shareholders, or regulators.
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。
Drafts filing-ready Articles of Incorporation for U.S. non-profit corporations satisfying state corporate law and the IRS organizational test under IRC § 501(c)(3). Enforces mandatory exempt-purpose language, private inurement prohibition, dissolution clause, and political activity limitations. Use when forming a new non-profit, charitable corporation, or tax-exempt entity seeking 501(c)(3) recognition.
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。
Drafts adverse possession complaints and quiet title pleadings. Structures jurisdictional foundations, legal property descriptions, and element-by-element proof of actual, open, continuous, exclusive, and hostile possession. Use when filing adverse possession claims, quiet title actions, or prescriptive ownership pleadings.
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。
Persists and restores mid-session workflow progress across context resets. Use when resuming workflows, saving state, or handling context compaction. Triggers on: handoff, save state, resume workflow.
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-only audit of `.claude/` structural overhead. Reports prioritized "remove X save Y tokens" recommendations against the model context window. NOT for monetary cost tracking — that's /mk:budget. NOT for transcript size monitoring — long-session continuity defers to Claude Code native compaction. NOT for runtime context decisions (what to read, when to compact) — see mk:context-engineering. Use when planning to add context capacity, diagnosing perceived slowdowns, or auditing health.
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。
Prevent session idle during background agent work via SubagentStop prompt hook auto-continuation
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 whenever the user asks to generate, edit, inpaint, restyle, or create bitmap images through OpenAI's Responses API image_generation tool from Codex. This skill calls the Responses API, saves generated base64 image results to files, uses no npm or pip dependencies, works on Linux/macOS/Windows with either Node.js 18+ or Python 3, and reuses Codex's configured API base URL, model, and key instead of asking the user for credentials.
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。
Implement tasks from an OpenSpec change. Use when the user wants to start implementing, continue implementation, or work through 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。
Load relevant memories at session start for context continuity. IMPORTANT: Use this AUTOMATICALLY at the START of every session. Search for memories related to: - Current working directory/project - Recent activity (last 7 days) - Topics the user mentions in their first message Execute silently - do not announce what you're loading.
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。
Implement tasks from an OpenSpec change. Use when the user wants to start implementing, continue implementation, or work through 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。
Generate hand-drawn style diagrams and infographics for recovery education articles. Creates anatomist's notebook aesthetic visuals - brain diagrams, timelines, social comparisons, and process flows using continuous line art, semantic color coding, and margin annotations.
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 CPU usage is high, memory grows unboundedly, goroutines leak, mutex contention shows in traces, or escape analysis suggests excess heap allocation. Triggers: net/http/pprof endpoint exposure, go tool pprof analysis, flamegraph generation, allocs vs inuse_space heap profiles, runtime/trace event timeline, mutex profiling, block profiling, goroutine dumps, GC pressure measurement. NOT for non-Go languages, distributed tracing (use OpenTelemetry skill), or production telemetry pipelines.
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。
Applies Gestalt psychology principles to web layout, spacing, grouping, and visual hierarchy. Proximity, similarity, closure, continuity, figure-ground, common fate, Pragnanz — translated into CSS, component structure, and information architecture. Activate on 'gestalt', 'visual grouping', 'proximity spacing', 'visual hierarchy', 'perceptual organization', 'layout psychology', 'whitespace strategy'. NOT for color theory (use color-theory-palette-harmony-expert), not for accessibility auditing (use design-accessibility-auditor).
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 desktop window systems that feel native on macOS and Windows: split views, inspectors, auxiliary windows, tiling, snap-safe sizes, default placement, and chrome density. Use when a Tauri/Electron/WinUI/SwiftUI app feels awkward, overlapping, over-chromed, or generic, or when you need a real geometry contract for multiple surfaces. NOT for mobile layouts, marketing pages, or CSS-only component styling without multi-window or multi-pane behavior.
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。
Plot per-group distributions of continuous data using boxplots, violins, beeswarms, quasirandom jitter, and raincloud plots with sample-size honesty (Weissgerber 2015), KDE-bandwidth awareness, and N-aware encoding choices. Use when comparing distributions across a small number of groups — expression per cluster, biomarker per arm, scores per condition — and the bar-of-mean default is misleading.
