「case」の検索結果

Operating-system distillation of LlamaIndex — the leading RAG / document-agent framework. Activate when the calling agent must build, debug, harden, or evaluate a Retrieval-Augmented Generation pipeline over unstructured/private data, decide between RAG primitives (Index types, retrievers, query engines, routers, agents), or pick LlamaIndex vs LangChain / Haystack / raw vector store for a coding task. Encodes the 5-layer mental model (Documents → Nodes → Indices → Retrievers → Query Engines / Response Synthesizers), the canonical RAG bootstrap SOP from baseline `VectorStoreIndex` through hybrid + reranker + eval-loop hardening, the official 13-failure-mode checklist, and 5 dilemma cases distilled from docs, GitHub issues, and 2025 production post-mortems.

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。

Engineer and audit SIEM detection rules — log source coverage, Sigma / KQL / SPL / Elastic query authoring, MITRE ATT&CK mapping, false-positive tuning, and detection-as-code workflows. Use when the user mentions 'SIEM,' 'detection engineering,' 'detection rules,' 'Sigma,' 'KQL,' 'SPL,' 'Splunk,' 'Sentinel,' 'Elastic,' 'Wazuh,' 'Chronicle,' 'detection-as-code,' 'MITRE ATT&CK mapping,' 'log coverage,' 'alert tuning,' 'use case development,' or needs help building or improving security detections.

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。

Generate SaaS product launch and software demo video prompts for Seedance 2.0 on Higgsfield. Use whenever the user wants a product launch video, app demo, software walkthrough, feature showcase, startup promo, tech product reveal, or any SaaS/software marketing video. Triggers on SaaS, app, software, product launch, demo, feature, startup, tech, UI, dashboard, landing page video.

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。

Generate luxury and premium aesthetic video prompts for Seedance 2.0 on Higgsfield. Use for luxury brand content, premium product showcases, high-end lifestyle, minimalist aesthetic, elegant brand videos, or any content requiring sophisticated visual treatment. Triggers on luxury, premium, high-end, elegant, minimalist, sophisticated, exclusive, refined, designer, couture, bespoke.

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。

Execute multi-level exploratory testing of the app covering basic functionality, complex operations, adversarial testing, and cross-cutting scenarios, plus usability observations through a UX lens reported separately from defects. Deeper than $smoke-test. Use when the user asks to "exploratory test", "test thoroughly", "test all scenarios", "deep test", "test edge cases", "test everything", "break it", "find bugs by testing", "test usability", or "check the UX while testing".

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。

Analyze what changed and generate a structured test plan at .turbo/test-plans/<slug>.md covering four escalating levels: basic functionality, complex operations, adversarial testing, and cross-cutting scenarios. Use when the user asks to "create a test plan", "plan tests", "what should I test", "generate test scenarios", "test plan for this PR", or "what are the test 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。

Execute multi-level exploratory testing of the app covering basic functionality, complex operations, adversarial testing, and cross-cutting scenarios, plus usability observations through a UX lens reported separately from defects. Deeper than /smoke-test. Use when the user asks to "exploratory test", "test thoroughly", "test all scenarios", "deep test", "test edge cases", "test everything", "break it", "find bugs by testing", "test usability", or "check the UX while testing".

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 this skill when stuck in circular debugging, when solutions aren't working despite multiple attempts, or when the user expresses frustration with lack of progress. Bring in GPT-5 as a third-party consultant to provide fresh perspective on complex technical problems, architectural decisions, or multi-system debugging issues. Ideal when you've tried multiple approaches without success, when the problem involves obscure edge cases or novel challenges, or when a second expert opinion would help break through an impasse. Gather all context from the conversation so far and present it comprehensively to GPT-5.

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 the user explicitly asks to commit and push the current repository changes. Suitable for analyzing current changes, generating an English commit message that follows the repo's conventions, and completing the push. Not suitable for status-only checks, local-only commits, push-disallowed contexts, or any case where the user has not explicitly asked to write to the remote.

