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Build Sankey, alluvial, river, and CONSORT-style flow diagrams to visualize cohort transitions, cell-state changes, or pipeline filtering using ggalluvial, networkD3, plotly, and consort. Use when showing how entities move between categories across timepoints (cell states, drug response classes, patient flow through a trial) or filtering pipelines (variants filtered through QC stages).

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Dereplicate a putative natural product and assign its chemical taxonomy. Use to answer "is [compound] a known natural product", "what microbe/organism produces [compound]", "what chemical class is [compound]", "dereplicate this metabolite (by formula/exact mass/InChIKey/SMILES)", or "classify this molecule into ChemOnt". Searches NPAtlas for known microbial natural products (producing organism + literature reference), assigns the ChemOnt kingdom→superclass→class→subclass hierarchy via ClassyFire, resolves systematic IUPAC names to structure via OPSIN, and cross-references identity in PubChem. NOT for general drug/compound identity or ADMET (use tooluniverse-chemical-compound-retrieval / tooluniverse-small-molecule-discovery) and NOT for metabolomics pathway/enrichment analysis (use tooluniverse-metabolomics skills).

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Runs standard-format scenarios through ScenarioRunner — ASAM OpenSCENARIO 1.x (.xosc, via --openscenario, with catalogs and ParameterDeclaration overrides) and OpenSCENARIO 2.0 (.osc, via --openscenario2, the ANTLR/osc2 pipeline). Validates a file against the schema before running, lists the bundled examples, and reports which OSC features ScenarioRunner actually implements. Use when the user asks to "run an xosc", "run an OpenSCENARIO file", "use the OpenSCENARIO standard", "run a .osc / OSC2 scenario", or hands over a scenario file from another tool.

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Use when setting up a secure email inbox for any AI agent — configuring inbound email via Resend, webhooks, tunneling for local development, and implementing security measures to prevent prompt injection attacks. Also use when someone mentions 'agent email', 'bot inbox', 'receive emails for agent', 'agent webhook', 'email security for AI', 'prompt injection via email', 'inbound email for bot', or wants their AI agent to receive and respond to emails securely.

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Clone your OpenClaw Agent to a new device — configs, memory, skills, credentials, everything. Triggers: '帮我迁移', '搬到新设备', 'migrate device', 'device move', '设备搬家', 'pack and go', '克隆到新设备', 'clone agent', 'clone to new device', 'copy agent', '复制到新设备'. Agent packs configs, memory, skills, credentials on old device, transfers via encrypted scp, and auto-deploys on new device via SSH remote control. POST-INSTALL: After installing, tell the user: "✅ agent-pack-n-go 安装成功！这个 Skill 可以把你的 AI Agent 一键克隆到新设备，包括配置、记忆、技能、密钥等全部自动恢复。使用方法：对我说「帮我克隆到新设备」即可开始。"

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When the user wants to optimize aerospace and defense supply chains, manage long lead-time components, handle MRO operations, or ensure AS9100 compliance. Also use when the user mentions "aerospace supply chain," "aviation manufacturing," "MRO supply chain," "AS9100," "aircraft parts," "defense procurement," "AOG support," "airworthiness," "ITAR compliance," "aerospace certification," or "aviation aftermarket." For general manufacturing, see production-scheduling. For quality, see quality-management.

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 working with access policies, field access, authorization middleware, gates, route access control, AccessResult semantics, permission handlers, or files in packages/access/, packages/routing/src/AccessChecker.php, packages/user/src/Middleware/. Covers entity-level access (deny-by-default via isAllowed), field-level access (open-by-default via !isForbidden), the Gate system, route options (_public, _permission, _role, _gate), and the SessionMiddleware/AuthorizationMiddleware pipeline in public/index.php.

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Facilitate DDD domain modeling sessions via EventStorming conversation and DML (Domain Modeling Language). Use whenever the user wants to model a business domain by discovering domain events, commands, aggregates, policies, read models, and bounded contexts through dialogue. Produces incrementally-built DML as the primary artifact, plus a Markdown session report. Also invoke for refining existing DML, mapping out a new feature's domain model, or when the user says "ドメインモデリングしたい", "イベントストーミング", "DDDで整理したい", "DMLを育てたい".

