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概要と使いどころ
Orchestrate a comprehensive git workflow from code review through PR creation, leveraging specialized agents for quality assurance, testing, and deployment readiness. This workflow implements modern git best practices including Conventional Commits, automated testing, and structured PR creation.
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。
Elite code review expert specializing in modern AI-powered code analysis, security vulnerabilities, performance optimization, and production reliability. Masters static analysis tools, security scanning, and configuration review with 2024/2025 best practices. Use PROACTIVELY for code quality assurance.
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。
Measures test effectiveness. Coverlet code coverage, Stryker.NET mutation testing, flaky 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。
Audit and remediate dependency/package vulnerabilities across .NET backend, frontend (npm/pnpm/yarn), and Rust projects with severity-based quality gates. Use when the user asks for SCA checks, dependency hardening, or release security gates before build/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。
Build and maintain CI/CD pipelines, containerization, Kubernetes manifests, and quality gates for this repository. Use when the user asks for Docker, Kubernetes, GitHub Actions/Azure DevOps pipelines, deployment, reliability, or static analysis workflows.
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。
Perform repository-aware code reviews focused on bugs, regressions, architectural violations, missing tests, and operational risks. Use when the user asks for review of diffs, pull requests, patches, or implementation quality.
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 and implement observability and SRE controls including SLO/SLI, OpenTelemetry instrumentation, alerting, dashboards, and incident readiness. Use when tasks involve telemetry coverage, operational reliability metrics, incident response, or runbook quality.
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 maintain CI/CD pipelines, containerization, Kubernetes manifests, and quality gates. Use when tasks involve Docker, Kubernetes, GitHub Actions/Azure DevOps pipelines, deployment, reliability, or static analysis workflows.
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 and remediate dependency/package vulnerabilities across .NET backend, frontend (npm/pnpm/yarn), and Rust projects with severity-based quality gates. Use when the user asks for SCA checks, dependency hardening, or release security gates.
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。
Switch model profile for GSD agents (quality/balanced/budget/inherit)
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 source files changed during a phase for bugs, security issues, and code quality problems
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 SOC performance metrics and KPI tracking dashboards measuring Mean Time to Detect (MTTD), Mean Time to Respond (MTTR), alert quality ratios, analyst productivity, and detection coverage using SIEM data. Use when SOC leadership needs operational visibility, continuous improvement tracking, or executive-level reporting on security operations effectiveness.
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 structured and unstructured threat intelligence feeds to extract actionable indicators, adversary tactics, and campaign context. Use when ingesting commercial or open-source CTI feeds, evaluating feed quality, normalizing data into STIX 2.1 format, or enriching existing IOCs with campaign attribution. Activates for requests involving ThreatConnect, Recorded Future, Mandiant Advantage, MISP, AlienVault OTX, or automated feed aggregation pipelines.
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 images across AEM Edge Delivery Services pages for SEO and performance. Checks alt text quality, missing dimensions, lazy loading on LCP images, fetch priority, file naming, and decorative image handling. Generates a per-page report with specific fix instructions for alt text (in the source document) and performance attributes (in block code or scripts). Use when optimizing Core Web Vitals, improving image accessibility, or auditing image SEO.
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 code for AEM Edge Delivery Services projects. Use at the end of development (before PR) for self-review, or to review pull requests. Validates code quality, performance, accessibility, and adherence to EDS best practices.
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。
Write and review component documentation (MDX) and registry descriptions. Covers doc structure, description writing, Features, Composition, Credits, and References sections. Use when creating new component docs, updating descriptions, adding Features sections, adding Composition sections, adding Credits sections, adding References sections, or reviewing component documentation quality.
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。
Configure or modify concrete CI/CD pipelines, jobs, quality gates, test runners, or deployment automation; do not trigger merely because an existing CI gate failed or ordinary source code needs repair.
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。
Scores and prioritizes technical debt by impact, risk, and effort. Provides ranked backlog with ROI analysis and quarterly paydown recommendations. Use for "tech debt", "technical debt management", "code quality", or "refactoring priorities".
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。
Upgrades existing websites and apps to premium quality. Audits current design, identifies generic AI patterns, and applies high-end design standards without breaking functionality. Works with any CSS framework or vanilla CSS.
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。
Creates reusable prompt templates with strict output contracts, style rules, few-shot examples, and do/don't guidelines. Provides system/user prompt files, variable placeholders, output formatting instructions, and quality criteria. Use when building "prompt templates", "LLM prompts", "AI system prompts", or "prompt engineering".
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 LLM output failures including hallucinations, constraint violations, format errors, and reasoning issues. Provides root cause classification, prompt fixes, tool improvements, and new test cases. Use for "debugging AI", "fixing prompts", "quality issues", or "output errors".
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。
Compares old vs new prompts across test cases with diff summaries, stability metrics, breakage analysis, and fix suggestions. Use for "prompt testing", "A/B testing prompts", "prompt versioning", or "quality regression".
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。
Converts documents into clean, chunked datasets suitable for embeddings and vector search. Produces chunked JSONL files with metadata, deduplication logic, and quality checks. Use when preparing "training data", "vector datasets", "document processing", or "embedding 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。
Builds repeatable evaluation systems with golden datasets, scoring rubrics, pass/fail thresholds, and regression reports. Use for "LLM evaluation", "testing AI systems", "quality assurance", or "model benchmarking".
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。