548 件 ・ 関連度順
概要と使いどころ
Sanguo knowledge growth loop. Use for: 三國知識增長、武將關鍵字、武將內容、事件型態擴充、deterministic extractor 擴充、批次 enrichment、relationshipEdges、人際關係、情感故事、專長、職業權重、活動任務、士農工商、當官、當賊、當兵、貧民、乞討、黃巾賊、暴民、裝備事件、外交、內政治理、決策權重、生活化素材、每輪覆蓋率成長報告。
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。
Maintain entity references, ownership links, target relationships, deletion cleanup, and network-safe relation state.
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, update, and query a persistent project knowledge graph from skills, memory, docs, and code structure — stdlib Python only, no external tools. Dual-mode: skill-library (agent-loom) or application (any consumer repo). Load when the user asks for a knowledge graph, project map, skill relationships, query the graph, update the graph, or trace how components connect. Auto-runs on memory-handoff and project-setup bootstrap. Also triggers on "build the graph", "what connects to X", "map this project".
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 C4 Component-level documentation specialist. Synthesizes C4 Code-level documentation into Component-level architecture, defining component boundaries, interfaces, and 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。
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。
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.
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 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.
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 Cell Reports 36:109597), 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。
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.
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。
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.
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。
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.
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 when the user asks about carrier-driver relationships — Independent Contractor (IC / 1099) vs Employee (W-2), Owner-Operator lease agreements, the AB5 California law, the DOL 2024 IC test, lease-purchase programs from the carrier side, settlement statements, deductions, and how to structure a compliant IC arrangement. Cite 49 CFR 376 + 1099 IRS rules.
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。
Comprehensive overview of ALL Coval platform resources, their hierarchy, relationships, API endpoints, and ID formats. Use when user asks about Coval resources, data model, how things relate, what endpoints exist, or needs context about the platform structure before making API calls.
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。
Early-user discovery and feedback trials. Use when reviewing unfamiliar AI Matrx accounts, classifying owner relationships, recommending trial plans, or running approved outreach and reply checks.
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。
キャリアポートフォリオ管理、運賃交渉、キャリアパフォーマンス追跡、 貨物配分、戦略的キャリア関係の維持に関する体系化された専門知識。 15年以上の経験を持つ輸送マネージャーの知見に基づく。 スコアカードフレームワーク、RFPプロセス、市場インテリジェンス、 コンプライアンス審査を含む。キャリア管理、運賃交渉、キャリア パフォーマンス評価、貨物戦略の構築時に使用。
Explain an existing Convex app — data model + relationships, public vs internal functions, auth/ownership model, components, a request→data flow — read from the schema and function surface. Read-only.
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 generate Convex database schemas with proper validation, indexes, and relationships. Use when creating schema.ts or modifying table definitions.
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 knowledge graph for autonomous coding. Use when storing relationships, querying connected knowledge, building project understanding, or maintaining semantic memory.
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。
Deep knowledge expert for the 33GOD agentic pipeline system, understands component relationships and suggests feature implementations based on actual codebase state
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。
OneKey monorepo architecture and code organization. Use when understanding project structure, package relationships, import rules, or component organization. Triggers on architecture, structure, packages, imports, hierarchy, dependencies, monorepo, organization.
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。
JPA/Hibernate patterns for entity design, relationships, query optimization, transactions, auditing, indexing, pagination, and pooling in Spring Boot.
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。
Explains how OpenClaw, OpenShell, and NemoClaw form the ecosystem, NemoClaw's position in the stack, what NemoClaw adds beyond the community sandbox, and when to prefer NemoClaw versus integrating OpenShell and OpenClaw directly. Use when users ask about the relationship between OpenClaw, OpenShell, and NemoClaw, or when to use NemoClaw versus OpenShell. Trigger keywords - nemoclaw ecosystem, openclaw openshell, nemoclaw vs openshell, sandboxed openclaw, how nemoclaw works, nemoclaw sandbox lifecycle blueprint, nemoclaw overview, openclaw always-on assistants, nvidia openshell, nvidia nemotron, nemoclaw release notes, nemoclaw changelog.
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 technical diagrams using Mermaid syntax for architecture, sequences, ERDs, flowcharts, and state machines. Use for visualizing system design, data flows, and processes. Triggers: diagram, mermaid, architecture diagram, sequence diagram, flowchart, ERD, entity relationship, state diagram, C4 model, component diagram, visualize, draw.
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。