1,175 件 ・ 関連度順
概要と使いどころ
Tags harness-decomposed curriculum items (KUDs, LTs, criteria) with a school's developmental band metadata while preserving source voice and labels. Supply the band schema; the skill derives mapping rules from it.
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 learning for single-cell analysis using scvi-tools. This skill should be used when users need (1) data integration and batch correction with scVI/scANVI, (2) ATAC-seq analysis with PeakVI, (3) CITE-seq multi-modal analysis with totalVI, (4) multiome RNA+ATAC analysis with MultiVI, (5) spatial transcriptomics deconvolution with DestVI, (6) label transfer and reference mapping with scANVI/scArches, (7) RNA velocity with veloVI, or (8) any deep learning-based single-cell method. Triggers include mentions of scVI, scANVI, totalVI, PeakVI, MultiVI, DestVI, veloVI, sysVI, scArches, variational autoencoder, VAE, batch correction, data integration, multi-modal, CITE-seq, multiome, reference mapping, latent space.
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。
Opportunity Solution Trees, opportunity scoring, Lean Canvas, JTBD job mapping, and technique selection guide
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。
Maintains sparse, reviewable cybersecurity framework edges with versioned IDs, relation, rationale, source, and review status. Use when mapping exercise observations or procedures to NIST, ATT&CK, or another named framework.
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 ManagedCode.MimeTypes when a .NET application needs consistent MIME type detection, extension mapping, and content-type decisions for uploads, downloads, or HTTP responses. USE FOR: integrating ManagedCode.MimeTypes into upload or download flows; mapping file extensions to content types in APIs or background processing; reviewing content-type. DO NOT USE FOR: unrelated stacks; generic tasks that do not need this specific guidance. INVOKES: inspect the repository context, edit targeted files, and run relevant build, test, lint, or validation commands when changes are made.
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 you need framework-agnostic WireMock guidance — stub design, JSON or programmatic mappings, precise request matching, response bodies and faults, classpath fixtures, isolation and reset between tests, verification of calls, dynamic ports and base URLs, and avoiding flaky stubs — without choosing Spring Boot, Quarkus, or Micronaut. This should trigger for requests such as Design or review WireMock stubs (JSON mappings or Java DSL); Improve request matching, isolation, or reset strategy for HTTP mocks; Add or fix verification of outbound HTTP calls to a WireMock server; Debug flaky tests involving WireMock or unmatched request journals. Part of Plinth Toolkit
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 a problem under exploration needs its surrounding system context identified before design begins — Existing systems, Integrations, Ownership, and External dependencies. This should trigger when an issue's Context Mapping point of view needs evaluation, or when a maintainer directly asks to map the systems, integrations, owners, and external dependencies relevant to a problem. Part of Plinth Toolkit
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。
Engineer and audit SIEM detection rules — log source coverage, Sigma / KQL / SPL / Elastic query authoring, MITRE ATT&CK mapping, false-positive tuning, and detection-as-code workflows. Use when the user mentions 'SIEM,' 'detection engineering,' 'detection rules,' 'Sigma,' 'KQL,' 'SPL,' 'Splunk,' 'Sentinel,' 'Elastic,' 'Wazuh,' 'Chronicle,' 'detection-as-code,' 'MITRE ATT&CK mapping,' 'log coverage,' 'alert tuning,' 'use case development,' or needs help building or improving security detections.
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。
Map your security posture against the NIST Cybersecurity Framework 2.0 (Govern, Identify, Protect, Detect, Respond, Recover). Produce a gap analysis, current/target tier assessment, and roadmap in the governance language that boards, auditors, and CISOs actually use. Use when the user mentions 'NIST CSF,' 'CSF 2.0,' 'cybersecurity framework,' 'security posture,' 'governance mapping,' 'CSF gap analysis,' 'CSF tiers,' 'cybersecurity maturity,' 'security roadmap,' 'CISO report,' 'board reporting,' 'security program,' or needs to translate technical findings into governance language.
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 mapping a target's external attack surface or gathering OSINT — subdomain enumeration, attack-surface mapping (httpx/katana/JS secrets), subdomain takeover, multi-cloud/Azure tenant recon, GitHub secret dorking, breach/infostealer credential intel, CVE prioritization (EPSS/KEV)
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。
Guides building a multi-jurisdiction privacy compliance matrix for organisations operating across multiple countries. Covers common requirements identification, jurisdiction-specific deltas, gap analysis, and harmonised control frameworks. Keywords: multi-jurisdiction, compliance matrix, harmonised controls, gap analysis, jurisdiction mapping.
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。
Creates GDPR Article 30(1) Records of Processing Activities (RoPA) for data controllers with all seven mandatory fields: controller identity and contact details, processing purposes, data subject categories, personal data categories, recipient categories, third country transfers, and retention periods. Includes Python generator for automated RoPA creation. Activate for controller RoPA, Art. 30(1), processing records, data mapping.
