「distribution」の検索結果

Segments the genome into chromatin states from combinatorial histone modification and chromatin factor ChIP-seq data. Uses ChromHMM (multivariate HMM on binarized signal, v1.27), Segway (Dynamic Bayesian Network on continuous signal), EpiSegMix (flexible-distribution HMM with duration modeling, 2024), EpiLogos (multi-biosample visualization), IDEAS (cell-type-aware joint), and full-stack ChromHMM (Vu Ernst 2022) for cross-cell-type segmentations. Handles state-count selection (15 vs 18 vs 25 states), binarization choice, OverlapEnrichment / NeighborhoodEnrichment downstream analysis, and cross-biosample integration. Use when learning chromatin states from a histone mark panel, characterizing learned states by genomic feature enrichment, or comparing chromatin landscapes across cell types.

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。

Validate alignment quality with insert size distribution, proper pairing rates, GC bias, strand balance, and other post-alignment metrics. Use when verifying alignment data quality before variant calling or quantification.

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。

Azure Cosmos DB SDK for Java. NoSQL database operations with global distribution, multi-model support, and reactive 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。

Optimize multi-agent systems with coordinated profiling, workload distribution, and cost-aware orchestration. Use when improving agent performance, throughput, or reliability.

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 power systems covering power generation, transmission, distribution, power flow analysis, fault analysis, protection, and power electronics.

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 probability theory knowledge. Use when working with probability spaces, random variables, distributions, expectation, variance, conditional probability, stochastic processes, Markov chains, martingales, or measure-theoretic probability. Also use when the user mentions 'sample space', 'random variable', 'expected value', 'variance', 'conditional probability', 'independence', 'Markov chain', 'Poisson process', 'moment generating function', 'law of large numbers', 'central limit theorem', or 'stochastic process'.

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 the quality, composition, and usefulness of a lead list. Use when the user provides a lead list and wants completeness, distribution, quality issues, and recommended next actions.

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。

Chooses ecosystem-native release and changelog tooling (Changesets, Melos, release-plz) plus binary distribution (GitHub Release tarballs, install.sh) when the product is an executable. Use for release CI, install.sh, versioning, CHANGELOGs, shipping MCP/CLI without clone, or meta repos that only ship skills via npx skills—not domain app tutorials.

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 shipping a macOS app outside the App Store — Developer ID signing with hardened runtime, notarization with notarytool, stapling, building a clean drag-to-Applications DMG, and verifying the result launches on a stranger's Mac.

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。

Measure and audit C/C++ code size, namespace distribution, function complexity, nesting, recurrence, and namespace coupling in the EMULATORS framework. Use when Codex needs codebase statistics, complexity baselines, namespace comparisons, duplication indicators, hotspots, architectural growth analysis, or before/after complexity reports while excluding third-party code.

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。

Azure Cosmos DB SDK for Java. NoSQL database operations with global distribution, multi-model support, and reactive patterns. Triggers: "CosmosClient java", "CosmosAsyncClient", "cosmos database java", "cosmosdb java", "document database java".

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。

Optimize multi-agent systems with coordinated profiling, workload distribution, and cost-aware orchestration. Use when improving agent performance, throughput, or reliability.

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。

Builds automated threat intelligence feed integration pipelines connecting STIX/TAXII feeds, open-source threat intel, and commercial TI platforms into SIEM and security tools for real-time IOC matching and alerting. Use when SOC teams need to operationalize threat intelligence by automating feed ingestion, normalization, scoring, and distribution to detection systems.

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。

Analyze the threat landscape using MISP (Malware Information Sharing Platform) by querying event statistics, attribute distributions, threat actor galaxy clusters, and tag trends over time. Uses PyMISP to pull event data, compute IOC type breakdowns, identify top threat actors and malware families, and generate threat landscape reports with temporal trends.

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。

Volume-based PUMP token reward system with day-indexed epochs, pro-rata distribution, accumulator tracking, and cross-program claiming on Solana.

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 and release pipeline for the Pump SDK — tsup TypeScript builds, Cargo release profiles, semantic release with commitizen, npm publishing, linting, Makefile targets, Vercel deployment, and MCP server distribution.

