「method」の検索結果

Foundational theory for process-oriented concurrency through synchronous message-passing, applicable to multi-agent coordination and parallel decomposition

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。

Patterns for vibe coding that produce production-quality output instead of garbage. Covers prompt engineering for AI coding, verification without reading every line, accept/reject/refine decision framework, architectural guardrails, context management (CLAUDE.md, .cursor/rules), TDD-driven vibe coding, screenshot-driven development, diff review discipline, and the anti-patterns that destroy quality. Activate on: "vibe coding", "AI coding quality", "Claude Code workflow", "AI-assisted development", "how to use AI for coding", "coding with AI", "agentic engineering workflow", "AI code quality", "vibe code without garbage", "production quality AI code", "CLAUDE.md setup", "cursorrules setup", "pre-commit hooks for AI", "TDD with AI", "test-driven vibe coding". NOT for: multi-person collaborative coding sessions (use cooperative-vibe-coding), building AI/LLM applications (use ai-engineer), prompt engineering for non-coding LLM use cases (use prompt-engineer).

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 stuck on a hard problem, decomposition is unclear, a related problem is needed, or explicit looking-back review will improve transfer. NOT for routine execution with a known procedure, rote checklist work, or domain questions that need specialized subject matter expertise instead of heuristic search.

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。

Structured interview and observation techniques for extracting tacit knowledge from domain experts

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。

Domain-Driven Design methodology using bounded contexts, ubiquitous language, and strategic/tactical patterns

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。

Star schema, snowflake schema, SCD types, and Kimball methodology for analytical data modeling. Activate on: dimensional model, star schema, snowflake schema, SCD, fact table, dimension table, Kimball, grain, surrogate key. NOT for: dbt implementation (use dbt-analytics-engineer), warehouse tuning (use data-warehouse-optimizer).

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。

Cooperative vibe coding across machines — real-time collaborative development with humans and AI agents. Covers current tools (Live Share, Code With Me, Zed, Cursor multiplayer, tmux), git-based async patterns, AI agent coordination, and the aspirational future of multi-human multi-agent development. Activate on: "pair programming", "vibe coding together", "collaborative coding", "mob programming", "remote pairing", "code together", "multiplayer coding", "Live Share", "Code With Me", "ensemble programming", "cooperative development". NOT for: building a real-time collaboration ENGINE (use real-time-collaboration-engine), git workflow mechanics (use git-best-practices), single-user AI coding assistance (use prompt-engineer).

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。

Review of Cognitive Systems Engineering applications and current practice in safety-critical domains

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。

Identify and counteract cognitive biases in medical decision-making through systematic error analysis and contextual algorithm application. For diagnostic reasoning, treatment decisions, and clinical judgment improvement. NOT for basic medical knowledge, technical procedures, or non-clinical healthcare domains.

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 implement systematic checklists that reduce errors by 30-50% in complex, high-stakes domains (medicine, aviation, construction, finance). NOT for simple tasks or when comprehensive instruction is needed.

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 patterns for BDI agents using the MORA methodology for practical multi-agent system 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。

Apply cognitive task analysis to expert work that depends on perceptual cues, branching judgment, and recurring monitoring loops. Use when decomposing expert capability into agent structure, simulation design, or validation interviews. NOT for ordinary step-by-step SOP capture or simple pipelines with no tacit cue layer.

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。

Structured second-pass reasoning after code review or plan evaluation. Offers named reasoning methods (pre-mortem, inversion, red team, Socratic, etc.) to re-examine findings through a specific lens. Use after /mk:review verdict or any time deeper analysis is needed on an existing output. NOT for initial review (see mk:review); NOT for creating implementation plans (see mk:plan-creator).

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 at the START of every task, before writing any code, running tests, debugging, reviewing, or designing. This skill determines which methodology and domain skills must be invoked. MUST be invoked before any implementation, debugging, review, testing, or specification work begins.

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 and removes doublets/aggregates from flow, spectral, and mass cytometry before clustering or quantification. Covers FSC-A vs FSC-H singlet discrimination (the Area-Height non-proportionality, not a 1D area gate), FSC-W/SSC width gating, CyTOF Gaussian discrimination parameters (Center/Offset/Width/Residual/Event_length) and DNA intercalator gating, and the residual heterotypic conjugates that survive scatter gating and masquerade as double-positive populations. Use when filtering aggregates before phenotyping, choosing a doublet method for flow vs CyTOF, or diagnosing a suspicious double-positive cluster.

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。

Extracts cfDNA fragmentomics features (DELFI genome-wide short/long ratios, WPS nucleosome positioning, Griffin GC-corrected accessibility profiles, end-motifs/MDS, OCF) for cancer detection and tissue-of-origin from plasma WGS. Centers on the nuclease-footprint reframe (every feature re-reads one nucleosome object), the mandatory GC correction, and the cross-protocol non-comparability that breaks naive classifiers. Runs FinaleToolkit (real CLI/Python, MIT) and the Griffin Snakemake pipeline; DELFI is a method, not a package. Use when deriving fragment-based signal from cfDNA, choosing a feature family for detection vs subtyping, or diagnosing why a fragmentomic model failed validation.

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 genomics experiments so technical nuisance variation (batch, lane, plate, flow cell, operator, reagent lot, processing day) is balanced against the biological variable of interest and therefore estimable rather than confounded, using constrained sample-to-batch assignment (designit, OSAT), the confounder/mediator/collider distinction, and the principle that no post-hoc correction recovers a fully confounded design. Covers detecting hidden batches with surrogate variable analysis, a decision table for downstream correction (ComBat-seq, RUVSeq, SVA) whose execution is deferred to differential-expression/batch-correction, and reproducibility metadata. Use when assigning samples to sequencing batches/lanes/plates, avoiding batch-condition confounding, deciding whether a design is salvageable by correction, choosing a correction method, or estimating the number of hidden batches. For the experimental unit, randomization, and blocking concepts see experimental-design/randomization-blocking.

