「modules」の検索結果

Infer gene regulatory networks from bulk or general expression data with mutual-information (ARACNe) and tree-ensemble (GENIE3, GRNBoost2) methods, and infer transcription-factor protein activity from regulons with VIPER and msVIPER. Covers the activity-not-edges paradigm, the undirected-association caveat, the DREAM5 wisdom-of-crowds and method-complementarity result, AUPRC-over-AUROC evaluation, and gold-standard incompleteness. Use when inferring a regulatory network from a bulk expression matrix, finding master regulators, or scoring TF activity from a signature. For single-cell motif-pruned regulons see scenic-regulons; for co-expression modules see coexpression-networks.

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。

Audit required bioinformatics command-line tools against Lmod or Environment Modules on Linux HPC clusters, resolve executable and version requirements, and reproducibly deploy missing software through a site-approved EasyBuild, Spack, Bioconda/micromamba, Apptainer, binary, or source-build workflow. Use when Codex must inspect module availability, diagnose a missing or wrong-version bioinformatics tool, install scientific software on Slurm/PBS clusters, publish or validate modulefiles, or produce an auditable and rollback-ready cluster software deployment.

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 reusable Convex components with isolated tables and app-facing APIs. Use for new components, reusable backend modules, integrations, or component boundary work.

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。

Shared vocabulary for designing deep modules. Use when the user wants to design or improve a module's interface, find deepening opportunities, decide where a seam goes, make code more testable or AI-navigable, or when another skill needs the deep-module vocabulary.

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 a deterministic, read-only evidence map of the repository for SDD Composy (languages, manifests, commands, modules, boundaries, staleness) and publish the context bundle only on request. Use before a PRD or TechSpec, or when the map is stale — 'mapear o repositório', 'atualizar o contexto do código', 'what does this codebase look like for SDD'. Do NOT use for architecture decisions, code review, or repository exploration without an SDD workspace.

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。

Orchestrates legacy AEM (6.x, AMS, on-prem) to AEM as a Cloud Service migration using BPA CSV or cache, CAM/MCP target discovery, and one-pattern-per-session workflow. Use for BPA/CAM findings, Cloud Service blockers, or fixes for scheduler, ResourceChangeListener, replication, EventListener, OSGi EventHandler, DAM AssetManager, HTL data-sly-test lint. OSGi configs → Cloud Manager — scan ui.config, .cfg.json, secrets, $[secret:]/$[env:] — agent follows references/osgi-cfg-json-cloud-manager.md when prompted. Transformation steps live in the best-practices skill—read it and the right references/ modules before editing 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。

Apply DESIGN, canon, and modules to every page in the inventory, producing a deployable static HTML site. Three render branches (approved page, template-applied sibling, unique render). Per-page, incremental, idempotent, content-preserving by default.

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。

AEM as a Cloud Service Java/OSGi best practices, guardrails, and legacy-to-cloud pattern transformations. Use for Cloud Service–correct bundles, deprecated APIs, schedulers, ResourceChangeListener, replication, Replicator, JCR observation (javax.jcr.observation.EventListener), OSGi Event Admin (org.osgi.service.event.EventHandler), DAM AssetManager, BPA-style fixes, HTL (Sightly) Cloud SDK lint warnings (data-sly-test redundant constant value comparison), or any time you need the detailed pattern reference modules under this skill.

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。

React Native and Expo best practices for building performant mobile apps. Use when building React Native components, optimizing list performance, implementing animations, or working with native modules. Triggers on tasks involving React Native, Expo, mobile performance, or native platform APIs.

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。

Find deepening opportunities in a codebase, informed by the domain language in CONTEXT.md and the decisions in docs/adr/. Use when the user wants to improve architecture, find refactoring opportunities, consolidate tightly-coupled modules, or make a codebase more testable and AI-navigable.

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。

Plan ATM framework refactors by preserving atom/map semantics before splitting large governance modules.

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。

Plan which model tier handles which work BEFORE execution begins — a high-cognition model deeply understands the problem, lays the foundations, then emits a modular plan assigning each module the cheapest tier that can safely execute it, with escalation tripwires and one-way-door protection. Advisory only: it announces "next module → tier X / model Y" at each boundary and the HUMAN switches models — harnesses like Cursor cannot switch mid-run. Load when the user asks which model to use, wants a model plan, model tiers, model-tier routing, assign models to tasks or modules, says "cheap model got stuck", "which model for this task", "cost-efficient model choice", or when implementation-plan / problem-to-plan need a model: tier column. NOT dynamic-routing (plan-path selection after failure) — this skill assigns cognition tiers to work.