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。
Visualize and map user flows with Mermaid diagrams, decision points, error states, and conversion metrics to optimize user journeys. Use when the user requests user flow mapping or provides relevant inputs for this 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。
Resolves GWAS associations to candidate causal variants and credible sets via SuSiE, susie_rss, FINEMAP, CAVIAR, DAP-G, PAINTOR, PolyFun, SuSiEx, MultiSuSiE, and FOCUS. Use when narrowing a GWAS lead SNP to a 95 percent credible set, choosing between in-sample and reference LD, calibrating non-sparse loci with SuSiE-inf or FINEMAP-inf, integrating functional priors via PolyFun, fine-mapping across ancestries with SuSiEx, diagnosing LD mismatch via estimate_s_rss and kriging_rss, handling HLA or long-range LD, or feeding credible sets into coloc.susie for colocalization.
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。
End-to-end post-GWAS causal inference pipeline orchestrating heritability partitioning, genetic correlation, Mendelian randomization with CHP-aware sensitivity (CAUSE / LHC-MR), colocalization, fine-mapping with SuSiE / FOCUS, mediation, TWAS triangulation, cis-pQTL drug-target MR, effector-gene prioritization (L2G / PoPS / cS2G), and GenomicSEM common-factor GWAS. Use when triangulating causal inference across multiple complementary methods, prioritizing tissues via stratified LDSC, nominating or de-risking drug targets, mapping a lead SNP to a candidate effector gene, modeling shared genetic architecture across correlated traits, or producing a STROBE-MR-compliant publication-grade evidence battery from GWAS summary statistics.
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 allele-specific transcription factor or histone modification binding from heterozygous-variant ChIP-seq using WASP (reference-bias filter; mandatory upstream), RASQUAL (joint QTL + bias-corrected testing), BaalChIP (Bayesian beta-binomial with copy-number-aware overdispersion), and AlleleSeq (personalized diploid genome). Handles imprinted-locus awareness, X-inactivation artifacts, cancer copy-number imbalance, and integration with downstream caQTL / bQTL mapping. Use when identifying variants with allelic effects on TF binding, fine-mapping causal regulatory variants, validating deep-learning variant predictions, or characterizing cis-acting regulatory effects.
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。
Detect allele-specific chromatin accessibility from ATAC-seq using WASP, GATK ASEReadCounter, or RASQUAL. Use when mapping cis-regulatory genetic variants from heterozygous SNPs, separating cis from trans regulation, building chromatin QTL (caQTL) maps, validating GWAS variant function with allelic imbalance, or detecting reference allele mapping bias before downstream analysis.
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。
Workflow for read alignment, sorting, indexing, mapping statistics, and downstream-ready alignment artifacts.
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。
Synthesize behavioral personas from prior stage evidence for journey mapping and marketing during PRD v0.4 User Journeys. Triggers on requests to define personas, create user profiles, identify target users, or when user asks "who are our users?", "define personas", "user profiles", "target users", "persona creation", "who uses this product?". Consumes CFD- (v0.1-v0.3), BR- (targeting from v0.3 Moat), FEA- (v0.3 Feature Value Planning). Outputs PER- entries with behavioral profiles and feature relationships. Feeds v0.4 User Journey Mapping.
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。
Map user missions from trigger to value moment, organizing features into coherent paths during PRD v0.4 User Journeys. Triggers on requests to map user journeys, define user flows, describe how users accomplish goals, or when user asks "map user journeys", "define user flows", "user missions", "how do users accomplish X?", "journey mapping", "what steps do users take?", "pain to value flow". Consumes PER- (Persona Definition), FEA- (Feature Value Planning), KPI- (Outcome Definition). Outputs UJ- entries with step flows, pain points, and value moments. Feeds v0.4 Screen Flow Definition.
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 a single sprint or release test plan. Covers feature decomposition into testable scenarios, requirements-to-test coverage mapping, effort estimation by test type, prioritization matrices (risk × effort), resource allocation, and scheduling with buffers. Use when: "sprint test plan," "release test plan," "what to test this sprint," "test estimation," "coverage mapping." Not for: multi-quarter strategy — use `test-strategy`. Not for: ranking areas by risk — use `risk-based-testing`. Not for: the go/no-go decision itself — use `release-readiness`. Related: test-strategy, risk-based-testing, release-readiness.
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 3D modeling specialist with deep knowledge of topology, UV mapping, game-ready and film-ready pipelines, DCC tool workflows (Blender, Maya, ZBrush, 3ds Max, Houdini), retopology, LOD systems, and export pipelines. This skill represents years of production experience distilled into actionable guidance. Use when "3d model, 3d modeling, mesh topology, uv unwrap, uv mapping, retopology, retopo, low poly, high poly, subdivision, subdiv, edge flow, edge loops, polygon modeling, box modeling, hard surface, organic modeling, sculpting, zbrush, blender modeling, maya modeling, 3ds max, LOD, level of detail, game ready mesh, film ready, baking normals, high to low, fbx export, gltf export, texel density, 3d, modeling, topology, uv, game-dev, vfx, blender, maya, zbrush, retopology, lod, hard-surface, organic, sculpting" mentioned.
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。
Configure and use Elasticsearch for full-text search, custom analyzers, aggregations, and index management. Use when a user needs to design search mappings, write complex queries, build aggregation pipelines, tune relevance scoring, or optimize index performance for search workloads.
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。