「map」の検索結果

Analyzes an existing codebase using strategic Domain-Driven Design to discover bounded contexts, surface ubiquitous language and semantic collisions, find places where code boundaries diverge from domain boundaries, and produce an evidence-backed domain and context map. Use when the goal is DDD-specific: bounded context discovery, ubiquitous language analysis, domain model discovery, or mapping contested ownership and responsibility. The entire repository is a valid scope; no module or directory must be named first. Does not assess architectural coupling, cohesion, or technical debt — use architectural-analysis. Does not design service communication, integration patterns, or deployment topology — use architectural-analysis or plan-a-feature. Does not refactor code, diagnose bugs, or review code quality — use refactor, investigate, or code-review.

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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.

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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.

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Owns repositories, data sources and mapping for Compose and Compose Multiplatform apps: DTO to domain to UiModel boundaries, Ktor clients and bearer auth, WebSocket and SSE, Room, DataStore, Paging 3, offline-first and data-layer tests. Use when writing or reviewing a repository, data source, DTO, mapper, HttpClient, bearer token, WebSocket, SSE, Room DAO, migration, DataStore, PagingSource, RemoteMediator, offline cache or MockEngine test. Do NOT use for ViewModel or UI wiring (compose-feature), composables (compose-ui), the MVI contract or error tiers (compose-architecture), Gradle modules (compose-project), or commonMain vs platform splits (compose-platform).

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Use when designing organizational structure (team topologies, Conway's Law alignment), mapping stakeholders by power-interest for change initiatives, defining team interface contracts (APIs, SLAs, decision rights, handoffs), assessing capability maturity (DORA, CMMC, agile maturity models), planning org restructures (functional to product teams, platform teams, shared services), or when user mentions "org design", "team structure", "stakeholder map", "team interfaces", "capability maturity", "Conway's Law", or "RACI".

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Generate beautiful, high-resolution mindmaps from Markdown unordered lists. Outputs interactive HTML, HD PNG, and PDF with colorful branch themes.

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Map this codebase into project memory — a code graph of every symbol and how they connect, plus a written profile of stack, layout, conventions and workflows. Re-run any time to refresh both in place. Use when memory is newly installed on an existing project, or when the project has changed enough that the stored map is stale.

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Nuxt SEO v5 modules (robots, sitemap, og-image, schema-org, link-checker, seo-utils, site-config). Use when implementing SEO, robots.txt, sitemaps, Schema.org, or meta tags in Nuxt apps.

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Think and work like an expert Geologist. Use when a task calls for Geologist judgment. Reasons from Steno's principles and Walther's Law through Brunton strike/dip, measured sections, hand-lens rock ID (QAPF/Folk/Dunham), thin-section petrography (PPL/XPL, Michel-Lévy, point counting), FGDC/GeMS geologic maps, NGMDB/Geolex/Macrostrat, and stereonet structural analysis while treating weathering, float, and map-as-hypothesis as first-class failure modes.

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Think and work like an expert Computer Vision Scientist. Use when a task calls for Computer Vision Scientist judgment. Reasons from image formation, projective geometry (pinhole intrinsics and extrinsics, epipolar/PnP/bundle adjustment, similarity-scale ambiguity), and COCO/LVIS AP mechanics through DINOv3/SigLIP 2 foundation baselines, RF-DETR/YOLO26/SAM 3 models, COLMAP 4/GLOMAP and VGGT geometry, pycocotools/TrackEval/BOP evaluation, and CVPR reporting and EU AI Act limits while treating train–test and pretraining leakage, AP evaluation-setting gaming, preprocessing mismatches (EXIF, BGR, aliased resizing), label noise, and camera-convention and scale errors as first-class failure modes.

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Competitive positioning, strategic group mapping, differentiation analysis, competitive advantage assessment, market positioning map, value chain positioning, brand positioning, cost leadership vs differentiation, niche strategy analysis, disruptive positioning

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Institutional-grade equity research skills for AI agents. 80 Claude-type skills across 14 verticals (equity-research-core, models-and-pitches, bio-pharm, scenarios, quantitative-analysis, idea-generation, options-derivatives, business-intelligence, industry-analysis, macro-strategy, portfolio-strategy, technical-analysis, risk-and-psychology, trading-as-business) powered by agentii.ai's agent-use-ready SEC filing data plane — SEC filings and 15K+ earnings call transcripts, 15.99M XBRL facts, and company profiles for 1,146+ US-public-equity tickers. Features the three-layer retrieval protocol (Document Discovery → Page Map → Deep Read, with deep-outline escalation) — measured, not asserted: 97.5% saved against a sequential read, and the page map itself is 10.0% of one (spec 062 T030, 2026-09-23) — server-side parallel multi-period search via search_cross_period, a full Excel/PPT generation pipeline with 3-tier office backend support, and the spec-046 governance layer (constitution, thesis and single-skill modes, mechanical gates, and a template-owned disclaimer on generated reports).

