「cases」の検索結果

Runs one or more installed review lenses — adversarial critique, edge cases, verification gaps, structure, prose — and reports triaged findings. Use when, and only when, the user asks you to review a diff, a pull request, or an artifact — code or documents, one or many — and actually says "review"; an explicit skill:bmad-review directive from another skill counts as that ask. A request to act on feedback from an earlier review is a change, not a review. Never invoke this uninvited, including on edits you just made.

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Walk every branching path and boundary condition in content, report only unhandled edge cases. Orthogonal to adversarial review - method-driven not attitude-driven. Use when you need exhaustive edge-case analysis of code, specs, or diffs.

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Independent review loop run before delivery in code work. An independent reviewer (Codex, or fresh Codex-style subagents) reads the whole PR against the approved outcome; the lead fixes in-scope functional findings and re-reviews until none remain. Its differentiator is independence and exhaustiveness — a reviewer that fails differently from the authors, run to clean — distinct from the recursive-refinement ladder. Invoked by the team lead from the unified pre-ship gate (code-mode and /swarm:refine) after the ship steps.

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Advanced React Flow patterns for complex use cases. Use when implementing sub-flows, custom connection lines, programmatic layouts, drag-and-drop, undo/redo, or complex state synchronization.

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Plan and run manual tests of the project's running app. Runs a session in one of five modes: exploratory, verification, smoke, assessment (UX, localization, accessibility, improvement review), or AI probe. Also writes a manual test plan that holds only what automated tests cannot judge. Use whenever the user asks, in any language, for something to be tested by hand or for a manual test plan — "manual test", "exploratory test", "bug hunt", "verify the bug fix", "smoke test", "QA this screen", "review this screen's UX", "what would you improve", "check the localized copy", "test the AI agent's answers", "write manual test cases", "kịch bản test manual" — or assigns a test design, test plan or test-case IDs to execute by hand. Not for writing automated tests.

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Create high-quality, model-aware system prompts for any LLM (Claude, GPT, Gemini, open-source, etc.). Use this skill whenever the user wants to create, write, build, design, draft, or improve a system prompt, system instructions, or custom instructions for any AI model. Also trigger when the user asks about prompt engineering, prompt design, prompt optimization, or wants to define behavior for an AI assistant, chatbot, agent, or any LLM-powered application — even if they don't explicitly say "system prompt". Covers all use cases including chatbots, agentic systems, tool-use workflows, content generation, data extraction, code assistants, and multi-turn conversations.

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Interactive demo: Generate pseudo-labels from a strong teacher model, build a cheap student function, and evaluate accuracy. Showcases pseudo-labeling and teacher-student distillation.

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Guide creation of new industry use cases for the Adobe Experience Platform blueprints repository. Use this skill when adding a new use case to an industry vertical (retail, automotive, healthcare, financial services, insurance, media & entertainment, telecommunications, travel & hospitality, B2B), when the user wants to add business scenarios to industry pages, or when updating the use case catalog. Handles the full workflow: gathering use case details, assessing maturity level, generating content, updating the industry overview page and use case catalog, and invoking the use-case-icon-generator skill for icon creation.

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Generate dispute letters for consumer, landlord, labor, and insurance cases. Use when filing complaints, disputing charges, drafting grievances.

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Audits Solidity liquidation mechanisms for denial of service vulnerabilities including unbounded loops over positions causing out-of-gas reverts, data structure corruption preventing liquidation, front-running to block liquidation via nonce changes or self-liquidation, pending withdrawals forcing reverts, malicious ERC721/ERC20 callback reverts, collateral in external vaults not seized, insufficient insurance fund blocking liquidation, fixed bonus exceeding available collateral, incorrect decimal handling, conflicting nonReentrant modifiers, zero value transfer reverts, token deny list issues, and single borrower edge cases

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Audit existing mocks and test doubles — find contract drift, missing error cases, and stale fixtures. Use when asked to "audit our mocks", "find stale test fixtures", or "check for contract drift".

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Expert guide for creating high-converting application promotional media — App Store & Google Play screenshots, social promo graphics, dynamic Open Graph banners, Remotion programmatic video teasers, and AI promo showcases / Panduan ahli pembuatan media promosi aplikasi — mockup screenshot App Store & Google Play, kartu promo media sosial, banner Open Graph dinamis, video teaser terprogram Remotion, dan showcase promo berbasis AI.

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The logical structure of mathematical proof -- how first-order logic, natural deduction, and semantic reasoning combine to produce rigorous mathematical arguments. Covers the logical skeleton of direct proof, contraposition, contradiction, cases, and induction; the role of definitions, lemmas, and theorems; how informal mathematical prose maps to formal deductive structure; and the difference between proof-as-object and proof-as-activity. Use when the question is "why is this a valid proof?" rather than "what proof technique applies?"

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Proof writing and verification techniques for mathematical reasoning. Covers 13 proof strategies with canonical worked examples — direct, contrapositive, contradiction, cases, WLOG, vacuous/trivial, biconditional, counterexample, existence (constructive and non-constructive), uniqueness, induction (weak, strong, structural), pigeonhole/double-counting, and diagonal argument/invariants. Use when writing, verifying, or selecting proof strategies for any mathematical claim.

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Core programming concepts from variables through recursion. Covers data types (integers, floats, strings, booleans, arrays, objects), variables and scope (lexical, dynamic, block, function, global), control flow (conditionals, loops, pattern matching), functions (parameters, return values, closures, higher-order functions), recursion (base cases, call stack, tail recursion, mutual recursion), type systems (static vs dynamic, strong vs weak, type inference, generics), and error handling (exceptions, Result types, defensive programming). Use when teaching, reviewing, or diagnosing issues with fundamental programming constructs.

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。

Generates test cases for functions and components. Use when writing tests or creating test suites.

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。

Generates simulation inputs across fidelity levels — OpenFOAM CFD cases, ngspice netlists, interactive React artifacts, and FreeCAD FEM setups — from verified engineering calculations.

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 when analyzing failing test cases to determine whether failures indicate genuine bugs or test implementation issues. Activates on "analyze failing tests", "debug test failures", "investigate test errors", or when provided with specific failing test names or output. Applies balanced investigative reasoning — does not auto-fix tests without establishing root cause.

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。

Decision framework for choosing between regex and LLM when parsing structured text — start with regex, add LLM only for low-confidence edge cases.

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。

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

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。

Security audit and code review checklist. Covers 30+ vulnerability types with real-world exploit cases (2021-2026) and EVMbench Code4rena patterns. Use when conducting security audits, code reviews, or pre-deployment security assessments.

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Reads model uncertainty from token log-probabilities. Covers collecting logprobs and aggregating multi-token labels, confidence tiers and margins for triage, calibration assessment with ECE, Brier scores, and reliability diagrams against human labels, responsible downstream use of confidence, and reproducibility archives. Use when the user asks about classifier confidence by item, logprobs, top-k tokens, calibration, ECE, Brier, reliability diagrams, or routing low-confidence cases to human review. Codebook and validation design go to text-classification.

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。

Reads a model's own uncertainty from token log-probabilities: collecting and aggregating label logprobs, confidence tiers and margins for triage, calibration against human labels (ECE, Brier, reliability diagrams), downstream use, and reproducibility. Use for per-item classifier confidence or routing low-confidence cases to human review; codebook and validation design go to text-classification.

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。

Create and maintain AgentV YAML evaluation files for testing AI agent performance. Use this skill when creating new eval files, adding eval cases, or configuring custom evaluators (code validators or LLM judges) for agent testing workflows.

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。