「ask」の検索結果

Delegate a coding task to the Pi coding agent CLI (`pi`) as a background implementer, then review its diff and land it yourself. Use this whenever the user wants to delegate implementation work to Pi - phrasings like "have Pi implement X", "delegate this to pi", "run it through Pi", or "use pi to implement/fix/refactor" - or wants to run a queue of coding tasks through Pi while staying the reviewer. DO NOT USE for tasks small enough to do inline, or when the user wants the code written directly without delegating.

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Delegate a coding task to the Cursor Agent CLI (`cursor-agent`) as a background implementer, then review its diff and land it yourself. Use this whenever the user wants to hand implementation work to Cursor — phrasings like "have Cursor implement X", "delegate this to Cursor", "run it through Cursor Agent", or "use Cursor to implement/fix/refactor" — or wants to run a queue of coding tasks through Cursor while staying the reviewer. DO NOT USE for tasks small enough to do inline, or when the user wants the code written directly without delegating.

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Delegate a coding task to the Cline coding agent CLI (`cline`) as a background implementer, then review its diff and land it yourself. Use this whenever the user wants to delegate implementation work to Cline - phrasings like "have Cline implement X", "delegate this to cline", "run it through Cline", or "use cline to implement/fix/refactor" - or wants to run a queue of coding tasks through Cline while staying the reviewer. DO NOT USE for tasks small enough to do inline, or when the user wants the code written directly without delegating.

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Delegate a coding task to Oh My Pi (`omp`) as a background implementer, then review its diff and land it yourself. Use this whenever the user wants to delegate implementation work to Oh My Pi / omp - phrasings like "have omp implement X", "delegate this to oh my pi", "run it through omp", "use oh-my-pi to implement/fix/refactor" - or wants to run a queue of coding tasks through omp while staying the reviewer. DO NOT USE for tasks small enough to do inline, when the user wants the code written directly without delegating, or when they mean the original Pi CLI (`pi`) — that is pi-delegate.

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A step-by-step problem-solving loop (classify the ask, define done, gather evidence, decide, act surgically, verify by observation, report outcome-first). Use when the user says "/fable-method", "use the fable method", or "approach this like Fable", or proactively when starting any multi-step task that no task-specific skill covers. Subcommands - plan (stop after the plan), audit (grade finished work against the loop), report (rewrite an answer outcome-first).

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Analyze tau2-bench task export files to identify exactly where and why tasks failed. Map failures to specific conversation rounds, show root causes, check whether relevant documentation was available, and cite sources. Use this skill whenever you need to debug a tau2-bench evaluation, understand why an agent made wrong decisions, identify missing knowledge, or review task performance—especially when context length, eligibility criteria, or policy compliance might be at fault.

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Use this skill when the user asks about Stripe Connect configuration, charge patterns, Dashboard access, or how to get started with Connect, is building a marketplace, platform, multi-vendor store, gig platform, or subscription platform, needs to pay out sellers, vendors, or providers, mentions split payments, revenue sharing, multi-party payments, or similar payment distribution concepts, provides a company URL or business description for a recommendation, builds SaaS that routes money between parties (for example, POS, booking, invoicing — not operational SaaS without payment routing), asks about onboarding or KYC for merchants, sellers, and vendors, mentions connected account Dashboard or responsibility configurations, or asks about payment flows, white-label payments, or embedded payments.

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Explore an existing PostgreSQL database before answering questions about its data or writing SQL. Use this skill whenever a user asks for a query or a data-backed answer against an unfamiliar schema (counts, missing or failed records, recent changes), asks where a business concept lives, or asks how tables, joins, views, routines, triggers, RLS, or extensions work. Find the relevant objects with read-only pg_catalog queries, then request approval before inspecting data-derived statistics or rows. Not a schema-design or migration guide.

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Use when executing implementation plans with independent tasks in the current session or facing 3+ independent issues that can be investigated without shared state or dependencies - dispatches fresh subagent for each task with code review between tasks, enabling fast iteration with quality gates

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Run independent tasks concurrently across multiple files or targets using parallel sub-agents, with per-task model selection and LLM-as-a-judge verification. Use when tasks do not depend on each other and can run side by side.

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Design multi-agent architectures for complex tasks. Use when single-agent context limits are exceeded, when tasks decompose naturally into subtasks, or when specializing agents improves quality.

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creates draft task file in .specs/tasks/draft/ with original user intent

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AI DevKit · Track dev-lifecycle / structured-debug progress on a durable task with the ai-devkit task CLI. Use to record phase, progress, next step, blockers, and validation evidence.