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Maps GWAS-implicated loci to candidate effector (causal) genes by integrating variant-to-gene (V2G) features via Open Targets L2G (Mountjoy 2021), MAGMA gene-based association (de Leeuw 2015), FUMA SNP2GENE, cS2G combined SNP-to-gene scores (Gazal 2022), Polygenic Priority Scores (PoPS, Weeks 2023), FLAMES, INQUISIT, DEPICT, and enhancer-gene predictors (ABC, ENCODE-rE2G). Use when narrowing a GWAS lead locus to a candidate causal gene, picking between proximity, eQTL-based, and similarity-based prioritizers, integrating multi-evidence streams (fine-mapping, colocalization, ABC enhancer-gene, distance, chromatin), reconciling discordant L2G vs PoPS calls, prioritizing tissue-specific eQTL evidence, or triangulating across at least three independent lines of evidence for a publication-grade effector-gene nomination.

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Produce and interpret PCA, t-SNE, UMAP, and PHATE plots for high-dimensional omics data with rigor about which method preserves what (variance, local structure, manifold, transitions), hyperparameter sensitivity, and the well-documented limits of 2D embeddings. Covers PCA biplot/scree/loadings, t-SNE PCA initialization (Kobak-Berens 2019), UMAP n_neighbors/min_dist trade-offs, and the Chari-Pachter 2023 critique. Use when visualizing high-dimensional data — bulk PCA, single-cell embeddings, multi-omics integration projections.

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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.

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Designs Bayesian clinical trials including Phase I dose-finding (BOIN, CRM, EWOC, mTPI-2), meta-analytic-predictive (MAP) priors with robust mixtures for external data borrowing, EXNEX for basket trials, hierarchical models for safety AE (Berry-Berry), Bayesian platform trials (I-SPY 2, GBM AGILE, REMAP-CAP), and posterior probability stopping rules. Covers FDA Bayesian Devices Guidance (2010), FDA Bayesian Methodology in Drugs Draft (January 2026), BOIN Fit-for-Purpose qualification (December 2021), and Project Optimus dose-optimisation. Use when designing dose-finding studies, platform trials, or sensitivity analyses with informative priors.

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Maps query single-cell data to reference atlases using scArches transfer learning with scVI and scANVI models. Transfers cell type labels without retraining on combined data. Use when annotating new single-cell datasets using pre-trained reference models.

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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.

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Workflow for read alignment, sorting, indexing, mapping statistics, and downstream-ready alignment artifacts.

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Analyze customer reviews for local businesses on Google Maps. Covers review extraction, sentiment patterns, rating distribution, common complaints, and competitor review comparison for local business intelligence and reputation management.

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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.

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知らない分野にキャッチアップするとき、汎用的な言葉から界隈の用語・中心の人・場（学会・雑誌・個人ブログ・動画・リポジトリ・一覧）を特定し、 引用・リンク・著者を芋づる式にたどって、当たりの原文（論文も個人の記事や動画の字幕も）を要約せずそのまま ~/research/<話題>/sources/ に落とす。 周ごとに地図（map.md）を育て、要る主張の全部に原文が付くか飽和したら止める。 見張りのモードでは、地図の場（Qiita・Zenn のタグ、note、YouTube、ブログ、arXiv）の新着を RSS で引いて原文の冒頭つきで並べる。 以下で使用:「芋づる式に調べて」「この分野をキャッチアップしたい」「界隈の用語と人から掘って」 「論文とか先行研究を漁って」「既存の技術を真似したいから元ネタを探して」「上手い人のやり方を集めて」「原文で落として」「/imozuru」 「〇〇って研究あるの？原典ほしい」「全然わからないから流れを掘って」「どういう界隈でどう呼ばれてるか知りたい」 「〇〇の新着見て」「今週の〇〇の記事まとめて」「キャッチアップ」「/imozuru watch」 "dig into this field", "find the canonical papers on X", "literature dive", "what's new in X this week"。 以下には使用しない: 答えが1つの事実の確認（年・有無だけ・仕様1つ → そのまま検索）／ 手元の PC の中の資料を探す／読んだ資料の要約や解説を書く。

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.

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Connect user journeys to screens, defining the UI structure and navigation paths during PRD v0.4 User Journeys. Triggers on requests to define screens, design screen flows, map UI structure, plan navigation, or when user asks "what screens do we need?", "define screens", "screen flow", "UI structure", "information architecture", "navigation design", "wireframe planning". Consumes UJ- (User Journey Mapping), FEA- (Feature Value Planning), BR- (constraints). Outputs SCR- entries for screens and DES- entries for design system elements. Feeds v0.5 Red Team 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。