「gate」の検索結果

Use when modifying OpenAmer's own core code safely. Gate every change behind the test suite with automatic rollback.

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。

Delegate coding to OpenAI Codex CLI (features, PRs).

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 Claude Code CLI (features, PRs).

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。

They re context management, tool design, policy gates, memory strategies and, if you want to run loc

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 agent system prompts cannot serve as the sole enforcement layer — guardrails must be implemented at the API gateway and token-permission level, 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。

Use for Vercel cost and performance optimization on deployed projects, especially Next.js, SvelteKit, Nuxt, and limited Astro apps. Collect Vercel metrics, usage, project config, and code scan results first; investigate only metric-backed candidates; produce ranked recommendations grounded in verified files and version-aware Vercel/framework docs. Trigger for Vercel bill reduction, slow or expensive routes, caching opportunities, Function Invocations, Build Minutes, Fast Data Transfer, Core Web Vitals, Bot Management, Fluid compute, or cost breakdown requests.

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 a read-only, defect-first review of a specified code change and return every actionable finding. Use when another agent delegates review of uncommitted changes, a base-branch diff, a commit, or custom review instructions.

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 routine work to subagents — boilerplate generation, data transformation, file analysis, documentation drafting. Use when splitting tasks into independent subtasks for parallel subagent execution.

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 AI agent tasks remotely on Netlify using Claude, Codex, or Gemini. Use when the user wants to run an AI agent on their site, get a second opinion from another model, or delegate development tasks to run remotely against their repo.

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。

Generate read-only MongoDB queries (find) or aggregation pipelines using natural language, with collection schema context and sample documents. Use this skill whenever the user asks to write, create, or generate MongoDB queries, wants to filter/query/aggregate data in MongoDB, asks "how do I query...", needs help with query syntax, or discusses finding/filtering/grouping MongoDB documents. Also use for translating SQL-like requests to MongoDB syntax. Does NOT handle Atlas Search ($search operator), vector/semantic search ($vectorSearch operator), fuzzy matching, autocomplete indexes, or relevance scoring - use search-and-ai for those. Does NOT analyze or optimize existing queries - use mongodb-query-optimizer for that. Does NOT handle aggregation pipelines that involve write operations. Requires MongoDB MCP server.

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。

Map tasks to specialist agents. Use when choosing which agent for a job, comparing agent capabilities, or routing to React/Next.js/Playwright/docs/code-quality experts. Keywords: which agent, best agent for this, delegate to expert, agent capability mapping.

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 multiple Claude agents to investigate and fix independent problems concurrently

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。

Evaluate and improve AI-generated output with explicit rubrics, reflection loops, and stop conditions. Use when building self-critique workflows, evaluator-optimizer pipelines, or acceptance gates for code, docs, analysis, or plans.

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。

Validate documentation before merging - check completeness, broken links, code example accuracy, and factual correctness. Use when reviewing docs for quality gates or running pre-merge doc validation.

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 en edge computing v2 (CDN, edge functions, WebAssembly, IoT gateway, contexte DZ)

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。

Manage GitHub issues, PRs, branches, and repositories via OAuth (3LO). For reading issues or PR details, not for deep code analysis — delegate that to code-agent.

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。

Transform code reviews from gatekeeping to knowledge sharing through constructive feedback, systematic analysis, and collaborative improvement.

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。

Serverless and microservices development guidelines covering FastAPI, cloud-native patterns, API gateways, and best practices for scalable serverless architectures.

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。

This skill should be used when the user asks to "implement LLM-as-judge", "compare model outputs", "create evaluation rubrics", "mitigate evaluation bias", or mentions direct scoring, pairwise comparison, position bias, evaluation pipelines, or automated quality assessment.

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 coding agent. Delegate any task that involves understanding, writing, or running code — from a GitHub issue, a bug report, or a user request. It explores, implements, and verifies on its own.

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Orchestrator for the full academic research pipeline: research -> write -> integrity check -> review -> revise -> re-review -> re-revise -> final integrity check -> finalize. Coordinates deep-research, academic-paper, and academic-paper-reviewer into a seamless 10-stage workflow with mandatory integrity verification, two-stage peer review, and reproducible quality gates. Triggers on: academic pipeline, research to paper, full paper workflow, paper pipeline, end-to-end paper, research-to-publication, complete paper workflow.

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。

Navigate the gap between what you can see and what you can make. The Ira Glass gap. Use when a designer is frustrated by the distance between their taste and their output, when mentoring someone through a creative plateau, or when someone is considering quitting because their work doesn't match their vision.

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。

Decomposes a large implementation plan into small-model-sized tasks executed by supervised worker agents (e.g. Haiku) in dependency-ordered parallel waves, with a plan-first approval gate inside every task, file-based state for context compaction, and a hard escalation ladder. Use when a master plan or multi-workstream feature is too large for one session and should be executed cheaply by many small agents under frontier-model supervision.

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。