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概要と使いどころ
Run convex-doctor static analysis, interpret findings, and fix issues across security, performance, correctness, schema, and architecture categories. Use when running convex-doctor, fixing convex-doctor warnings or errors, improving the convex-doctor score, or when asked about Convex code quality, static analysis, or linting Convex functions.
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。
Create Convex queries, mutations, and actions with proper validation, authentication, and error handling. Use when implementing new API endpoints.
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: analysing a project's debug and reliability health — inline markers, logging hygiene, and error handling.
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: the agent captures a correction from the team lead, hits an unexpected error, discovers a capability gap, or finds a better practice. Logs to .learnings/ and promotes durable patterns to permanent project memory.
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 communication features with Twilio: SMS messaging, voice calls, WhatsApp Business API, and user verification (2FA). Covers the full spectrum from simple notifications to complex IVR systems and multi-channel authentication. Critical focus on compliance, rate limits, and error handling. Use when: twilio, send SMS, text message, voice call, phone 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。
Run tests and systematically fix all failing tests using smart error grouping. Use when user asks to fix failing tests, mentions test failures, runs test suite and failures occur, or requests to make tests pass.
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。
Modern React UI patterns for loading states, error handling, and data fetching. Use when building UI components, handling async data, or managing UI states.
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。
PowerShell Windows patterns. Critical pitfalls, operator syntax, error handling.
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。
Integrate MercadoPago Checkout Pro (redirect-based) into Next.js applications with any PostgreSQL database (Supabase, AWS RDS, Neon, PlanetScale, self-hosted, Prisma, Drizzle, or raw pg). Use when the user needs to: (1) Add MercadoPago payment processing to a Next.js app, (2) Create a checkout flow with MercadoPago, (3) Set up payment webhooks for MercadoPago, (4) Build payment success/failure pages, (5) Create a shopping cart with payment integration, (6) Troubleshoot MercadoPago integration issues (auto_return errors, webhook failures, hydration mismatches, double submissions). Triggers on requests mentioning MercadoPago, Mercado Pago, payment integration with MP, Argentine/Latin American payment processing, or checkout with MercadoPago. Supports all MercadoPago countries: Argentina (ARS), Brazil (BRL), Mexico (MXN), Colombia (COP), Chile (CLP), Peru (PEN), Uruguay (UYU).
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。
Create custom external web service APIs for Moodle LMS. Use when implementing web services for course management, user tracking, quiz operations, or custom plugin functionality. Covers parameter validation, database operations, error handling, service registration, and Moodle coding standards.
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。
Next.js best practices - file conventions, RSC boundaries, data patterns, async APIs, metadata, error handling, route handlers, image/font optimization, bundling
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。
Guide for continuous improvement, error proofing, and standardization. Use this skill when the user wants to improve code quality, refactor, or discuss process improvements.
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。
Tools are how AI agents interact with the world. A well-designed tool is the difference between an agent that works and one that hallucinates, fails silently, or costs 10x more tokens than necessary. This skill covers tool design from schema to error handling. JSON Schema best practices, description writing that actually helps the LLM, validation, and the emerging MCP standard that's becoming the lingua franca for AI tools. Key insight: Tool descriptions are more important than tool implementa
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。
Autonomous agents are AI systems that can independently decompose goals, plan actions, execute tools, and self-correct without constant human guidance. The challenge isn't making them capable - it's making them reliable. Every extra decision multiplies failure probability. This skill covers agent loops (ReAct, Plan-Execute), goal decomposition, reflection patterns, and production reliability. Key insight: compounding error rates kill autonomous agents. A 95% success rate per step drops to 60% b
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。
Bash/Linux terminal patterns. Critical commands, piping, error handling, scripting. Use when working on macOS or Linux systems.
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。
Bash scripting workflow for creating production-ready shell scripts with defensive patterns, error handling, and testing.
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。
Bash/Linux terminal patterns. Critical commands, piping, error handling, scripting. Use when working on macOS or Linux systems.
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 copy following Sentry brand guidelines. Use when writing UI text, error messages, empty states, onboarding flows, 404 pages, documentation, marketing copy, or any user-facing content. Covers both Plain Speech (default) and Sentry Voice tones.
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。
Proper Lambda function structure with error handling
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 production-ready REST API endpoints with validation, error handling, authentication, and documentation. Follows best practices for security and scalability.
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。
Modern Angular UI patterns for loading states, error handling, and data display. Use when building UI components, handling async data, or managing component states.
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 spec with 98 rules for building CLI tools that AI agents can safely use. Covers structured JSON output, error handling, input contracts, safety guardrails, exit codes, and agent self-description.
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。
Uses Microsoft RESTler to perform stateful REST API fuzzing by automatically generating and executing test sequences that exercise API endpoints, discover producer-consumer dependencies between requests, and Bul: security and reliability bugs. The tester compiles an OpenAPI specification into a RESTler fuzzing grammar, configures authentication, runs test/fuzz-lean/fuzz modes, and analyzes results for 500 errors, authentication bypasses, resource leaks, and payload injection vulnerabilities. ...
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。
Deploy a new LLM model via LiteLLM (GitOps) and/or Azure (Terraform + node backend), add it to the ai toolkit, create the required pull requests, and verify end-to-end. Use when rolling out a model to any environment, adding a model to the toolkit, troubleshooting "model not available" errors, or finding pricing/token limits/model ids for any provider.
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。