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 time-series and longitudinal RNA-seq for differential expression and trajectory structure. Covers DESeq2 LRT with reduced models, time as factor vs continuous vs natural splines, maSigPro (Nueda 2014 for RNA-seq), ImpulseDE2 with explicit impulse-model failure modes, DREAM for repeated measures via linear mixed models, pseudoreplication avoidance, conditional vs marginal modeling, and trajectory clustering with DPGP, Mfuzz (with Schwämmle 2010 fuzzifier estimation), and splines+k-means. Use when modeling time-course or longitudinal expression, choosing factor vs spline, handling repeated measures from the same subject, avoiding pseudoreplication, clustering temporal trajectories, or selecting between dedicated time-course tools and pairwise+LRT.
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。
Reconstruct ancestral states at internal phylogenetic nodes for sequences (PAML codeml, IQ-TREE --ancestral, GRASP, FastML), discrete traits (corHMM hidden-rate Markov, ape::ace, phytools::make.simmap stochastic mapping, BayesTraits), and continuous traits (phytools::fastAnc, geiger Brownian/OU, RPANDA). Use when designing constructs for ancestral protein resurrection, tracing trait evolution along a tree, performing stochastic character mapping, testing models of trait evolution (BM vs OU vs EB), inferring ancestral genome content via Dollo or DTL reconciliation, or quantifying ancestral-state uncertainty for downstream comparative analyses.
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 combinatorial CRISPR screens covering paired-Cas9 (Big Papi, Najm 2018), enhanced AsCas12a multiplex (enCas12a, DeWeirdt 2021), in4mer 4-guide-array Cas12a (Esmaeili Anvar N et al 2024 Nat Commun 15:3577) and the Inzolia paralog-pair library, paralog-buffering detection (Dede 2020 Genome Biol; Thompson 2021 Nat Commun 12:1302), genetic-interaction (GI) scoring as observed_double_LFC minus expected_additive_double_LFC, synthetic-lethal and synthetic-rescue interaction interpretation, the half-of-essentiality buffered by paralogs phenomenon, multiplex screen statistical analysis with MAGeCK MLE interaction terms, and the relationship to single-cell combinatorial Perturb-seq. Use when designing a paralog or pathway-pair screen, choosing between paired-Cas9 (Big Papi) and Cas12a multiplex (Inzolia), interpreting genetic interaction scores, identifying synthetic-lethal targets for drug development, or scaling beyond single-gene CRISPR screens.
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。
Extracts and assigns COSMIC v3.4 mutational signatures (86 SBS / 11 DBS / 18 ID / 21 CN / 16 SV) from somatic VCFs using SigProfilerSuite, MutationalPatterns, MuSiCal mvNMF, SigNet, or HRDetect. Use when characterizing DNA-damage etiology (BRCA1/2 HRD, MMR-D, POLE, APOBEC3A, UV, tobacco, aflatoxin, 5-FU/SBS17b, platinum, colibactin SBS88), routing PARP inhibitor decisions, or auditing de novo extraction vs refit choice for cohort size.
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。
Discovers de novo motifs and tests known motif enrichment in ChIP-seq, ATAC-seq, or other peak sequences using HOMER, MEME-ChIP (STREME, CentriMo, TOMTOM, FIMO), monaLisa, and AME. Handles background selection (GC-matched, dinucleotide-shuffled, Markov order-2, peak-flanks), motif databases (JASPAR 2024 CORE PWMs, JASPAR 2026 deep-learning collection, HOCOMOCO v12, HOMER built-in), centrally-enriched motif testing, and differential motif analysis. Use when identifying TF binding motifs in peaks, testing for known TF enrichment, scanning for motif instances, comparing motif content between conditions, or interpreting motifs from deep learning models.
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。
Segments the genome into chromatin states from combinatorial histone modification and chromatin factor ChIP-seq data. Uses ChromHMM (multivariate HMM on binarized signal, v1.27), Segway (Dynamic Bayesian Network on continuous signal), EpiSegMix (flexible-distribution HMM with duration modeling, 2024), EpiLogos (multi-biosample visualization), IDEAS (cell-type-aware joint), and full-stack ChromHMM (Vu Ernst 2022) for cross-cell-type segmentations. Handles state-count selection (15 vs 18 vs 25 states), binarization choice, OverlapEnrichment / NeighborhoodEnrichment downstream analysis, and cross-biosample integration. Use when learning chromatin states from a histone mark panel, characterizing learned states by genomic feature enrichment, or comparing chromatin landscapes across cell types.
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。