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AI DevKit · Planning phase guidance for creating and reconciling feature task plans. Use when the user wants to create an implementation plan, update planning docs, mark task progress, capture blockers or new tasks, or run dev-lifecycle planning work.

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Invoke the locally installed Grok Build CLI from Codex and return Grok's response. Use whenever the user asks Codex to call, consult, run, or delegate a prompt to Grok or Grok Build, including Grok web/X searches and agentic Grok tasks. This Skill covers only the invocation mechanism, process monitoring, and response capture; it does not prescribe the task itself.

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PR code review that surfaces decay risks, design smells, and maintainability issues with concrete Symptom → Source → Consequence → Remedy findings, drawing on twelve classic engineering books. Triggers when: user asks to review code, check a PR, shares a diff or pastes code asking "does this look right?" / "any issues here?" / "ready to merge?", or asks for feedback on a function, class, or file. Also triggers when user mentions: code smells / refactoring / clean architecture / DDD / SOLID principles / Hyrum's Law / deep modules / tactical programming / conceptual integrity / Brooks's Law / Mythical Man-Month / second system effect. Do NOT trigger for: questions about how to write code from scratch, language syntax questions, or framework/tool questions where no existing code is shared.

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Build NPC AI in Unreal Engine 5 with Behavior Trees and Blackboards: composites (Selector/Sequence), tasks, decorators, services, and running the tree from an AIController. Use when creating enemy/NPC AI, BT_/BB_ assets, custom BTTask or BTService nodes, or when the user mentions Behavior Tree, Blackboard, AIController, BTTask, decorator, or service.

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Stress-test a decision or claim through a sharp, fair Q&A — the questions a good lawyer or skeptical friend would ask before you commit. Use when asked to cross-examine me, ask me hard questions about this, interrogate my plan, or make me defend this. Produces a sequenced line of probing questions (from clarifying to challenging to the killer question), space to answer, and a debrief on where your answers were strong, evasive, or exposed a gap — so weaknesses surface in private before they surface in public.

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Compare job levels (职级) across Chinese technology companies and against international ladders, to judge an offer, a job move or a promotion target: what a level usually means in scope and expectations, how levels roughly line up, and what to ask the recruiter. Use when asked P7 相当于什么级别, 跳槽职级怎么对标, 字节 2-2 对应阿里什么, 这个 offer 的职级合理吗, compare Chinese tech levels, or how does Alibaba P-level map to Google levels. Produces a level comparison built from the person's own ladders and offer details, a scope-and-expectation profile for each level, the negotiation questions to ask, and a clear note on what is approximate.

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Decode an employment benefits package into what it's actually worth and where the fine print bites. Use when someone asks 'is this offer good', 'decode my benefits package', 'what does my equity actually mean', or 'what should I ask HR before signing'. Produces a benefit-by-benefit decode with real dollar values, ranked red flags (vesting cliffs, clawbacks, 'discretionary' bonuses, unlimited-PTO economics), and the questions to ask HR before signing.

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Write status updates people actually read — the traffic-light-plus-narrative format (state first, story second), the blockers-are-asks rule, and the skimmable structure that respects a reader with thirty seconds. Use when asked write my weekly update, format our team's status posts, nobody reads my updates, or what goes in a good async check-in. Produces the update format with a filled example, the blockers-as-asks discipline, and the reader-time contract.

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Act as a body-double for a work session — a present, low-pressure companion that keeps you accountable and moving through the task without doing it for you. Use when asked be my body double, keep me company while I work, help me focus on this task, or I work better with someone there. Produces a session structure (goal, time block, check-in cadence), gentle presence and momentum nudges at intervals, distraction rescue when you drift, and an end-of-session acknowledgment — recreating the focus that comes from someone just being there.

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Review an LLM agent design and find where it will be unreliable, expensive, or unsafe. Use when asked to review an agent architecture, critique a multi-step/tool-using agent, debug an agent that loops or goes off-task, or harden an agent before launch. Produces a structured review — task fit, control flow, tools, memory/context, failure handling, cost, and safety — with prioritised findings and fixes.

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Build the context an AI needs to do a task well — the background, constraints, examples, and format it can't guess — so you get a great result on the first try instead of a generic one you have to keep correcting. Use when asked why does AI give me generic answers, how do I give AI better context, my AI results are mediocre, or how do I get it right the first time. Produces the specific context this task needs (who/what/constraints/examples/format), a reusable primer you can paste ahead of the request, the difference between a starved prompt and a well-briefed one, and what to leave out — turning vague back-and-forth into a strong first result.

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。