「automated」の検索結果

Skill Chain Engine — compose skills into automated pipelines. One task triggers multi-skill workflows with progress tracking, auto-detection, and step management.

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。

Generate A-share end-of-day market review reports with Pandadata data, covering trade-date checks, index performance and valuation, market breadth, limit-up/down sentiment, industries/concepts, 龙虎榜, block trades, margin financing, northbound holdings, risk notes, and optional scheduled after-close automation. Use when the user asks for 今日复盘, 收盘总结, 每日市场报告, A股复盘, 龙虎榜复盘, 北向资金动向, or to set up an automated daily market review.

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。

Executes automated diagnostics using the AWS Systems Manager Automation API and SSM Documents. Collects system metrics via the CloudWatch GetMetricData API and correlates with AWS Health events.

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。

axe-core is the industry-standard accessibility testing engine by Deque Systems that automatically detects WCAG 2.0, 2.1, and 2.2 violations in web interfaces. It integrates into any existing test framework — Playwright, Cypress, Selenium, Jest — to catch accessibility issues during development rather than after 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。

Auto-Editor is a command-line application that automatically edits video and audio by analyzing loudness, motion, and other signals to cut dead space. It exports to Premiere Pro, DaVinci Resolve, Final Cut Pro, ShotCut, and Kdenlive timelines.

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。

Diagnoses ArgoCD application sync failures and degraded states using the ArgoCD REST API and argocd CLI. Queries /api/v1/applications/{name} for sync status, resource health, and operation state. Provides automated remediation steps for OutOfSync, Degraded, and Missing resource conditions.

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。

Manages ArgoCD application syncs using the argocd CLI and the Argo CD REST API (v1alpha1). Supports progressive delivery with Argo Rollouts integration and automated health checks via Kubernetes readiness probes.

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。

Manages ArgoCD application deployments via the ArgoCD REST API and argocd CLI. Configures GitOps sync policies, automated rollbacks, and multi-cluster application sets with generator 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。

Activepieces is an open-source, self-hostable workflow automation platform with 200+ integrations. It provides a visual builder for creating automated workflows and exposes all its connectors as MCP servers for AI agent use.

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。

Master orchestrator that chains all Remotion video creation skills together in a single automated pipeline. Takes a creative brief and produces a complete, production-ready Remotion video project. Use when starting a new video from scratch, when asked to "create a video", "make a video", "build a complete video", or "video from idea to 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。

Use when scraping JavaScript-heavy pages or SPAs, crawling multiple URLs concurrently, extracting structured data with reusable CSS/JSON schemas, or building automated web data pipelines. Wraps the Crawl4AI library (`crwl` CLI and Python SDK) with schema-generation patterns for LLM-free extraction. Triggers on crawl4ai, crwl, scrape JS-heavy site, scrape SPA, headless browser scrape, schema-based extraction, batch crawl, sitemap crawl, web data pipeline. SKIP when a static HTML page can be read with `defuddle` / `fetch-web` — those are faster cold-start and don't need a browser.

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 rules and lifecycle event handlers for the dev-tools package. Defines automated behaviors that trigger at specific points in the agent workflow. (converted from hooks)

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 rules and lifecycle event handlers for the core-tools package. Defines automated behaviors that trigger at specific points in the agent workflow. (converted from hooks)

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 or update automated tests for a change using the repository’s verification rules in `AGENTS.md`. Use when implementing a feature, bugfix, refactor, or regression test; prefer stable integration/API/UI coverage and pull deeper test strategy from the bundled references.

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。

AI-assisted debugging specialist with deep knowledge of modern debugging tools, observability platforms, and automated root cause analysis. Implements Cursor Debug Mode methodology — structured hypothesis ranking, targeted code instrumentation, human-in-the-loop reproduction gate, log-confirmed root cause, and mandatory cleanup.

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。

Automated health checks for framework artifacts modified during a pipeline. Validates hook syntax, security patterns (SE-01/SE-02), skill wiring, agent consistency, and routing correctness. Invoked as the final pipeline step when framework artifacts were created, modified, or deleted.

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。

Automated brownfield codebase analysis with weighted keyword scoring, three-stream analysis, and design pattern recognition. Detects project type, frameworks, dependencies, architecture patterns, and generates comprehensive project profile.

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。

Automated security scanning pipeline covering SAST (Semgrep), SCA (OWASP dependency-check), SBOM generation (Syft), vulnerability scanning (Grype), and secrets detection (detect-secrets). Invoked by security-architect for pre-commit gates, CI integration, and finding triage.

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 GitOps workflows with ArgoCD and Flux for automated, declarative Kubernetes deployments with continuous reconciliation. Use when implementing GitOps practices, automating Kubernetes deployments, or setting up declarative infrastructure management.

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。

Programmatic code style validation using AST analysis. Complements (not replaces) code-style rules by providing automated checking and instant 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。

Automated security scanner for external skill/agent content fetched from GitHub or web sources. Runs a 7-step PASS/FAIL security gate against fetched markdown/text content.

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 product photos and a minimal brief into verified, platform-adapted e-commerce detail-page systems, including product fact review, visual analysis, localized copy, 6-10 module story planning, policy-aware text-free image generation or real-photo routing, deterministic Pillow typography, platform-sized slices, long-page previews, manifests, and automated validation. Supports 淘宝/天猫/京东/拼多多/抖音/小红书/Amazon/AliExpress/Temu/TikTok Shop/eBay/Etsy/Shopify. Use when a user asks for 电商详情页, 商品详情图, 商详设计, A+ content, listing galleries, or a short-input workflow that turns product photos into ready-to-review commerce assets without inventing unsupported claims.

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, validates, packages, tests, and optimizes deployment-ready Skill files (SKILL.md + references/ + scripts/ + agents/) from design specifications. Three pre-build gates + 9-dimension quality gate (with D9a/b/c empirical evidence tiers) + behavioral iteration loop + automated description optimizer + blind version comparator. Use for building, reviewing, revising, testing, or optimizing Skills. Trigger on: "build this Skill," "review this Skill," "test my Skill," "optimize the description," "compare these Skill versions," "should this be a hook instead," "build a template first." Also: "my Skill doesn't activate," "my description truncates," "my SKILL.md is too long," "my Skill is overtriggering." Do NOT use when designing Skill methodology — that's design work, not packaging. Do NOT use for prompt compilation, project auditing, or prompt scoring (use rootnode-prompt-compilation, rootnode-project-audit, or rootnode-prompt-validation if available).

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 comprehensive model drift monitoring using Evidently AI, statistical tests (PSI, KS), and custom metrics to detect data drift and concept drift in production ML systems. Set up automated alerting and reporting workflows to catch degradation before it impacts business metrics. Use when production models show unexplained performance degradation, when new data distributions differ from training data, when seasonal shifts affect input features, or when regulatory requirements mandate model monitoring.

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。