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。

Controls error rates across thousands of simultaneous tests in genomics discovery using false-discovery-rate methods (Benjamini-Hochberg 1995; Benjamini-Yekutieli 2001 for arbitrary dependence; Storey q-value with pi0 estimation; local FDR; independent filtering Bourgon 2010; covariate-weighted FDR via IHW Ignatiadis 2016), plus family-wise error control (Bonferroni, Holm) and the GWAS genome-wide threshold. Covers the FDR-versus-FWER choice as the discovery-versus-confirmatory distinction, the dependence assumptions behind BH (PRDS) versus BY, pi0 estimation, the independent-filtering and false-coverage-rate traps, and reproducibility ranking via IDR (Li 2011). Use when correcting p-values from genome-wide tests, choosing between BH/BY/q-value/Bonferroni, setting an FDR threshold, applying IHW or independent filtering, or interpreting q-values. For confirmatory trials with few pre-specified endpoints (closed testing, graphical/gatekeeping), see clinical-biostatistics/multiplicity-graphical.

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。

Extracts, filters, annotates, and exports differential expression results from DESeq2 or edgeR with proper handling of padj=NA (independent filtering, Cook's outliers, all-zero), multiple-testing correction choice (BH vs Storey q-value vs IHW vs lfsr), TREAT vs post-hoc fold-change filtering, p-value histogram diagnostics, gene annotation via org.db/biomaRt/mygene, GSEA preranked input, ORA background construction, replication reality (Schurch 2016 small-n result), and SABV/sex-stratified reporting. Use when extracting and interpreting DE results, troubleshooting padj=NA, choosing FDR method, preparing ranked lists for pathway analysis, annotating gene IDs, or comparing DESeq2 vs edgeR outputs.

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 pooled CRISPR screens with MAGeCK (Li et al 2014), covering count generation (mageck count), the RRA two-condition workflow (mageck test using alpha-RRA over per-sgRNA negative-binomial p-values), the MLE multi-condition workflow (mageck mle with explicit design matrix and beta-score output), normalization choice (median vs total vs control-sgRNA vs spike-in), sgRNA efficiency injection, paired-sample testing, time-course design, drug-screen versus dropout-screen design matrices, MAGeCKFlute and MAGeCK-VISPR downstream visualization, and decision logic for when to use MAGeCK vs JACKS / BAGEL2 / drugZ / Chronos. Use when running a fresh CRISPR screen analysis, picking RRA vs MLE for the experimental design, choosing a normalization method from QC signatures, debugging MLE convergence failure or NaN beta scores, comparing MAGeCK output across tools, or building a batch-aware multi-cell-line / multi-condition MLE design matrix.

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。

Tests genotype-environment associations and identifies adaptive loci while correcting for the four-confound landscape (structure, demography, background selection, sampling design) using LFMM2 with mandatory K via sNMF cross-entropy elbow (LEA 3), BayPass Core/AUX/C2/IS with Omega covariance matrix, RDA / pRDA for polygenic adaptation (Forester 2018; requires imputed genotypes), OutFLANK with trimmed FST null, pcadapt, gradient forests (Ellis-Smith-Pitcher 2012, NOT mis-cited Ellis-Manel), Capblancq & Forester 2021 RDA Swiss-army-knife, genomic-offset prediction with Lind & Lotterhos 2025 three-regime caveat, Lotterhos-Whitlock sampling optima, Wang & Bradburd 2014 IBD vs IBE, and Circuitscape + ResistanceGA. Use when identifying adaptive loci across gradients, choosing K for LFMM2, deciding among GEA methods, predicting maladaptation with the novel-environment caveat, distinguishing IBD vs IBE, or optimizing sampling design.

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 pooled prime-editor (PE) screens for installing precise genetic variants without bystander confounding. Covers pegRNA design with PRIDICT and PRIDICT2 for predicting per-pegRNA editing efficiency, pegRNA architecture (spacer + scaffold + PBS + RTT), PE2/PE3/PE3b/PEmax variants, MOSAIC in situ saturation mutagenesis, the PRIME pooled-screen methodology (Ren 2023; ~3,699 ClinVar variant screens), chromatin context as a major locus-level determinant of PE efficiency, scaffold-incorporation and indel byproduct quantification with CRISPResso2, and the cross-modal validation strategy of PE + base-editor screens for variant function. Use when designing a pegRNA library for variant installation, choosing between BE and PE for a specific edit, predicting pegRNA efficiency before library synthesis, analyzing PE screen output, distinguishing intended-edit from scaffold-incorporation, or scaling PE screens to thousands of variants.

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。

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.

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。

Assesses genetic health of populations for conservation with Ne estimation across time horizons (LDNe NeEstimator V2 option-file API + SNeP physical-linkage correction; recent trajectory via GONE/GONE2; deep history via Stairway Plot 2 / dadi / fastsimcoal2 / PSMC), F-statistics, runs of homozygosity binned by length class to date inbreeding, genetic-load decomposition (Bertorelle 2022 realized vs masked), the modern 100/1000 Ne rule (Frankham 2014), Ne/Nc 2-6 orders of magnitude in marine fish (Hauser & Carvalho 2008), tree-sequence forward simulations (SLiM 4 + pyslim + tskit), and the Sukumaran-Knowles caveat against MSC methods for management-unit definition. Use when estimating Ne by time horizon, detecting inbreeding via F_ROH, decomposing genetic load, justifying conservation thresholds, distinguishing ESU/MU/DPS, configuring NeEstimator V2, or correcting LDNe physical linkage.

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。