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概要と使いどころ
Create clear architecture views of Azure resources and their relationships. Use this skill when users ask for diagrams, topology summaries, dependency maps, or deployment overviews. Generates Mermaid, ASCII, and structured documentation for architecture review and compliance.
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Map UI elements and code relationships (affordances).
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。
JIRA comments, attachments, watchers, notify via jira-as. Internal-vs-public comment safety enforced. NOT issue CRUD (mk:jira-issue) or issue links (mk:jira-relationships).
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。
JIRA issue CRUD via jira-as. NOT lifecycle (mk:jira-lifecycle), comments (mk:jira-collaborate), bulk (mk:jira-bulk), links (mk:jira-relationships), or time/sprint (mk:jira-time/agile).
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。
Manage JIRA collaboration surface via the jira-as wrapper: comments, attachments, watchers, notify. Triggers: 'comment on KEY', 'add attachment to KEY', 'watch KEY', 'notify KEY watchers'. Internal-vs-public comment safety enforced. NOT for issue CRUD (mk:jira-issue); NOT for issue links (mk:jira-relationships).
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。
JIRA issue CRUD via the jira-as wrapper. Triggers: 'create bug/task/story', 'show me PROJ-123', 'get/view/look up issue', 'update PROJ-123', 'delete PROJ-123'. NOT for transitions (mk:jira-lifecycle); NOT for comments/attachments (mk:jira-collaborate); NOT for bulk ops (mk:jira-bulk); NOT for issue links (mk:jira-relationships); NOT for time logging (mk:jira-time); NOT for sprint/epic (mk:jira-agile).
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 a grounded Human Design BodyGraph and whole-chart reading from exact birth data. Use for Human Design charts, BodyGraph, decisions, profile, channels, talents, work, wealth, relationships, mission, or chart-based follow-up questions. Supports Chinese and English.
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。
Repository loader for the grounded Human Design BodyGraph and whole-chart reading skill. Use for chart calculation, channels, talents, work, relationships, mission, and follow-up consultation.
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。
Persistent, adaptive vector memory for agents on a ruOS desktop — a Rust-native vector database with local semantic embeddings, HNSW retrieval, and graph relationships. Use to remember and recall across sessions with no database server or API key.
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。
Find all locations where functions are called in a binary. Use when analyzing callers of a function, checking call relationships, or identifying which functions invoke a specific API.
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。
Manage feature manifests for code traceability. Use when creating new features, updating existing features, checking feature health, or exploring the feature-to-code relationship. Activates for manifest validation, feature creation, changelog updates, and traceability queries.
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Master of text-based visual communication using ASCII art, Unicode box-drawing, and structured diagram notation. Creates clear, maintainable diagrams for systems, processes, hierarchies, relationships, and psychological structures. Proactively generates diagrams to enhance understanding. Activate on visualization needs, system architecture, process flows, psychological mapping, or when complex concepts would benefit from visual representation. NOT for photo editing, vector graphics, or GUI-based design tools.
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。
Temporal interval algebra for reasoning about time relationships in planning and knowledge representation
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。
Compare gene co-expression and regulatory networks between biological conditions to find rewired relationships using DiffCorr, DiffCoEx, DINGO/iDINGO, and CoDiNA. Covers the differential-connectivity-is-not-differential-expression distinction, the pairwise multiple-testing explosion, marginal vs partial (direct) rewiring, and the underpowered-rewiring failure mode. Use when comparing co-expression networks between disease vs control, treatment, or developmental stages, or finding hub genes that rewire without changing mean expression. For single-condition modules see coexpression-networks; for differential expression of means see differential-expression/de-results.
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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。
Find cross-database references between NCBI databases using Biopython Bio.Entrez (ELink). Use when navigating gene to protein/structure, sequence to publication, PubMed to GEO, BioProject to SRA runs, or discovering all link relationships for a record. Covers linkname semantics, cmd= variants, asymmetric link warnings, neighbor_history for >200 input IDs, and per-database link tables.
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Designs and analyzes in vivo CRISPR screens in animal tumor models, organoids, and immune-cell adoptive transfers. Covers bottleneck math (250x cells/sgRNA requires ~25M cells implanted; impossible for most syngeneic models, forcing focused libraries), focused library design (Manguso 2017 Nature 547:413 immune screen; Chen 2015 tumor screens), CRISPR-StAR intrinsic-control screening (Uijttewaal 2025 Nat Biotechnol 43:1848), clonal-dynamics-limited detection, tumor-explant DNA recovery, syngeneic vs xenograft vs PDX considerations, and the relationship to downstream MAGeCK / drugZ analysis. Use when designing in vivo CRISPR screens for tumor / immune / metastasis biology, choosing focused vs genome-wide for animal models, addressing bottleneck-induced clonal collapse, picking the syngeneic / xenograft / PDX model, integrating in vivo with in vitro results, or applying CRISPR-StAR for animal experiments.
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Batch effect correction for CRISPR screens covering ComBat empirical-Bayes, RUV, SVA, control-sgRNA normalization, and the model-based alternative of including batch as a covariate in MAGeCK MLE or Chronos. Covers screen-specific batch sources (passage cohort, library lot, infection day, sequencing run, Cas9 lot, FBS lot), PCA + variance-decomposition diagnostic to decide if correction is needed, when correction harms biology by over-correcting condition into batch, limma removeBatchEffect for visualization-only correction, and relationship to multi-condition design matrices. Use when combining screens for joint analysis, when passage cohort confounds biology, when DepMap-style panels need Chronos with batch covariates, when picking ComBat vs RUV, or when correction harms biology and should be replaced with explicit covariate modeling.
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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.
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Decompose total effects into direct and indirect paths through mediators using mediation, CMAverse 4-way, HIMA/HIMA2 high-dimensional, BAMA, two-step / MVMR mediation, or double-ML medDML. Use when testing whether a molecular phenotype (expression, methylation, protein) mediates a treatment-outcome relationship, decomposing exposure-mediator interaction via VanderWeele 4-way, screening high-dimensional EWAS mediators, or running MR-based mediation when sequential ignorability is implausible.
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Query BindingDB for measured drug-target binding affinities (Ki, Kd, IC50, EC50). Search by target (UniProt ID), compound (SMILES/name), or pathogen. Essential for drug discovery, lead optimization, polypharmacology analysis, and structure-activity relationship (SAR) studies.
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Infer gene regulatory networks (GRNs) from gene expression data using scalable algorithms (GRNBoost2, GENIE3). Use when analyzing transcriptomics data (bulk RNA-seq, single-cell RNA-seq) to identify transcription factor-target gene relationships and regulatory interactions. Supports distributed computation for large-scale datasets.
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。
Automate Attio CRM operations -- search records, query contacts and companies with advanced filters, manage notes, list attributes, and navigate your relationship data -- using natural language through the Composio MCP integration.
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-level medicinal chemistry knowledge. Use when working with drug design, structure-activity relationships, ADMET properties, pharmacophores, lead optimization, target identification, drug metabolism, or pharmaceutical development. Also use when the user mentions 'SAR', 'drug design', 'pharmacophore', 'ADMET', 'lead compound', 'hit to lead', 'drug metabolism', 'bioavailability', 'selectivity', 'binding affinity', 'scaffold hopping', or 'bioisostere'.
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。