7,704 件 ・ 関連度順
概要と使いどころ
Implement application state with Angular Signals, computed derivations, and NgRx Signal Store. Use when implementing reactive state with signal(), computed(), effect(), or @ngrx/signals in Angular.
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。
This skill should be used only when the user explicitly asks to use `$ralph-specum-status`, or explicitly asks Ralph Specum in Codex for status or active spec progress.
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。
Share state between components that don't have a direct parent-child parameter relationship, using cascading values, scoped services with change events, or CascadingValueSource via DI. USE WHEN the user needs a CascadingParameter or CascadingValue that works across render mode boundaries, a shopping cart or notification count accessible from multiple pages, a theme or user preference cascaded app-wide, or when components in different parts of the tree must react when shared data changes. Also USE WHEN cascading values aren't reaching interactive children in per-page interactivity mode, or when the user needs to understand scoped vs singleton service lifetime for state on Blazor Server. DO NOT USE for direct parent-child parameter passing or EventCallback (see author-component), for persisting state across prerender-to-interactive transitions (see support-prerendering), or for service abstractions for data fetching in Auto/WebAssembly (see fetch-and-send-data).
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。
.NET MAUI app lifecycle guidance — the four app states, cross-platform Window lifecycle events (Created, Activated, Deactivated, Stopped, Resumed, Destroying), platform-specific lifecycle mapping, backgrounding and resume behavior, and state-preservation patterns. USE FOR: "app lifecycle", "window lifecycle events", "save state on background", "resume app", "OnStopped", "OnResumed", "backgrounding", "deactivated event", "ConfigureLifecycleEvents", "platform lifecycle hooks". DO NOT USE FOR: navigation events (use maui-shell-navigation), dependency injection setup (use maui-dependency-injection), platform API invocation (use conditional compilation and partial 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。
Use when a problem or issue needs explicit framing before deeper analysis begins — establishing the Problem statement, Current state, Desired state, Stakeholders, and Success criteria. This should trigger when an issue's Problem Framing point of view needs evaluation, or when a maintainer directly asks to frame a problem before root-cause analysis, design, or planning begins. Part of Plinth Toolkit
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 request lifecycle: commit, codex review, sync, review, fix, status, cleanup, and PR mining. Use when user wants to commit changes, get a second-opinion code review from Codex, push changes, create a PR, check PR status, fix review comments, clean up branches after merge, or mine tribal knowledge from PR reviews. Use for "commit my changes", "codex review", "push my changes", "create a PR", "pr status", "fix PR comments", "clean up branches", "mine PRs", or "address feedback".
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。
Screens biomedical / life-science papers for signs of data fabrication, image manipulation, and statistical anomalies, using the detection techniques distilled from the field's canonical exposure platforms (PubPeer, Data Colada, Science Integrity Digest, For Better Science) and tools (ImageTwin/Proofig, statcheck, GRIM/GRIMMER, Problematic Paper Screener, Seek & Blastn). Use when asked to check a paper/figure for image duplication, blot splicing, impossible statistics, paper-mill or tortured-phrase signals, research integrity, or "is this data faked"; or when a user shares a figure, Western blot, supplementary dataset, or DOI and asks whether it looks manipulated. Reports observable anomalies as questions for clarification — it never accuses anyone of fraud.
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。
Evidence-before-action diagnosis of failing ML experiments. Probes the system before guessing causes, process list, dmesg, GPU stats, log scrollback, checkpoint state, then states a hypothesis as a hypothesis and runs a smoke before claiming a root cause. Use when the user asks why a run is failing, diverging, OOMing, hanging, slow, producing weird metrics, has crashed, or asks to debug, diagnose, troubleshoot, or investigate a training issue.
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。
Diagnostic status report — shows metaswarm installation state, project setup, and potential issues
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。
Interactive scientific and statistical data visualization library for Python. Use when creating charts, plots, or visualizations including scatter plots, line charts, bar charts, heatmaps, 3D plots, geographic maps, statistical distributions, financial charts, and dashboards. Supports both quick visualizations (Plotly Express) and fine-grained customization (graph objects). Outputs interactive HTML or static images (PNG, PDF, SVG).
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。
MATLAB/GNU Octave numerical computing: matrices, linear algebra, ODEs, signal processing, optimization, statistics, scientific visualization. MATLAB-syntax examples run on both. For Python use numpy/scipy; for statistical modeling use statsmodels.
