Creates RPC-style endpoint following layered architecture (Controller → Manager → Repository). Use when creating new API endpoints or CRUD operations.
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。
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概要と使いどころ
Creates RPC-style endpoint following layered architecture (Controller → Manager → Repository). Use when creating new API endpoints or CRUD operations.
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 real browser QA with Playwright. Use when testing a frontend feature, verifying UI before PR, smoke testing after deploy, or investigating reported visual bugs.
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 one idea into platform-native content for X, LinkedIn, TikTok, YouTube, newsletters. Use when the user wants social posts, threads, scripts, or content calendars.
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。
Anti-AI-generic design guidelines. Use when creating UI prototypes, reviewing designs for generic AI patterns, or setting up a project design 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。
Draft cold emails, warm intro blurbs, follow-ups, and investor communications. Use when the user needs to write to angels, VCs, or accelerators.
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 JS/TS projects against NPM Security Guidelines covering project setup, dependency hygiene, CI/CD pipeline, Dependabot, and incident response. Use when reviewing package.json or lockfiles, adding or upgrading npm dependencies, setting up CI security gates, hardening a new repo, or responding to a compromised 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。
Kotlin coding standards including null safety, Either error handling, coroutines, and Exposed ORM patterns. Use when writing Kotlin code, reviewing code quality, or learning project patterns.
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。
Python/FastAPI coding standards including async patterns, Pydantic v2, SQLAlchemy 2.0, and project structure. Use when writing Python code, reviewing FastAPI projects, or learning FastAPI conventions.
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。
Security best practices for Micronaut/Kotlin backend including authentication, authorization, input validation, and OWASP prevention. Use when implementing auth, validating inputs, or reviewing security.
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。
Coordinates the full startup idea pipeline from brainstorm to investor outreach. Use when the user starts a new idea project, asks for the 'full pipeline', or references stages/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。
PR review checklist for Terraform changes covering structure, state safety, security, naming, modules, variables, providers, and CI/CD. Use when reviewing Terraform PRs or doing pre-merge 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。
UI/UX design intelligence with searchable style, palette, typography, and chart databases. Use when designing UI components, choosing colors/fonts, reviewing code for UX issues, building landing pages, or implementing responsive layouts.
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。
Scan a live web app with Playwright, extract all features, generate PRD/epics/stories with priorities and dependencies, export to Notion. Checks required MCP servers before starting.
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 blog posts, guides, tutorials, and long-form content. Sounds like a real person, not AI. Use when the user wants polished written 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。
CI/CD pipeline patterns for GitHub Actions, PR automation, and deployment workflows. Use when setting up CI, fixing broken pipelines, automating PR checks, or configuring 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。
Toolkit for styling artifacts with a theme. These artifacts can be slides, docs, reportings, HTML landing pages, etc. There are 10 pre-set themes with colors/fonts that you can apply to any artifact that has been creating, or can generate a new theme on-the-fly.
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。
Troubleshoots Monte Carlo AI agent alerts and traces — eval score drops, latency/token spikes, trajectory and validation breaches. Not for data incidents (monte-carlo-analyze-root-cause) or monitor creation (monte-carlo-monitoring-advisor).
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。
Analyze a Monte Carlo monitor and recommend config changes to reduce alert noise. Supports metric, custom SQL, validation, table, and agent (metric, evaluation, trajectory, validation) monitors. Fetches the report, identifies patterns, and suggests tuning.
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。
Instrument a new AI agent in a Python codebase for Monte Carlo Agent Observability. Detects AI libraries, installs the Monte Carlo OpenTelemetry SDK, and proposes tracing setup and decorator placements as diffs. Asks before editing any file.
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。
Check the health of a data table/asset using Monte Carlo. Activates on "how is table X", "check health of X", "is X healthy", "status of X", "check on X table", or any health/status question about a data asset.
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 data incidents and find root causes using Monte Carlo's observability data. Guides the agent through systematic investigation: alert lookup, lineage tracing, ETL checks, query analysis, and data profiling. Activates when a user asks about data issues, incidents, alerts, or why data looks wrong.
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。
Expert guide for Monte Carlo's push ingestion model. Use this skill whenever a customer or engineer mentions: pushing data to Monte Carlo, the IngestionService, pycarlo push APIs, build me a collection script, push metadata/lineage/query logs, invocation_id tracing, custom lineage nodes or edges, deleting push tables, or any question about why pushed data is not showing up. Also trigger when they ask to generate code that collects metadata, table schema, row counts, freshness, lineage, or query history from any data warehouse or data source and sends it to Monte Carlo. If the user mentions any warehouse, database, or data platform alongside any Monte Carlo topic, this skill is almost certainly relevant.
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。