3,394 件 ・ 関連度順
概要と使いどころ
Orchestrate the polish team: coordinates performance-analyst, technical-artist, sound-designer, and qa-tester to optimize, polish, and harden a feature or area for release 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。
Generates a comprehensive milestone progress review including feature completeness, quality metrics, risk assessment, and go/no-go recommendation. Use at milestone checkpoints or when evaluating readiness for a milestone deadline.
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。
Performs an architectural and quality code review on a specified file or set of files. Checks for coding standard compliance, architectural pattern adherence, SOLID principles, testability, and performance concerns.
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 @aleabitoreddit ("Serenity")'s distilled stock-analysis method to ANY ticker, sector, or situation — critical-chokepoint / supply-chain-OSINT idea discovery, first-principles value-chain decomposition, a Buffett-style quality gate (moat / profitability / customer-replacement risk, all default unverified), and narrative-vs-fundamentals hygiene. Produces a beginner-friendly Chinese analysis (她的观点 / 小白解释 / 第一性原理 / Buffett 直接判断 / 当前结论) that classifies an idea as 研究地图 vs 可投资结论. Trigger on "analyze like Serenity / 用 aleabito 的方法分析 / 用 Serenity 框架 / critical chokepoint 分析 / 第一性原理 + Buffett 判断这只股 / supply-chain bottleneck thesis". Never emits buy/sell calls.
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。
Route data-related requests to the right Monte Carlo skill or workflow. USE WHEN alerts, incidents, data broken, stale, coverage gaps, data quality, or any ambiguous data observability request.
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。
Investigate and remediate data quality alerts using Monte Carlo MCP tools. Runs root cause analysis, assesses blast radius, discovers available tools (MCP/CLI/API), proposes and executes fixes, or escalates with full context when uncertain.
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 durable end-to-end tests for user/API-visible behavior. Detect or scaffold the E2E framework, write tests, run the app, and store evidence. Use for E2E, Playwright/Cypress, regression tests, or quality gates. Not exploratory QA.
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。
Producing automated feature demo videos via Playwright E2E tests: scenario design, recording config, implementation patterns, quality checklists. Use for product demos or onboarding material.
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。
Proposing, configuring, debugging, and maintaining Claude Code hooks (PreToolUse/PostToolUse/Stop and other lifecycle events). Use for workflow automation or quality gates via 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。
Improving usability, interaction quality, cognitive load reduction, feedback design, and a11y compliance. Use when improving UX usability or interaction feel.
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。
Orchestrating super-premium landing-page pipelines (Discover to Launch) across 6 craft axes with rubrics and quality gates. Not for a single LP section (Funnel) or design-only chains (Atelier).
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。
Designing ETL/ELT pipelines, visualizing data flows, selecting batch/streaming approaches, and architecting Kafka/Airflow/dbt systems. Use when building data pipelines or managing data 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。
Writing and producing product videos: scripts, storyboards, narration, and reproducible Playwright demo recordings. Use for explainers, onboarding, feature walkthroughs, multi-aspect exports, captions, and video quality 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。
Converting a supplied prompt into an executable specification: detects vague quality/quantity/explanation/style/design/technical/judgment wording, role and persona theater, and self-contradiction, then replaces each with a numeric bound, an observable behavior, or a scorable criterion — with a per-term ledger of what changed and what stayed open. Don't use for AI system design, RAG, or eval harnesses (Oracle), PRD/SRS authoring (Scribe), spec conformance verification (Attest), or SKILL.md normalization (Gauge).
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。
Implementing production frontend code for React/Vue/Svelte: hooks design, state management, Server Components, form handling, data fetching. Converts Forge prototypes to production 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。
Auditing AI CLI configs and designing, configuring, or debugging Claude Code hooks. Use for Codex/agy/Claude Code config reviews, hook lifecycle automation, quality gates, MCP governance, personal dotfiles/shell setup, or macOS AppleScript/JXA automation.
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。
Systematic code review skill covering both requesting a review (pre-commit checklist) and receiving and responding to review feedback. Checks code quality, security, test coverage, architectural alignment, and documentation before any code is committed.
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。
AI-driven image generation and editing tool for creating high-quality product images. Triggered when users ask to generate images, create product pictures, edit photos, text-to-image, image-to-image, replace backgrounds, transform styles, composite products into scenes, swap models, or create any type of AI-generated visual content. Even if the user does not explicitly say "AI image," this skill should be triggered whenever the request involves creating, modifying, or transforming images.
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 pair programming with multiple modes (driver/navigator/switch), real-time verification, quality monitoring, and comprehensive testing. Supports TDD, debugging, refactoring, and learning sessions. Features automatic role switching, continuous code review, security scanning, and performance optimization with truth-score verification.
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。
Search with feature attributions — return WHY each match scored where it did. Use when debugging recall quality, auditing for bias, or explaining results to a user.
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。
Verify mini-program implementations and fixes with risk-calibrated evidence across static checks, unit tests, integration tests, state matrices, simulators, real devices, cloud environments, and release artifacts. Use when users ask to test, validate, accept, regression-check, quality-check, confirm readiness, or determine whether a mini-program feature is actually complete. Binds results to a source and build fingerprint, records commands and observable evidence, separates passed, failed, blocked, and not-run layers, prioritizes the next highest-information check, and never converts local success into device, cloud, release, or formal acceptance 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。
Code quality dashboard. (gstack)
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。