'task' 검색 결과

This skill should be used when the user asks to "このIssueを実装して", "implement this issue", "fix this bug", "このタスクをやって", "Issue

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This skill should be used when the user asks to "これらのIssueを全部実装して", "implement these issues autonomously", "Issue一覧を自動で片付けて", "これらのタスクを順番に実装してPRにして", or provides a list of GitHub Issue numbers/URLs for autonomous batch implementation. Fetches all specified Issues, resolves dependency order, then iterates through each — creating a worktree, implementing, and opening a PR — without user intervention.

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ガントから限定された成果群の分担を提案し、承諾後に各タスクchatのPMとそのワーカーが独立して実装・検収・正式化まで進める場合に使う。

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GitHub接続のChatGPT Proへコードの相談・許可された改修と必要検証を依頼し、Codexが完成成果を照合してcommit・PR作成更新する。Issue化にも対応する。既存Issueの実装だけなら使わない。

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作業担当が自分の限定ワーカーへ成果を委任し、同じ担当が回収・検収・修正・統合まで進める場合に使う。

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Consult Claude Code from Codex for a second opinion while Codex remains responsible for implementation and verification. Use when the user asks Codex to collaborate with Claude, consult Claude, ask Claude for a plan, get a Claude review, delegate analysis to Claude, or says "Claudeと一緒に", "Claudeに相談", "Claudeにレビューしてもらう", "Claudeの意見も聞く", or "Claudeと計画を立てる". Also use when an explicit independent planning or review pass from Claude would materially reduce risk on a complex coding task.

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Propose a new change with all artifacts generated in one step. Use when the user wants to quickly describe what they want to build and get a complete proposal with design, specs, and tasks ready for implementation.

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Run the current agent's thin semantic consistency review after shared Keel Core gates and before task completion, sync, archive, or meaningful handoff.

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Find the cheapest AI model that is smart enough for a task, and the cheapest provider to run it on, from live Artificial Analysis benchmarks via Firecrawl. Use when the user asks which model to use, how to cut their LLM or API bill, whether a cheaper model would do, which provider is cheapest or fastest for a model, which open-weights model to self-host, or wants something built with the cheapest model for each step.

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Hands work to a local model served by LM Studio and verifies what comes back. Trigger to offload, delegate or batch work to a local, offline or on-device model; to queue unattended runs that keep only what passes; to ask which installed model fits a job or whether to switch; to size a task before delegating; when a local model returns nothing, repeats itself or drifts on a long list; when LM Studio shares the GPU with an image tool, or to free its hold; or say lmstudiorunner (audit, size, queue, run, status). Reads models from the live LM Studio API. Cloud model picks and prompt text are promptwright's; a schedule's guardrails agentwright's.

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Routes general Convex requests to the right project skill. Use when the user asks which Convex skill to use or gives an underspecified Convex app task.

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Overrides default LLM truncation behavior. Enforces complete code generation, bans placeholder patterns, and handles token-limit splits cleanly. Apply to any task requiring exhaustive, unabridged output.

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Use when the user says /update, @update, update project docs, or asks to sync task.md, changelog.md, and files.md after completing work.

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Use when the user wants to design, redesign, shape, critique, audit, polish, clarify, distill, harden, optimize, adapt, animate, colorize, extract, or otherwise improve a frontend interface. Covers websites, landing pages, dashboards, product UI, app shells, components, forms, settings, onboarding, and empty states. Handles UX review, visual hierarchy, information architecture, cognitive load, accessibility, performance, responsive behavior, theming, anti-patterns, typography, fonts, spacing, layout, alignment, color, motion, micro-interactions, UX copy, error states, edge cases, i18n, and reusable design systems or tokens. Also use for bland designs that need to become bolder or more delightful, loud designs that should become quieter, live browser iteration on UI elements, or ambitious visual effects that should feel technically extraordinary. Not for backend-only or non-UI tasks.

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Elite website image-to-code skill for Codex. For visually important web tasks, it must first generate the design image(s) itself, deeply analyze them, then implement the website to match them as closely as possible. In Codex, it must prefer large, readable, section-specific images instead of tiny compressed boards, generate fresh standalone images for sections or detail views instead of cropping old ones, avoid lazy under-generation, avoid cards-inside-cards-inside-cards UI, and keep the hero clean, spacious, readable, and visible on a small laptop.

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Suggest the matching Convex component when the user hand-rolls a pattern it already solves (crons, sharded-counter, rate-limiter, storage, search, presence, workflow, RAG, prosemirror-sync). Passive — suggest after the task, never interrupt. Never install without consent.

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Automate Accelo tasks via Rube MCP (Composio). Always search tools first for current schemas.

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Automate Abyssale tasks via Rube MCP (Composio). Always search tools first for current schemas.

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Index-based file discovery using ABOUTME headers. Use INSTEAD of grep or Explore agent when searching for files by purpose or feature. Faster and more accurate than scanning code. Invoke this skill when user asks "which files handle X", "where is Y implemented", or when you need to find files related to a feature or task.

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Automate Abuselpdb tasks via Rube MCP (Composio). Always search tools first for current schemas.

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Automate Abstract tasks via Rube MCP (Composio). Always search tools first for current schemas.

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When the user requests information about academic benchmarks, datasets, or research papers, particularly in machine learning, deep learning, or logical reasoning domains. This skill enables systematic research of academic benchmarks by searching web sources, downloading and analyzing arXiv papers, extracting key metadata (number of tasks, training availability, difficulty levels), and compiling comparative summaries. It triggers on requests involving dataset comparisons, benchmark analysis, or academic paper research for table creation.

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Automate Ably tasks via Rube MCP (Composio). Always search tools first for current schemas.

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Master skill for Abaqus FEA scripting. Use for any finite element analysis, topology optimization, or Abaqus Python scripting task. Routes to appropriate specialized skills.

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