'ept' 검색 결과

Document a technical decision in an architecture decision record, or ADR. Capture its context, alternatives, and consequences, assign a record number, and distinguish a proposed decision from an accepted one so dependent work reflects its actual status.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Recording the reasons behind a technical choice
• Documenting alternatives and consequences of a design decision
• Creating numbered records alongside existing architecture decisions

Break a game concept into the systems it needs, including supporting systems that are easy to overlook. Map dependencies, assign priorities by development stage, and create an index showing the recommended order for detailed design.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Identifying the systems a game concept requires
• Finding dependency cycles and bottleneck systems
• Choosing systems for the minimum playable version

Evaluate a machine learning GPU cluster during a cloud trial or acceptance test. Measure GPU computing performance, communication within and between machines, and local and shared storage, then compile the results into a dated Markdown report.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Evaluating trial GPU machines from a cloud provider
• Running acceptance tests for a GPU cluster
• Measuring GPU performance under load from neighboring GPUs

Extract web page content as Markdown from a single URL or a site crawl. When access is blocked, progressively switch from basic fetching to Chrome and Bright Data proxy services, which route requests through other IP addresses.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Converting a single web page to Markdown
• Extracting content from JavaScript-rendered pages
• Retrieving pages blocked by bot detection

Turn a feature idea into a specification through structured questions about users, business goals, and technical constraints. Produce requirements, user stories, testable acceptance criteria, and an implementation checklist, including performance, security, and error handling.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Defining requirements for a new feature
• Clarifying vague feature requests
• Writing testable acceptance criteria

Learn Apache Beam's core concepts through Java and Python examples. Understand distributed datasets, processing steps, pipeline execution, and time windows, with guidance on aggregation, late data, error handling, and sharing processing steps across languages.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Learn how Pipeline, PCollection, and PTransform fit together
• Understand grouping, aggregation, merging, and splitting
• Study time windows, output timing, and late data

Verify finished work against acceptance criteria using executable checks and a review of whether each requirement is met. Multiple models can review uncertain cases, and rejected work can enter an automated repair loop when the required Seed is available.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Checking finished work against its specification
• Verifying observed behavior against acceptance criteria
• Requesting multiple model reviews for uncertain results

Execute an existing Ouroboros Seed, a specification containing a goal and acceptance criteria, as a background job. The workflow checks each criterion, normally queues formal evaluation, and follows a bounded improvement loop if evaluation rejects the result.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Executing an existing Seed specification
• Checking whether each acceptance criterion is met
• Following execution, evaluation, and improvement results

Collect sold listings from eight eBay marketplaces and organize displayed prices, shipping costs, conditions, sale dates, and seller details. Filter results to research resale values and compare prices across markets, with accepted offers flagged separately.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Research resale prices for used products
• Compare sold prices across eBay marketplaces
• Collect sales records for collectibles

Learn programming concepts through a short definition, a minimal code example, and a practical use case. Explanations adapt to experience level, highlight common mistakes, and suggest related topics and a small practice exercise.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Learning React hooks from small examples
• Understanding how async/await works
• Exploring SQL joins in depth

