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概要と使いどころ
Surface and visualize hidden dependencies blocking delivery. Use when the user says "map our dependencies", "what's blocking what", "dependency audit", "why are teams blocked", "we have a bottleneck", "draw out the dependencies", or asks to identify cross-team blockers - even if they don't explicitly say "dependency mapping". Based on "Making Work Visible" by Dominica DeGrandis (time theft, invisible work, flow blockers).
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Use this skill to configure a Lightning Service Console app in Salesforce — Console Navigation (split view), workspace tabs and subtabs, the utility bar with Omni-Channel, Macros and History, Quick Text, keyboard shortcuts, and navigation rules per object. Trigger keywords: Service Console, console app, workspace tabs, subtabs, utility bar macros, Omni-Channel utility, split view, Quick Text, console navigation rules, keyboard shortcuts service console, navType Console, workspaceConfig mappings, WorkspaceMapping fieldName, UtilityBar FlexiPage, tabLimitConfig, listPlacement, MacroInstruction, QuickText Channel multipicklist, isNavTabPersistenceDisabled. NOT for opening or refreshing console tabs from code — use lwc/lwc-console-workspace-api. NOT for Omni-Channel routing setup — use admin/omni-channel-routing-setup.
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 gathering, structuring, and documenting requirements for a Financial Services Cloud (FSC) wealth management implementation — including financial planning workflow discovery, portfolio review process mapping, client lifecycle requirements, advisor tooling needs, and FSC architecture determination (managed package vs. FSC Core). Trigger keywords: wealth management requirements, FSC requirements gathering, financial planning workflow, portfolio review process, advisor tools setup, FSC data model scoping, wealth management process mapping. NOT for implementation, configuration, or code — use financial-account-setup, fsc-action-plans, or apex/fsc-financial-calculations for those. NOT for FSC architecture decisions — use architect/wealth-management-architecture. NOT for Health Cloud or NPSP requirements.
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 Salesforce-anchored personas and journey maps where every persona ties to a Permission Set Group, primary record types, list views, dashboards, automation, and a measured mobile/desktop posture, and journeys walk each task end-to-end with friction tags that drive UX decisions. NOT for stakeholder authority / RACI (use admin/stakeholder-raci-for-sf-projects), NOT for system-side process flows (use admin/process-flow-as-is-to-be), NOT for Lightning page design itself (use admin/lightning-app-builder-advanced or the lightning-record-page-auditor agent), NOT for pure design / UX research disconnected from org 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。
Use when picklist data quality has degraded: unrestricted picklists accepting garbage API values, dependent picklist relationships bypassed by integrations, record type picklist value mappings drifting out of sync, or orphaned values appearing in reports. NOT for the value-deactivation runbook or the API-name-vs-label trap — use admin/picklist-data-integrity. NOT for initial picklist creation or Global Value Set design — use admin/picklist-and-value-sets. NOT for record type configuration itself — use admin/record-types-and-page-layouts.
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 designing lead nurture journeys in MCAE (Account Engagement / Pardot) Engagement Studio — mapping content to funnel stages (Awareness, Consideration, Decision), behavioral trigger rules, branching program paths, and MQL handoff criteria to Sales. NOT for building a journey in Marketing Cloud Journey Builder — use admin/journey-builder-administration. NOT for the scoring and grading model behind MQL — use admin/mcae-lead-scoring-and-grading.
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 deploying a trained Einstein Discovery model to production records declaratively — activating a prediction definition, mapping output fields to page layouts, adding the Einstein Discovery Action to a Flow, running bulk predict jobs, and monitoring model health via Model Manager UI. Trigger keywords: prediction definition activation, bulk predict job, Einstein Discovery Flow action, Model Manager, prediction field mapping, model refresh activation, scoring job, Einstein Discovery recommendations on record. NOT for authoring the story in CRM Analytics Studio — use admin/einstein-discovery-setup. NOT for programmatic scoring through the API — use agentforce/einstein-discovery-development.
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 configuring or troubleshooting FSC Referral Management: setting up referral types via ReferralRecordTypeMapping custom metadata, routing referrals through Lead Assignment Rules keyed on the Expressed Interest picklist, tracking Referrer Score, and partner referral visibility in Experience Cloud. Trigger keywords: referral routing, FSC referral types, ReferralRecordTypeMapping, Expressed Interest picklist, partner referral Contact, Referrer Score Experience Cloud. NOT for clinical referrals in Health Cloud — use admin/referral-management-health. NOT for standard Lead capture and conversion outside FSC — use admin/lead-management-and-conversion.
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, filter, refresh, and activate audience segments in Salesforce Data Cloud: segment types (Standard, Real-Time, Waterfall, Dynamic, Data Kit), segment refresh schedules, activation field mapping, audience publishing. NOT for the SQL metrics a segment filters on - use admin/data-cloud-calculated-insights. NOT for ingestion, DLO to DMO mapping and identity resolution - use data/data-cloud-data-streams.
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。
Test-to-requirement coverage mapping. Reads plan acceptance criteria and test files, produces a coverage gap report showing which requirements have no tests. Use during Phase 2 (Test) or Phase 4 (Review) to verify test completeness. Named after the Nyquist sampling theorem — sufficient test coverage prevents aliased (missed) requirements. NOT for running tests (see mk:testing); NOT for structural code review (see mk:review).