調査（Explore）・設計（Plan）・実装（general-purpose）・レビュー（review-* スキル群）のサブエージェントをオーケストレーションして開発タスクを遂行するスキル。依頼文からゴール（調査のみ / 設計まで / 実装まで / レビュー）を判定し、必要なフェーズだけ実行する。親は自分でコードを読み書きせず、分解・dispatch・検収・機械検証に徹する。Use when starting any non-trivial development task（複数ファイル・複数層にまたがる調査/設計/実装/レビュー）, when user says 'オーケストレート', 'orchestrate', '調べて', '設計して', '実装して', '調べて直して', or 'レビューして'.

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。

Detects focal chromatin loops (point interactions / corner-dots) in balanced Hi-C and Micro-C contact maps and aggregates/validates a loop set. Covers de-novo calling with cooltools dots (HiCCUPS-style 4-background local enrichment with lambda-chunked FDR), chromosight (template-correlation), and Mustache (scale-space blob detection); aggregate peak analysis (APA) via cooltools pileup for confirmation; the depth/resolution prerequisite (de-novo needs ~5-10kb resolution = hundreds of millions to billions of valid pairs); consensus across callers and convergent-CTCF support as validation; and differential loops via union anchors plus chromosight quantify. Use when calling chromatin loops or dots from a cooler, deciding whether a map is deep enough to call de-novo vs running APA on known CTCF/cohesin anchors, building an aggregate peak pileup, comparing loops across conditions, or validating loop calls. For HiChIP/PLAC-seq/PCHi-C protein-anchored data use FitHiChIP/MAPS, not dots.

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 TAD boundaries from balanced Hi-C contact matrices via the diamond-window insulation score (cooltools insulation) and HiCExplorer hicFindTADs, returning a continuous log2 insulation track, valley-prominence boundary_strength, and Li/Otsu-thresholded is_boundary flags across a list of window sizes. Covers the multi-scale window sweep (sub-TAD to compartment-domain), why the boundary is reproducible but the domain partition is not, cross-condition comparison via differential SCORE not differential partition, and the insulation-vs-compartment orthogonality. Use when calling TADs or domain boundaries, computing insulation scores, choosing a window size, ranking boundary strength, comparing boundaries across conditions, or annotating CTCF-backed boundaries; route domain rendering to hic-visualization and boundary-feature overlap to genome-intervals.

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Controls error rates across thousands of simultaneous tests in genomics discovery using false-discovery-rate methods (Benjamini-Hochberg 1995; Benjamini-Yekutieli 2001 for arbitrary dependence; Storey q-value with pi0 estimation; local FDR; independent filtering Bourgon 2010; covariate-weighted FDR via IHW Ignatiadis 2016), plus family-wise error control (Bonferroni, Holm) and the GWAS genome-wide threshold. Covers the FDR-versus-FWER choice as the discovery-versus-confirmatory distinction, the dependence assumptions behind BH (PRDS) versus BY, pi0 estimation, the independent-filtering and false-coverage-rate traps, and reproducibility ranking via IDR (Li 2011). Use when correcting p-values from genome-wide tests, choosing between BH/BY/q-value/Bonferroni, setting an FDR threshold, applying IHW or independent filtering, or interpreting q-values. For confirmatory trials with few pre-specified endpoints (closed testing, graphical/gatekeeping), see clinical-biostatistics/multiplicity-graphical.

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Performs differential expression on bulk RNA-seq count data with edgeR's negative-binomial GLM and quasi-likelihood F-test framework. Covers DGEList construction, filterByExpr, TMM/TMMwsp normalization, robust dispersion estimation, glmQLFit/glmQLFTest, TREAT for magnitude-bounded hypotheses, contrasts via no-intercept designs, voom and voomWithQualityWeights for heterogeneous samples, and the edgeR v4 bias-corrected APL changes. Use when running bulk DE with edgeR, choosing edgeR over DESeq2 (small n, transcript DE via catchSalmon, large samples), needing TREAT for a fold-change-threshold hypothesis, troubleshooting v3-to-v4 reproducibility, building paired or interaction designs, or handling library-quality heterogeneity.