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。

State-first design pass — inventories and implements all non-happy states (loading, empty, error, partial, conflict, offline) before the happy path, and refactors impossible boolean state to proper state machines. Use when starting a new screen, reviewing an existing one for edge-case gaps, or when the user says "handle the error state" / "add loading states" / "what happens when data is missing". Invoke when the user asks for unhappy on their UI, or mentions 'unhappy' alongside design / UI / frontend work.

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 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.

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。

PNAS figure preparation: resolution (300-1000 PPI), formats (TIFF/EPS/PDF), strict RGB-only color, Arial/Helvetica fonts, italicized uppercase panel labels, automated image screening.

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。

Cell (Cell Press) figure preparation: resolution (300-1000 DPI), formats (TIFF/PDF), RGB color, Avenir/Arial fonts, uppercase panel labels, strict image manipulation policies.

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。

Nature figure preparation: resolution (300+ DPI), formats (AI/EPS/TIFF), RGB color, Helvetica/Arial fonts, lowercase panel labels, image integrity requirements.

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。

Scaffold a new SciAgent-Skills entry. Picks pipeline/toolkit/database/guide template, creates skills/{category}/{name}/SKILL.md with valid frontmatter, appends the registry.yaml entry, runs validation. Enforces name uniqueness, kebab-case, description keyword rules, schema rules from AGENTS.md. TRIGGER when user says (any language): "add a SciAgent skill", "add a skill for <X>", "create new skill", "create a SKILL.md for <X>", "scaffold a skill", "new skill entry", "register a skill", "신규 skill 추가", "스킬 만들어줘", "스킬 생성", "skill 만들어", or any request to add a new SKILL.md to this repo. ALWAYS invoke this skill BEFORE writing to skills/ or registry.yaml. DO NOT TRIGGER when: editing existing entry's content (just edit the file directly); migrating an existing entry (read AGENTS.md "Migrating from Existing Entries" first); only updating registry.yaml without creating a new SKILL.md.

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。

Provides patterns for testing utility classes, static methods, and helper functions. Validates pure functions, null handling, edge cases, and boundary conditions. Generates AssertJ assertions and @ParameterizedTest for string utils, math utils, validators, and collection helpers. Use when testing utils, test helpers, helper functions, static methods, or verifying utility code correctness.

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。

Provides parameterized testing patterns with JUnit 5, generates data-driven unit tests using @ParameterizedTest, @ValueSource, @CsvSource, @MethodSource. Creates tests that run the same logic with multiple input values. Use when writing data-driven Java tests, multiple test cases from single method, or boundary value analysis.

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。

Provides implementation patterns for Clean Architecture, Domain-Driven Design (DDD), and Hexagonal Architecture (Ports & Adapters) in NestJS/TypeScript applications. Use when structuring complex backend systems, designing domain layers with entities/value objects/aggregates, implementing ports and adapters, creating use cases, or refactoring from anemic models to rich domain models with dependency inversion.

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。

Keep a running implementation-notes.md during a build, logging every deviation from the plan and every discovered edge case. Use whenever implementing against an agreed plan or spec, especially in long autonomous sessions.

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。

Weighted pipeline forecast by probability. Historical accuracy tracking, commit vs best-case scenarios, deal slippage 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。

Debug regex patterns with visual breakdowns, plain English explanations, test case generation, and flavor conversion. Use when user needs help with regular expressions or pattern matching.

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。

Calculate comprehensive ROI for AI implementation projects. Takes current costs, manual process time, team size, and hourly rates. Generates detailed roi-analysis.md with executive summary, cost-benefit tables, sensitivity analysis, break-even timeline, and comparison scenarios. Use when evaluating AI investments, building business cases, or justifying automation spend.

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。

Turn real customer reviews, quotes, and case-study results into polished social-proof videos built in HyperFrames -- single-testimonial spotlights, review-wall montages, and stat-driven proof reels with captions and voiceover, cut for vertical, square, and wide. The highest-converting content most businesses never make.

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。

Construct statistical arguments for MVP/awards. Narrative framing, comparison to past winners, advanced metrics, counter-arguments.

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。