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。
Maps between gene identifier systems (Ensembl, Entrez, HGNC symbol, UniProt, RefSeq, MANE) using AnnotationDbi, biomaRt, mygene, pyensembl, and Ensembl REST. Encodes Ensembl version stripping with GENCODE _PAR_Y preservation, the Ziemann 2016 Excel autocorrect debacle and Bruford 2020 HGNC renames (SEPT*->SEPTIN*, MARCH*->MARCHF*, MARC*->MTARC*, DEC1->DELEC1), OCT4/POU5F1 alias resolution, biomaRt archive endpoints for release pinning, the `filters` (plural) gotcha, MANE Select for clinical reporting, cross-species orthology via Ensembl Compara / OMA / OrthoDB, and tx2gene construction for tximport. Use when converting gene IDs across systems, handling renamed symbols, building tx2gene, pinning to a specific Ensembl release for reproducibility, or mapping cross-species orthologs.
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。
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。
Performs gene-level association from GWAS summary statistics via genetically predicted tissue expression using FUSION, PrediXcan, S-PrediXcan, S-MultiXcan, UTMOST, MOSTWAS, kTWAS, EpiXcan, TIGAR-V2, and probabilistic fine-mapping with FOCUS and MA-FOCUS. Use when running TWAS from GWAS sumstats, prioritising candidate causal genes from a GWAS lead locus, picking single-tissue vs cross-tissue models, identifying LD-induced TWAS false positives, choosing ancestry-matched prediction weights, fine-mapping co-regulated TWAS hits, or triangulating TWAS with cis-eQTL Mendelian randomization and colocalization to nominate a causal gene.
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。
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。
Process, analyze, and visualize geospatial data at scale. Handles drone imagery, GPS tracks, GeoJSON optimization, coordinate transformations, and tile generation. Use for mapping apps, drone data processing, location-based services. Activate on "geospatial", "GIS", "PostGIS", "GeoJSON", "map tiles", "coordinate systems". NOT for simple address validation, basic distance calculations, or static map embeds.
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 AI agents through Senzing entity resolution workflows using the Senzing MCP server. Covers data mapping to Senzing format, SDK code generation (Python, Java, C#, Rust, TypeScript/Node.js), documentation search, error troubleshooting, sample data access, reporting and visualization, SDK setup guides, and V3-to-V4 migration. Use when working with entity resolution, record matching, record linkage, deduplication, Senzing SDK integration, data mapping for Senzing ingestion, or troubleshooting Senzing error codes. Also use when the user mentions matching records across data sources, finding duplicate entities, identity resolution, master data management, or needs to resolve who is who across 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。
Expert-level complex analysis knowledge. Use when working with complex functions, holomorphic functions, Cauchy's theorem, contour integration, Laurent series, residues, conformal mappings, or analytic continuation. Also use when the user mentions 'holomorphic', 'analytic', 'Cauchy theorem', 'residue', 'contour integral', 'Laurent series', 'conformal map', 'Riemann mapping', 'Mobius transformation', 'harmonic function', 'entire function', or 'meromorphic'.
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。
SOP for the automated refinement loop (/refine) workflow. Trigger when: - Exhaustively auditing and optimizing pre-existing codebase artifacts. - Applying iterative contraction mapping to reduce code entropy. - Prompt contains: /refine, refinement loop, contraction mapping, fixed-point sweep.
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 advanced persistent threat (APT) group tactics, techniques, and procedures (TTPs) to the MITRE ATT&CK framework using the ATT&CK Navigator and attackcti Python library. The analyst queries STIX/TAXII data for group-technique associations, generates Navigator layer files for visualization, and compares defensive coverage against adversary profiles. Activates for requests involving APT TTP mapping, ATT&CK Navigator layers, threat actor profiling, or MITRE technique coverage 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。
Analyzes intrusion activity against the Lockheed Martin Cyber Kill Chain framework to identify which phases an adversary has completed, where defenses succeeded or failed, and what controls would have interrupted the attack at earlier phases. Use when conducting post-incident analysis, building prevention-focused security controls, or mapping detection gaps to kill chain phases. Activates for requests involving kill chain analysis, intrusion kill chain, attack phase mapping, or Lockheed Martin kill chain 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。
再発しやすい危険な実装パターンを、安全側に倒れる標準パターン・禁止事項・共通層・静的ルール候補へ 変換するスキル。ORM強制、deny-by-default認可、サービス経由I/O、永続化確認後の成功表示などを設計する。 レビューで危険コードを見つけるのではなく、危険コードを書きにくくする設計標準を作ることが目的。 Use when turning repeated defect patterns into safe architectural defaults, forbidden-to-safe mapping tables, common layer designs, and enforceable static analysis rules.
Interactively build a Narrative identity graph workflow from one or more first-party datasets and (optionally) third-party data sources. Confirms each input dataset is mapped to the Rosetta Stone graph edge attribute (mapping it via /generate-rosetta-stone-mappings if not), then composes and submits a workflow that unions every edge source and labels connected components. Use when: "build an identity graph", "generate an identity graph", "create an identity graph", "stitch these datasets into a graph", "make a graph workflow", "label connected components on these datasets", "I want a person graph / household graph / device graph". (narrative-identity)
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。