「rev」の検索結果

Shaping the Safety Boundaries: Understanding and … — Vor einem Tag · Abstract Jailbreaking in Large

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。

Delegate coding to OpenCode CLI (features, PR 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。

Our large-scale experiment with 19 LLMs reveals that current models degrade documents during delegation: even frontier models (Gemini 3.

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。

A review of AI edge devices and lightweight CNN and LLM deployment — 21. Jan. 2025 · However, deploy GAP: OpenHands has a modular SDK design — our too

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Use when creating or editing cron jobs - prevents timeouts, silent failures, and delivery gaps.

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。

The paper 'When EOS Tokens Disagree: Understanding Length Inflation in On-Policy Distillation' revea

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 REST and GraphQL API design principles to build intuitive, scalable, and maintainable APIs that delight developers. Use when designing new APIs, reviewing API specifications, or establishing API design standards.

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Regression testing strategies for AI-assisted development. Sandbox-mode API testing without database dependencies, automated bug-check workflows, and patterns to catch AI blind spots where the same model writes and reviews 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。

Write and maintain Architecture Decision Records (ADRs) following best practices for technical decision documentation. Use when documenting significant technical decisions, reviewing past architectural choices, or establishing decision processes.

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Use after generating code, after accepting AI suggestions, or when reviewing AI-written modules. Also use when code works but feels brittle, when error handling seems thin, when orphaned resources or missing cleanup are suspected, or when the agent claims done but hidden debt may exist. Catches the specific failure patterns AI agents produce that humans would not.

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 applications and websites to Vercel. Use when the user requests deployment actions like "deploy my app", "deploy and give me the link", "push this live", or "create a preview deployment".

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Systematic strategic planning framework for philosophy and interdisciplinary academic papers targeting preprint platforms (PhilArchive, arXiv, PhilSci-Archive). Use when users want to: (1) plan a paper on a specific topic, (2) identify research gaps and assess originality, (3) develop optimized paper outlines, (4) prepare for preprint submission, or (5) understand platform requirements and writing standards. Triggered by phrases like 'plan a paper on,' 'help me design a paper about,' 'identify research gaps in,' 'is this idea original,' or when users need structured research planning. The skill guides through three phases: Platform Analysis (identifying target venue and studying sample papers), Theoretical Framework (AI-driven literature search and gap identification), and Outline Optimization (structured design with reviewer-perspective self-assessment). Each phase includes quality evaluation standards and validation checkpoints.

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Comprehensive web quality audit covering performance, accessibility, SEO, and best practices. Use when asked to "audit my site", "review web quality", "run lighthouse audit", "check page quality", or "optimize my website".

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Build or review production-ready Tavily SDK and API integrations for web search, extraction, crawling, mapping, and research. Use when implementing Tavily in an agent, RAG pipeline, or application rather than only running one CLI command.

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Search previous Claude Code conversations for facts, patterns, decisions, and context using semantic or text search

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Repository-grounded threat modeling that enumerates trust boundaries, assets, attacker capabilities, abuse paths, and mitigations, and writes a concise Markdown threat model. Trigger only when the user explicitly asks to threat model a codebase or path, enumerate threats/abuse paths, or perform AppSec threat modeling. Do not trigger for general architecture summaries, code review, or non-security design work.

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。

Execute implementation plan by dispatching fresh subagent for each task, with code review between tasks

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。

React and Next.js performance optimization guidelines from Vercel Engineering. This skill should be used when writing, reviewing, or refactoring React/Next.js code to ensure optimal performance patterns. Triggers on tasks involving React components, Next.js pages, data fetching, bundle optimization, or performance 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。

Capture a self-contained static HTML snapshot from a running app or mock component so it can be reviewed or uploaded to Stitch.

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。

JUnit 5 testing patterns and parameterized-test guidance. Use when writing or reviewing Java unit 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。

Java Javadoc best practices. Use when adding or reviewing documentation for Java types, methods, packages, and public APIs.

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。

Zero-config Postgres for Netlify apps via @netlify/database — querying data from Functions/Edge Functions, writing schema migrations, setting up Drizzle ORM, local dev with netlify dev, database branches for deploy previews, and migrating an existing Postgres project onto Netlify. Use when adding a database, building a contact form or CRUD API, writing SQL migrations, wiring up Drizzle, running netlify database commands, testing with a local Postgres, or switching from Neon/Supabase/RDS to Netlify Database.

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。

Recognize when disagreements reveal valuable context, preserve multiple valid approaches instead of forcing premature resolution

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。

Optimize MongoDB client connection configuration (pools, timeouts, patterns) for any supported driver language. Use this skill when working/updating/reviewing on functions that instantiate or configure a MongoDB client (eg, when calling `connect()`), configuring connection pools, troubleshooting connection errors (ECONNREFUSED, timeouts, pool exhaustion), optimizing performance issues related to connections. This includes scenarios like building serverless functions with MongoDB, creating API endpoints that use MongoDB, optimizing high-traffic MongoDB applications, creating long-running tasks and concurrency, or debugging connection-related failures.

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。