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。
Statistical visualization on matplotlib with native pandas support. Auto aggregation, CIs, grouping for distributions (histplot, kdeplot), categorical (boxplot, violinplot), relational (scatterplot, lineplot), regression (regplot, lmplot), matrix (heatmap, clustermap), grids (pairplot, FacetGrid). Use for quick statistical summaries; matplotlib for fine control; plotly for interactive HTML.
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。
Guided statistical analysis: test choice, assumption checks, effect sizes, power, APA reporting. Pick tests, verify assumptions, or format results for publication. Covers frequentist (t-test, ANOVA, chi-square, regression, correlation, survival, count, reliability) and Bayesian. Use statsmodels or pymc-bayesian-modeling to fit.
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。
3Dmol.js WebGL molecular visualization emitted as self-contained HTML. Render structures (PDB/SDF/XYZ/MOL2/cube) with stick, sphere, cartoon, line, and surface styles; animate trajectories with a frame-delay (interval, ms) control; and animate vibrational normal modes via vibrate() from per-atom dx/dy/dz displacements or from precomputed frames. Output standalone HTML that loads 3Dmol from a CDN, with optional play/pause and speed controls. Use for transition-state imaginary-mode animations, MD or reaction-path playback, docking poses, and orbital/density isosurfaces. For static 2D chemical structure drawings use rdkit-chemdraw-cdxml; for 2D statistical plots use matplotlib or plotly.
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 a profit & loss statement (compte de résultat / income statement) from a Well workspace. Use when the user asks for a P&L, income statement, compte de résultat, revenue vs expenses, or net result over a period.
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。
Assemble a report-ready walkthrough.md for a SOLVED engagement - populate the Evidence gallery from the poc/ images captured during the engagement, and draft the step-by-step reproduction from state/loot/log (Killchain.md for pentest/bugbounty, state.md's Chain/Status for ctf) without fabricating. Use when asked to "write the walkthrough", "assemble the walkthrough", "close out the box/engagement", or at close-out once an engagement is marked SOLVED.
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。
現セッションのリアルトークン使用量と推定削減量を表示。Claude Code セッションログから直接読込 — AI 推定なし。 `/genshijin-stats` で起動。出力は mode-tracker フックが注入し、モデル自身は数値計算しない。
Main app ViewModel patterns (ScopedViewModel, Hilt, StateFlow, triggerEvent, navArgs, savedState flows, events). Use when writing, editing, exploring, debugging, or reviewing ViewModels in the store management app. Covers navArgs, combine+asLiveData, sealed state hierarchies, custom events, init blocks, and companion object placement. NOT for POS (WooPos*) code — use the `pos` skill instead.
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。
Operate the `sno` command-line tool: install, update, check, and uninstall Sno products, read the exact commands and flags of the installed version, and run the account, skills, onboarding, Station telemetry and REM commands. Use when the owner or a task says "sno", "Sno CLI", "sno setup", "sno update", "sno doctor", "sno uninstall", "sno station", "telemetry consent", "rem verdict", "sno account", or "sno skills". The exact flags always come from the installed binary, never from this file. Do not use it to decide the owner's choices for them.
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。
US state privacy law applicability assessment tool. Evaluates revenue thresholds, data volume thresholds, business exemptions (GLBA, HIPAA, nonprofits), employee data carve-outs, and SBA small business determinations across all enacted state privacy laws.
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。
Universal opt-out mechanism implementation across US state privacy laws. Covers Global Privacy Control (GPC) signal technical implementation, state-by-state recognition requirements, browser detection methods, authenticated vs unauthenticated handling, and compliance testing.
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。
Implements HITECH Act privacy and security requirements including breach notification expansion, four-tier penalty structure, state attorney general enforcement authority, EHR meaningful use privacy conditions, and business associate direct liability. Keywords: HITECH Act, breach notification, penalty tiers, state AG enforcement, meaningful use, EHR privacy.
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。
Implements GDPR Art. 22 automated decision-making and AI Act Art. 14 human oversight requirements for AI systems. Covers identification of solely automated decisions, meaningful human intervention design, logic explanation mechanisms, and contestation procedures. Keywords: Art. 22, automated decision, human oversight, AI Act, profiling, contestation.
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。
Implements data subject rights mechanisms for AI systems including right to explanation of AI decisions, contestation procedures, human review, model output correction, and training data access. Covers GDPR Arts. 15-22 and AI Act Art. 86. Keywords: data subject rights, AI explanation, contestation, human review, training data access, model correction.
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。