「plans」の検索結果

AI DevKit · Systematic structural or multi-file refactors across any stack while preserving behavior and public contracts. Use for reorganizing modules, boundaries, naming, APIs/contracts, staged refactor plans, or refactor risk review.

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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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AI DevKit · Implementation phase guidance for executing feature plans and checking implementation against design. Use when the user wants to implement planned tasks, update implementation docs, verify code matches design, or run dev-lifecycle phases 5 and 7.

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End-to-end user research assistant — qualitative and quantitative. Use this skill whenever the user mentions user research, user interviews, discussion guides, interview guides, research plans, qualitative research, quantitative research, user surveys, survey design, usability studies, participant recruitment, research synthesis, interview transcripts, research reports, running studies with AI, or explicitly mentions Cookiy AI. Also trigger when users want to talk to customers, conduct discovery research, create a study or survey, analyze interview data, run AI-moderated interviews, or collect survey responses. Covers the full lifecycle: planning studies, creating discussion guides, running AI-moderated interviews (real or synthetic) via Cookiy, designing and distributing surveys, and synthesizing results into reports.

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Optimize existing Azure resources and analyze Reservations or Savings Plans. WHEN: "optimize Azure costs", "reduce cloud spending", "rightsize resources", "find idle resources", "orphaned disk", "deleted VM still charged", "public IP still charging", "reservation utilization", "Savings Plan coverage", "commitment recommendation", "why is pay-as-you-go still charged". DO NOT USE FOR: cost spikes, forecasts, pricing estimates, budgets, or governance.

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Assess and upgrade Azure workloads between plans, tiers, or SKUs, or modernize Azure SDK dependencies in source code. WHEN: upgrade Consumption to Flex Consumption, upgrade Azure Functions plan, change hosting plan, function app SKU, migrate App Service to Container Apps, modernize legacy Azure Java SDKs (com.microsoft.azure to com.azure), migrate Azure Cache for Redis (ACR/ACRE) to Azure Managed Redis (AMR).

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Guides the creation of technical design documents before writing code, producing architecture diagrams, data models, API interface definitions, implementation plans, and multi-option trade-off analyses. Use when the user asks to plan a feature, architect a system, design an API, explore implementation approaches, or requests a technical design or spec before coding — especially for complex features involving multiple components, ambiguous requirements, or significant architectural changes.

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Performance, load, and reliability (SLO/SRE) planning for production readiness. Use when defining service level objectives, load characterization, capacity, latency budgets, stress/soak/spike test plans, false-positive baselines, and reliability targets. USE FOR: SLO/SLA definition, load testing plan, performance budget, capacity planning, reliability/SRE backlog, latency targets, error-budget policy. DO NOT USE FOR: executing load tests (use Azure Load Testing tooling), security threat modeling, RAI assessment, privacy/compliance planning, or authoring/restating PRD requirements (cite the PRD's existing NFR/FR ids instead).

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Drive structured autoresearch iteration after evo:discover and the baseline commit. Use when the user invokes /evo:optimize or asks to try ideas, try variants, run experiments, use available GPUs, improve the current best/frontier, continue an evo search, or compare candidate changes in an evo workspace. The orchestrator plans and spawns optimization subagents; candidate edits/runs belong to those subagents. Width is set via subagents=N (1 for serial workloads, larger for parallel); the loop's structural value applies at any width.

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Work a GitHub issue in the octane repo. Use when given an issue number or link. Fetches the issue, classifies the owning area, and plans the fix.

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Multi-path parallel product analysis with cross-model test-time compute scaling. Spawns parallel agents (Claude Code agent teams + Codex CLI) to explore product from multiple perspectives, then synthesizes findings into actionable optimization plans. Can invoke competitors-analysis for competitive benchmarking. Use when "product audit", "self-review", "发布前审查", "产品分析", "analyze our product", "UX audit", or "信息架构审计".

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Sets up QA testing processes: test strategies, Google Testing Standards test cases, P0-P4 bug tracking, quality metrics, progress reports, autonomous execution via master prompts, and OWASP security testing, with third-party QA handoff docs. Use when establishing QA infrastructure, writing test cases, executing test plans, or tracking bugs. Not for auditing an already-rendered UI (use frontend-visual-qa).