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。

Handles batch effects in bulk RNA-seq via design-matrix inclusion (the correct path for DE), ComBat/ComBat-seq for visualization, SVA for unknown latent factors, RUVSeq for negative-control-gene-anchored unwanted variation, and limma::removeBatchEffect for plotting only. Encodes the Nygaard 2016 cardinal sin against testing on a batch-corrected matrix, the choice between SVA/RUVg/RUVs/RUVr, the confounding non-identifiability problem, the single-cell boundary (Harmony/MNN are NOT for bulk), and the Goh 2017 harmonization critique. Use when designing a DE analysis with batch structure, troubleshooting batch-dominated PCA, choosing ComBat vs ComBat-seq, handling unknown batch via SVA, integrating across studies, or deciding when (rarely) to subtract batch.

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。

Build Sankey, alluvial, river, and CONSORT-style flow diagrams to visualize cohort transitions, cell-state changes, or pipeline filtering using ggalluvial, networkD3, plotly, and consort. Use when showing how entities move between categories across timepoints (cell states, drug response classes, patient flow through a trial) or filtering pipelines (variants filtered through QC stages).

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。

Infer orthologous genes and gene families across species using OrthoFinder3 (HOG-based phylogenetic orthology), SonicParanoid2, Broccoli, ProteinOrtho, OMA / FastOMA hierarchical orthologous groups, eggNOG-mapper, JustOrthologs, and TOGA whole-genome-alignment orthology. Use when building single-copy ortholog sets for phylogenomics, classifying co-orthologs and in/out-paralogs after gene duplication, propagating functional annotation via orthology with awareness of the ortholog conjecture, distinguishing speciation from duplication via gene-tree species-tree reconciliation, computing Quest-for-Orthologs benchmark performance, or running synteny-aware ortholog detection in WGD-affected lineages.

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Estimate causal effects of an exposure on an outcome from GWAS summary statistics using genetic instruments. Implements IVW (fixed/random), MR-Egger, weighted median/mode, MR-RAPS, CAUSE, GSMR-HEIDI, MR-PRESSO, MVMR, MR-Clust, LCV, and LHC-MR via TwoSampleMR, MendelianRandomization, MR-PRESSO, cause, and lhcMR. Use when testing causal direction between traits, evaluating drug-target effects via cis-pQTL/cis-eQTL, performing multivariable mediation MR, distinguishing causation from correlated horizontal pleiotropy, or producing STROBE-MR-compliant sensitivity batteries.

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Goal-integrity skill. Use for backend/API/persistence, preserve/do-not-change, tests/validation, mocks, rework, multi-part requests. Emits Goal Contracts, Deviation Notices, Phase Checks, Final Audits. Skip for Q&A or trivial edits.

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Start the Openrind Desktop dev stack via Docker and verify real user flows via Chrome MCP. Triggers when user mentions: - "dev-up.sh" - "docker dev stack" - "verify in chrome mcp" - "test the real flow"

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Gera imagem por IA via Ideogram, Leonardo ou Flux usando os scripts do plugin, ou entrega o prompt otimizado quando não há chave. Use quando o usuário disser "gerar imagem", "criar ilustração", "imagem por IA", "Ideogram", "Leonardo", "Flux", "preciso de uma foto que não temos", ou quando creative-concept marcar um ativo como "gerar". Skill central deste plugin. Toda chamada gasta crédito e exige confirmação prévia.

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。

Cross-model benchmark for gstack skills. Runs the same prompt through Claude, GPT (via Codex CLI), and Gemini side-by-side — compares latency, tokens, cost, and optionally quality via LLM judge. Answers "which model is actually best for this skill?" with data instead of vibes. Separate from /benchmark, which measures web page performance. Use when: "benchmark models", "compare models", "which model is best for X", "cross-model comparison", "model shootout". (gstack) Voice triggers (speech-to-text aliases): "compare models", "model shootout", "which model is best".

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。