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。

Infer gene regulatory networks from bulk or general expression data with mutual-information (ARACNe) and tree-ensemble (GENIE3, GRNBoost2) methods, and infer transcription-factor protein activity from regulons with VIPER and msVIPER. Covers the activity-not-edges paradigm, the undirected-association caveat, the DREAM5 wisdom-of-crowds and method-complementarity result, AUPRC-over-AUROC evaluation, and gold-standard incompleteness. Use when inferring a regulatory network from a bulk expression matrix, finding master regulators, or scoring TF activity from a signature. For single-cell motif-pruned regulons see scenic-regulons; for co-expression modules see coexpression-networks.

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。

Compare gene co-expression and regulatory networks between biological conditions to find rewired relationships using DiffCorr, DiffCoEx, DINGO/iDINGO, and CoDiNA. Covers the differential-connectivity-is-not-differential-expression distinction, the pairwise multiple-testing explosion, marginal vs partial (direct) rewiring, and the underpowered-rewiring failure mode. Use when comparing co-expression networks between disease vs control, treatment, or developmental stages, or finding hub genes that rewire without changing mean expression. For single-condition modules see coexpression-networks; for differential expression of means see differential-expression/de-results.

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。

Audit required bioinformatics command-line tools against Lmod or Environment Modules on Linux HPC clusters, resolve executable and version requirements, and reproducibly deploy missing software through a site-approved EasyBuild, Spack, Bioconda/micromamba, Apptainer, binary, or source-build workflow. Use when Codex must inspect module availability, diagnose a missing or wrong-version bioinformatics tool, install scientific software on Slurm/PBS clusters, publish or validate modulefiles, or produce an auditable and rollback-ready cluster software deployment.

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 carrier needs to build an internal dispatcher training program, onboard new dispatchers, or certify dispatcher competency. Covers job description, required skills, training modules, certification frameworks (CDS, CTP), shadowing, evaluation, and career path.

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。

Pull version-current Convex docs for the version this project uses — pin the installed version, fetch page-as-markdown or check node_modules types, freshness hierarchy — instead of writing a possibly-stale API from memory.

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 reusable Convex components with isolated tables and app-facing APIs. Use for new components, reusable backend modules, integrations, or component boundary work.

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 coupling and cohesion metrics in PHP codebases. Calculates Afferent/Efferent coupling (Ca/Ce), LCOM metrics, instability index, and abstractness. Identifies highly coupled modules and low cohesion classes.

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。

ABP Framework cross-cutting patterns including authorization, background jobs, distributed events, multi-tenancy, and module configuration. Use when: (1) defining permissions, (2) creating background jobs, (3) publishing/handling distributed events, (4) configuring modules.

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。

Remove refusal behaviors from open-weight LLMs using OBLITERATUS — mechanistic interpretability techniques (diff-in-means, SVD, whitened SVD, LEACE, SAE decomposition, etc.) to excise guardrails while preserving reasoning. 9 CLI methods, 28 analysis modules, 116 model presets across 5 compute tiers, tournament evaluation, and telemetry-driven recommendations. Use when a user wants to uncensor, abliterate, or remove refusal from an LLM.

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。

React Native and Expo best practices for building performant mobile apps. Use when building React Native components, optimizing list performance, implementing animations, or working with native modules. Triggers on tasks involving React Native, Expo, mobile performance, or native platform APIs.

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。

Import existing Azure resources into Terraform using Azure CLI discovery and Azure Verified Modules (AVM). Use when asked to reverse-engineer live Azure infrastructure, generate Infrastructure as Code from existing subscriptions/resource groups/resource IDs, map dependencies, derive exact import addresses from downloaded module source, prevent configuration drift, and produce AVM-based Terraform files ready for validation and planning across any Azure resource type.

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。

Install, configure, and manage 1-SEC — an open-source, all-in-one cybersecurity platform (16 modules, single binary) on Linux servers and VPS instances. Use when the user asks to secure a server, install security monitoring, set up intrusion detection, harden a VPS, protect an AI agent host, or deploy endpoint defense. Covers installation, setup, enforcement presets, module configuration, alert management, and ongoing security operations.

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。