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Use when asked 公开课怎么准备, 帮我写说课稿, 优质课比赛, 评优课, 教研课, 汇报课, 青年教师赛课, 试讲, or prepare an observed lesson in a Chinese school. Produces a 公开课 preparation pack in Chinese: a 说课稿 (说教材, 说学情, 说教学目标, 说重难点, 说教法学法, 说教学过程, 说板书) timed to the required length, the lesson's highlights (亮点) and how observers will see them, a rehearsal and 磨课 plan, contingency plans, and answers to likely 评课 questions.

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Surface the hidden assumptions a plan or belief rests on, then test what happens when each one is wrong. Use when asked what am I assuming here, check my assumptions, what if I'm wrong about, or stress-test my thinking. Produces the unstated assumptions your conclusion depends on (ranked by how load-bearing they are), a flip of each to see which one breaking would change everything, and the cheapest way to check the riskiest ones — because the assumption you didn't know you were making is what sinks plans.

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Extract and risk-rate hidden assumptions in a product brief or PRD. Use when asked to review a product brief for assumptions, audit a PRD for risks, find hidden assumptions, validate product plans, or run an assumption analysis. Produces a prioritised assumption map with confidence and impact scores, recommended validation methods, and critical assumption flags.

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Draft the hard personal message you keep putting off — chasing money a friend owes, backing out of plans, checking in after a fight, following up on an unanswered text. Use when asked to help send an awkward text, how to say something uncomfortable to a friend, word a difficult personal message, or bring up something touchy. Produces a couple of calibrated options, the one line to open with, and the send/wait/call judgement call — warm, honest, and not a doormat.

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Use when you have an approved spec or written requirements for a multi-step task that needs a durable plan document before touching code. Small, single-owner, or fast-path tasks do not need this skill.

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Review changes for correctness, security, accessibility, and performance. Use for scoped quality reviews or verification plans; bug fixes use oma-debug.

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Create or review Terraform infrastructure and plans. Use for cloud resources, IAM, networking, state management, and infrastructure changes.

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Break large features into independently verifiable, human-reviewable slices under a feature directory in specs/. Use for risky or multi-step feature work that needs upfront questioning, API seams, browser-playable checkpoints, HTML visualizations, screenshot gates, staged implementation plans, recursive fog-of-war reslicing, or proactive research into reference implementations/best practices before slicing. Pairs with your project's verification harness and screenshot gates (the browser checkpoints), [refactor-clean](../refactor-clean/SKILL.md) (review the materialized spec so the plan describes one-owner architecture, not the feature bolted on), [screenshot-critique](../../visual/screenshot-critique/SKILL.md) and [compare-screenshots](../../visual/compare-screenshots/SKILL.md) (the visual gates), and a code-review pass (audit each slice before it lands).

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Use AtomLane to compile and execute safe atomic parallel plans on macOS and native Windows Preview for worthwhile independent argv tasks, dependency DAGs, supported platform entrypoints, or Apple-silicon operators. Use at task start or an execution boundary when structured local work may contain two or more worthwhile units; skip plain answers, one quick command, and work whose effects cannot be safely bounded.

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Simplify code, plans and systems without losing required outcomes; delete machinery that earns nothing; for code, AST-measured complexity with regression gates. Triggers: simplify, refactor, complexity, overengineered, dead code, useless guard.

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Use when handling research ethics, consent, community accountability, and data/transparency for an American Anthropologist (AA) manuscript — informed consent, anonymization, protection of vulnerable interlocutors, heritage and repatriation obligations, and what (and what NOT) to share. At AA this is an ethics-and-accountability skill grounded in the AAA ethics principles and an ethics of care. Plans the ethics and transparency; it does not grant waivers.

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Use when building exhibits for an American Anthropologist (AA) manuscript — photographs, maps, site plans, artifact images, kinship diagrams, transcripts, evidence tables, and multimodal media — so they are self-contained, accessible (alt text), ethically cleared (consent, permissions), and count toward the word budget. Designs exhibits; it does not run analysis or clear permissions for you.

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。