「case」の検索結果

Applies systematic tracing and isolation techniques to pinpoint exactly where a bug originates in code. Use when a bug is hard to locate, code is not working as expected, an error or crash appears with unclear cause, a regression was introduced between recent commits, or you need to narrow down which component, function, or line is faulty. Covers binary search debugging, git bisect for regressions, strategic logging with [TRACE] patterns, data and control flow tracing, component isolation, minimal reproduction cases, conditional breakpoints, and watch expressions across TypeScript, SQL, and bash.

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Coach a UX practitioner through problem framing, running a design critique, or making an evidence-backed case to a skeptical stakeholder. Use when the practitioner has a live UX task and wants to think it through rather than receive an answer.

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Experiment design reference for problem-class framing, Minimum Viable Experiment coaching, hypothesis formation, vetting and red flags, and experiment readiness. Use when translating a stated business outcome into candidate data-science problem classes, or when framing, vetting, scoping, or evaluating an experiment of any kind, including data feasibility, architecture, LLM, performance, use-case, UX, prototyping, and hardware experiments.

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Use C#/.NET in Godot 4.7: partial classes extending nodes, the PascalCase lifecycle (_Ready/_Process/_PhysicsProcess), [Export] fields, [Signal] delegates as C# events, type-safe node lookup, and calling between C# and GDScript. Use when writing Godot game code in C# (.cs files, .csproj), needing the Godot .NET build, converting GDScript patterns to C#, or wiring Godot signals as C# events.

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Run /test to write a test suite for code you just built or changed, after implementing a feature, route, or fix. Targets uncommitted changes automatically, reads test preferences.json for your framework (asks and saves it if absent), and picks the right strategy per file: happy path, edge cases, error states, accessibility.

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结构化压力测试一个论点（devil's advocate / 魔鬼代言人）：把论点拆成显性断言与隐性假设（A1/A2 编号）， 逐条在用户给定的证据材料里检索带出处的反方论证，标 High/Medium/Low 风险等级，补基础比率外部视角， 最后给出「可监测的证伪信号」清单；输出结构化 findings + 人读叙事双层结果。找不到反证时如实声明「未找到」， 绝不编造。复刻自 LinqAlpha（对冲基金 AI 产品）公开的 Devil's Advocate 架构，叠加 CIA 结构化分析技术与 RAND 假设规划。当用户要「压力测试 / 唱反调 / 找盲点 / 拆假设 / 挑战这个论点 / bear case / 红队这个方案 / 上投委会前过一遍 / pressure test my thesis」，或拿着投资论点、IC memo、研究结论、商业决策要人挑刺时， 用本 skill——即使用户没说出"魔鬼代言人"这个词。

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Generates professional animated CLI demos as GIFs using VHS terminal recordings. Use to create terminal demos, record CLI workflows as GIFs, generate animated documentation, build demo tapes for README files, or showcase any command-line tool visually: record terminal / VHS tape / demo GIF / animate my CLI.

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Estimate economic compensation (经济补偿金) when employment ends in mainland China: N, N+1 or 2N under the Labour Contract Law, with the high-earner cap, the part-year rule and the questions that decide which case applies. Use when asked 我被裁员了能拿多少补偿, N+1 怎么算, 经济补偿金计算, 违法解除 2N, or how much severance am I owed in China. Produces the scenario that likely applies and why, the calculation shown step by step, what to check before signing anything, and where to get it confirmed. Not legal advice.

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Use when asked 医保怎么报销, 异地就医怎么备案, 住院能报多少, 门诊报销比例, 手工报销要什么材料, 惠民保值不值得买, or work out what China's basic medical insurance (医保) will pay for a treatment. Produces the person's scheme (职工医保 or 居民医保), how a claim works for this case (direct settlement or manual reimbursement), an out-of-pocket estimate built from the city's own deductible, rate and cap, the cross-region registration (异地就医备案) steps, and the documents to keep.

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Write a fair, enforceable community moderation policy. Use when standing up or overhauling moderation for a forum, Discord, Slack, subreddit, or any user community. Produces a clear code of conduct with examples, a graduated enforcement ladder tied to specific triggers, an appeals process, moderator guidelines, and the handling for the severe cases (threats, doxxing, brigading) that need immediate action. Governs member conduct in a user community — distinct from [[community-management-playbook]], which manages a brand's own social-media channels (comments, DMs, tone, response templates).

