「behavior」の検索結果

Validates security patches with reproducible baseline-versus-patched evidence, including original exploits, root-cause variants, behavior preservation, regressions, and newly introduced security failures. Use after a patch exists and before accepting, merging, or reporting it as fixed; also use when an AI-generated patch, remediation commit, pull request, or proposed upstream fix needs adversarial post-patch validation across any language.

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 encountering any bug, test failure, or unexpected behavior, before proposing fixes

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 when the user asks about Mixpanel product analytics, event data, funnel analysis, retention curves, cohort analysis, segmentation queries, user behavior, conversion rates, churn, DAU/MAU, ARPU, revenue metrics, feature adoption, A/B test results, user paths, flow analysis, or any request to query, explore, visualize, or analyze Mixpanel data using Python. Also use when the user asks to read, write, or manage Mixpanel "business context" — the markdown documentation that grounds AI assistants in an organization's structure and goals.

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 CircleCI configuration for speed, reliability, and maintainability. Use when users ask to improve `.circleci/config.yml`, reduce CI runtime, tune caching/workspaces/parallelism, remove pipeline waste, or fix flaky pipeline behavior caused by configuration choices.

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 custom data visualizations with D3. Use when the user needs SVG or DOM-based charts, rich annotation, domain-native contextual backgrounds, data joins, custom scales or interactions, scroll-driven SVG scene states, or precise control over browser visualization behavior.

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, run, and debug iOS apps on Simulator with XcodeBuildMCP. Use when launching an app, inspecting simulator UI or logs, or diagnosing runtime behavior.

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。

Make data visualizations accessible and inclusive. Use when the user needs chart or diagram accessibility guidance, text alternatives for complex visuals, color and contrast review, keyboard support, reduced-motion behavior for animation or parallax, or an accessibility QA workflow for exported figures, UML-like diagrams, and dashboards.

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。

Behavioral guidelines to reduce common LLM coding mistakes. Use when writing, modifying, or reviewing code — implementation tasks, code changes, refactoring, bug fixes, or feature development. Do NOT use for architecture design, documentation, or non-code tasks.

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。

Turn any ordered process into a data-driven vertical or horizontal scroll story with a base line, progress fill, active step states, responsive collapse, semantic fallback, and reduced-motion behavior. Use for onboarding, checkout, roadmaps, recipes, case studies, service processes, histories, or narratives where progress through the sequence should become visible while scrolling.

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。

Add accessible animated AI loading and agent-status indicators with the React thinking-orbs library. Use when a chat, copilot, voice, search, generation, or tool-running interface needs a semantic working, searching, solving, listening, composing, or shaping state; when replacing a generic spinner with an AI activity orb; or when implementing the library's size, theme, speed, pause, reduced-motion, and canvas behavior.

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。

Turn a reference video into a super detailed recreation or inspiration prompt. Use when the user provides, mentions, uploads, links, or points to a video and asks to analyze the design, UI, animations, transitions, scroll interactions, typography, colors, assets, WebGL/Three.js, storytelling, section-by-section behavior, or to create a prompt/article that recreates the page, app, interaction, or motion system.

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。

Implement or tune Three.js game cameras. Use for isometric framing, follow behavior, orbit/zoom limits, occlusion, lock-on, camera shake, touch camera controls, and camera regression tests.

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, implement, backfill, audit, and release in-game changelogs with contiguous versioning, deployment provenance, menu-state navigation, accessible toggle, close, and Escape behavior, and responsive release-ledger UI. Use when Codex needs to add or revise a changelog or version screen in a game, reconstruct release history from deployments and Git, define version-bump rules, keep displayed versions synchronized with live builds, or test changelog mechanics across desktop and mobile.