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 meeting minutes documents for completeness, action item clarity, decision documentation, and consistency with source materials like hearing sheets. Use when validating meeting minutes quality or preparing minutes for distribution.

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。

Archive, export, and notarize macOS apps using xcodebuild and asc. Use when you need to prepare a macOS app for distribution outside the App Store with Developer ID signing and Apple notarization.

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。

Orchestrate TestFlight distribution, groups, testers, and What to Test notes using asc. Use when rolling out betas.

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。

Knowledge base from the NIST/SEMATECH e-Handbook of Statistical Methods (NIST HB 151) — the practical statistics reference for engineering, metrology, and quality. Use for: exploratory data analysis and the four univariate assumptions (4-plot); measurement process characterization including bias/precision, calibration designs, gauge R&R, and ISO/GUM uncertainty budgets; production process characterization (stability vs. capability); process modeling and regression (LS/WLS/NLS/LOESS); design of experiments (screening, fractional/factorial, response-surface, Taguchi); statistical process control (Shewhart/CUSUM/EWMA charts, capability indices, acceptance sampling); product/process comparisons (hypothesis tests and confidence intervals for 1/2/3+ groups, ANOVA, multiple comparisons); and reliability (lifetime & repair-rate models, accelerated testing, reliability growth). Scope limits: this is applied frequentist statistics for measurement and quality — it does NOT reproduce the per-distribution formula galleries, worked case studies, datasets, plot images, or Dataplot/R code of the original web Handbook (those are described, not copied); it is thin on modern machine learning, Bayesian methods beyond conjugate reliability priors, time-series/forecasting, and Bayesian experimental 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。

Knowledge base from OMB Circular A-94 (revised 2023-11-09) on guidelines and discount rates for benefit-cost and cost-effectiveness analysis of federal programs. Use for BCA principles, benefit/cost measurement, discount-rate policy, uncertainty/sensitivity, distributional incidence, and decision reporting. Rescoped at build to A-94 only (Army CBA Guide unreachable). Does not replace A-11 budget preparation detail, agency-specific statutory BCA rules, or Army CEAC process templates.

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。

Knowledge base from FAA-HDBK-006D (2020), the FAA's System Reliability, Maintainability, and Availability (RMA) Handbook for National Airspace System (NAS) hardware and software. Use for RMA figures of merit (MTBF, MTBO, MTTR, the availability variants), the bathtub curve and probability distributions, the four-stage RMA lifecycle mapped to the FAA Acquisition Management System (AMS), the six-task RMA technical-management/acquisition process, service-thread criticality and the NAS-RD severity-to-target table, the RMA analysis toolbox (RBD, FMEA/FMECA, FTA, Ishikawa, Monte Carlo, Bayesian, FRACAS, ALT, reliability-growth/recovery tests), software reliability (early prediction + reliability growth), and tailorable example RMA requirements for a Program Requirements Document. Scope limits: it is FAA/NAS-specific guidance (not a requirement, by its own statement), centred on hardware-plus-software availability — it brackets out human/service/facility/communications availability, names but does not reproduce the math derivations or external standards (MIL-STDs, IEEE 1633, RTCA DO-178C/DO-278A, SAE JA1011), is thin on detailed worked numerical examples and on safety-case method, and predates any current revision beyond 006D.

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。

Signpost (not a knowledge pack) for the automotive standards landscape: ISO 26262, ASPICE, SAE J3016, UNECE R155, UNECE R156, and the NHTSA vehicle set. Contains NO source content: designation, edition, owner, status, and an official catalogue URL only. Use when you need to identify or locate an automotive standard. Most are paywalled or carry no redistribution grant and cannot be packaged; the one open path is the nhtsa-vehicle pack.

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。

Signpost (not a knowledge pack) for the functional safety standards landscape: IEC 61508, ISO 26262, ISO/SAE 21434, UL 4600, SAE J3016, and ASPICE. Contains NO source content: designation, edition, owner, status, and an official catalogue URL only. Use when you need to identify or locate a functional safety standard. All six are paywalled or carry no redistribution grant and cannot be packaged; open paths point at US Government packs, which are not equivalents.

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。