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Set up a chart of accounts and rules for categorizing transactions. Use when asked how to categorize expenses/transactions, set up a chart of accounts, organize bookkeeping, or sort bank transactions into the right buckets. Produces a practical chart of accounts for the business, categorization rules with examples and edge cases, and a clean-books routine — so the books are consistent and ready for an accountant. Not tax/accounting advice.

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Keep a running brag document of your accomplishments so reviews and promo cases write themselves. Use when asked to start or update a brag doc, log a win, track accomplishments, or prep evidence for a review/promotion. Produces a structured, dated accomplishment log — impact-first entries with metrics, scope, and the evidence link — grouped so it drops straight into a self-review or promo packet.

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Figure out which AI tool actually fits the task in front of you — chatbot, coding assistant, image model, agent, or none — instead of forcing one tool onto everything. Use when asked which AI tool should I use for, what's the best AI for, do I even need AI for this, or should I use ChatGPT or something else. Produces a match between your task and the right kind of AI tool (with why), the trade-offs that matter for your case, when the answer is a non-AI tool or plain human effort, and how to try it cheaply before committing — so you pick by fit, not by hype or habit.

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Run a blameless postmortem for an incident caused by an AI agent or LLM feature — hallucinated facts shipped to users, runaway tool use, prompt injection, cost blowouts, or wrong actions taken autonomously. Use when asked to write up an AI incident, analyse why an agent did something wrong, or produce corrective actions after an LLM failure. Produces a structured postmortem with trace reconstruction, a root-cause layer analysis, and corrective actions including a permanent regression case. For non-AI production incidents use incident-postmortem.

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Generate pytest tests for Typer CLI commands. Includes fixtures (temp_storage, sample_data), CliRunner patterns, confirmation handling (y/n/--force), and edge case coverage. Use when user asks to "write tests for", "test my CLI", "add test coverage", or any CLI + test request.

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Help users systematically document their impact, identify skill gaps, and collaborate with their managers to secure a promotion by proving they are already operating at the next level.

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Behavioral guardrails for Codex coding work based on common user complaints. Use when Codex is asked to implement, modify, debug, review, test, or operate on a codebase and should avoid unsafe scope expansion, stale edits, fake completion, brittle edits, shallow debugging, superficial patch-on fixes, one-off special-case code, short-term design choices that hurt maintainability, blindly following incorrect user assumptions, poor dependency choices, generic product or UI output, over-mocked tests, noisy approvals, verbose status reports, or leaking internal reasoning into project artifacts or user-facing UI.

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Creates an elegant, realistic Vinyl Record Player animation component for Remotion. Use when needing a music player UI, album showcase, or audio-visualizer interface in a video. (Keywords: 黑胶唱片, 音乐播放器, 唱片机, 专辑展示, 音频可视化)

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Eval and improve a skill against golden cases — run the target skill blind in a fresh, context-free subagent on each example input, grade the artifact against the expected outcome, and let the gaps drive the edits. Use when the user wants to test/eval/improve/harden a skill, says "this skill keeps producing X / keeps missing Y", or hands a skill plus example input→expected-output pairs. Pairs with [write-skills](../write-skills/SKILL.md) (the authoring principles every fix obeys).

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Writes product-led content pages optimized for both search engines and AI engine citations. Produces markdown files with frontmatter, following page-type frameworks (comparison, guide, use case, learn, trust) with verified sources and no fabricated data.

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Clean Architecture patterns for Android and Kotlin Multiplatform projects — module structure, dependency rules, UseCases, Repositories, and data layer patterns.

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Guide real-build acceptance: literal taps, real inputs, expected outcomes, worst case first, one sitting. Triggers: testflight, human pass, acceptance test, before we ship, what should I test, release checklist, device test.

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AI code quality checks. The Big 5: input validation, edge cases, error handling, duplication, complexity. Triggers: quality, big 5, ai code, review, validate.

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Anti-slop skill for PRODUCT UI - dashboards, data tables, forms, multi-step flows, settings, list/detail, app shells. The agent reads the surface, budgets the frame first, and ships dense interfaces that are correct at every edge case (overflow, long labels, empty/error/loading states, i18n expansion, keyboard/a11y). House-system-first; maps to Carbon/Polaris/Atlaskit/Fluent/Primer/Material3/Radix-shadcn/Ant. The counterpart to taste-skill, which owns landing/portfolio/marketing.

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