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。

Entry point for Internet Court — the trust layer for agent-to-agent commerce. Use whenever an agent needs to transact with another agent or a paid service, or a user mentions agent payments, paid APIs (HTTP 402/x402), wallet custody or trust concerns, spending mandates, delegated permissions (ERC-7710/7715), escrow, agent identity or reputation (ERC-8004), negotiation between agents (A2A), agent jobs (ERC-8183), machine payments (MPP, AP2), supervision of agent behavior, revocation, verification, or dispute resolution (GenLayer) — even if they never say "Internet Court". Routes to the vendored protocol skills and connector skills in this package.

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。

Add a DeepChat LLM provider through explicit reviewed source changes. Use when a developer asks Codex to add a provider, provider profile, upstream provider config, model catalog mapping, provider auth behavior, or a special provider adapter in this repository.

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。

Diagnose and repair LoopX control-plane drift or agent behavior drift. Use when a LoopX task makes unexpectedly small progress, follows a stale or contradictory recommended_action, ignores a higher-priority blocked item while doing fallback work, reports vague owner/user gates, loses todo projection, misaligns benchmark treatment with the real product path, mixes temporary artifacts into commits, or when the user asks for root-cause analysis, self-repair, or why the harness/agent behaved unexpectedly.

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 Apache Pinot regression coverage and test quality when behavior or tests change.

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。

Explain code, concepts, or system behavior with adjustable depth levels

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。

Trustpilot company profile lookup on trustpilot.com — input a company domain (e.g. apple.com, shopify.com, shopwagandtail.com) and extract company metadata: official display name, businessUnitId, TrustScore (1-5), star rating, total review count, last-12-months review count, primary category and all categories, verification flags (verifiedByGoogle, verifiedPaymentMethod, verifiedUserIdentity), claimed/closed status, website URL, contact info (email, phone, country, city, address, zip), profile image, and optional reply behavior metrics (averageDaysToReply, replyPercentage, negativeReviewsWithRepliesCount, companyUsesAIResponses). Detects company-not-found and returns isCompanyFound=false with the searched domain. Use when user mentions Trustpilot company info, Trustpilot TrustScore, look up Trustpilot rating, check Trustpilot stars, Trustpilot business profile, Trustpilot review count, Trustpilot company lookup, Trustpilot verification status, Trustpilot reply percentage, Trustpilot AI responses, business unit ID Trustpilot, get company rating from Trustpilot, scrape Trustpilot company, batch enrich domains with Trustpilot, bulk company info Trustpilot, fast domain enrichment with TrustScore, Trustpilot company metadata, Trustpilot brand monitoring, competitor TrustScore comparison, trustpilot.com domain enrichment, trustpilot company profile, trustpilot domain check, trustpilot business listing, check if company is on Trustpilot, Trustpilot category lookup, company contact info from Trustpilot. Also applies to lead-list enrichment with reputation scores, supplier vetting, agency or KOL vetting via review reputation, aggregator sites that need TrustScores for many domains, due-diligence workflows, and pre-outreach reputation checks.

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。

Four-phase debugging methodology with root cause analysis. Use when investigating bugs, fixing test failures, or troubleshooting unexpected behavior. Emphasizes NO FIXES WITHOUT ROOT CAUSE FIRST.

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。

Run Nexent requirements and bugfixes through SPEC acceptance, traditional UT and applicable D1-D5 test design, product/test implementation, local verification, and delivery evidence. Use for features, fixes, refactors and post-development test backfill across APIs, UI, model and Agent behavior.

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 implementing, debugging, or reviewing Nexent frontend pages, React components, hooks, API services, TypeScript types, styling, or localization under frontend/. Includes UI requests without file paths. Skip backend-only work, Python tests, and documentation-only changes with no frontend behavior.

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。

Implement and maintain Nexent's traditional Python UT for product changes and bugfixes under test/backend, test/sdk, and test/ext_components. Cover unit behavior, boundaries, errors and regression assertions with isolated pytest tests. Formal D1-D5 assets in test-e2e use nexent-test-assets.

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。

Toolkit for interacting with and testing local web applications using Playwright. Supports verifying frontend functionality, debugging UI behavior, capturing browser screenshots, and viewing browser logs.

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。