Hunting skill for cache poison vulnerabilities. Built from 10 public bug bounty reports including X-Forwarded-Host poisoning, X-HTTP-Method-Override / GCS cache, reflected→stored XSS via cache, classic Omer-Gil Web Cache Deception, Cloudflare Cache Deception Armor bypass, session-token cache deception, Akamai hop-by-hop smuggling → server-side edge poisoning, and Kettle's 2024 path-normalization WCD against Cloudflare/Fastly/GCP. Host/X-Forwarded-Host injection that reaches app logic (reset-link poisoning, routing SSRF, OAuth issuer) is owned by hunt-host-header; this skill owns the case where the poisoned response is CACHED and served to other users. Use when hunting cache poisoning, Web Cache Deception, CDN-fronted apps.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Guide for running acceptance tests for a Terraform provider. Use this when asked to run an acceptance test or to run a test with the prefix `TestAcc`, when a test fails with missing environment variables, or when diagnosing a failing or suspiciously passing acceptance test.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Analyze images, video, speech, motion, products, and websites; route local vision, audio, depth, tracking, matting, identity, and restoration models; auto-edit, replicate, enhance, caption, voice, assemble, annotate, validate, and export editable Timeline Studio projects and videos. Use for reference-video remakes, filter and repeated-shot reconstruction, subject-aware reframing, clip splitting, source-time speed curves, Color Wheels grading, ramps and holds, person or product cutout, person or object outline, authorized face swap, shot and timing reconstruction, timeline markers, chapters, beat cues, review notes and ranges, supplied or web-sourced footage, AI video platform selection, cleanup, highlights, product promotion, website walkthroughs, image-to-video assisted edits, optical-flow editing, depth-assisted relighting, grayscale depth-video conversion, depth/2.5D/transition finishing, AI voiceover, subtitles, short-form production, .timeline automation, or editor evaluation.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Provides patterns for unit testing `@ExceptionHandler` and `@ControllerAdvice` in Spring Boot applications. Validates error response formatting, mocks exceptions, verifies HTTP status codes, tests field-level validation errors, and asserts custom error payloads. Use when writing Spring exception handler tests, REST API error tests, or mocking controller advice.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Captions living in 3D space around a talking head: a camera flies between the speaker and the words (whip-in, parallax truck, push), caption groups sit at different depths so camera moves pull them apart, hero words hide BEHIND the speaker through an alpha matte, a ring of words wraps round the speaker and turns in front of them, focus racks between depths, and text steps at 15 fps with ghost motion blur. Works in any type style (editorial serif, bold sans) or hand-drawn (p5.brush write-on). Trigger on: "3D text", "3D camera captions", "text around / behind the speaker", "camera moves through the text", "depth captions on my avatar video". Covers prep (portion, padded plate, matte, word clock, font metrics), the shot grammar, depth rules, QC, and limits.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

现代建筑设计 (从 concept 草图到施工图与现场服务的整套设计流程, 含 BIM / 可持续 / 参数化 / 在地性 模块, 全球语境 + 中国本土实践并重) (modern architecture design — 当代建筑设计 (concept → schematic → DD → CD → CA + sustainability / BIM / parametric / 中外双语 canon, 不含纯室内 / 景观 / 城市规划 / 结构 / MEP, 那些是相邻专业)) Master OS — automated mastery of modern architecture design — 当代建筑设计 (concept → schematic → DD → CD → CA + sustainability / BIM / parametric / 中外双语 canon, 不含纯室内 / 景观 / 城市规划 / 结构 / MEP, 那些是相邻专业): top builders' mental models, tool stack, current workflows, jargon, and where to keep up. Trigger this skill when the user works on modern architecture design — 当代建筑设计 (concept → schematic → DD → CD → CA + sustainability / BIM / parametric / 中外双语 canon, 不含纯室内 / 景观 / 城市规划 / 结构 / MEP, 那些是相邻专业) problems and wants industry-grade thinking, tool selection, or workflow guidance. 触发词：「建筑设计」「建筑师」「建筑事务所」「设计院」「方案设计」

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Acceptance Orchestrator workflow skill. Use this skill when the user needs a coding task should be driven end-to-end from issue intake through implementation, review, deployment, and acceptance verification with minimal human re-intervention and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use when the user wants to pressure-test a product, internal-tool, or OSS concept against Amazon's Working Backwards PRFAQ gauntlet before committing to a spec. Triggers on: "work backwards", "write a PRFAQ", "press release first", "is this idea worth building", "pressure-test this concept", "filter this before brainstorm", "is this a real product". Also catches solution-first pitches ("I want to build X that does Y") and technology-first pitches ("use AI to...") that need customer-first filtering. Produces a binary pass/fail verdict, not a polished doc. Hardcore coaching — direct, skeptical, concrete. On pass, hands off to brainstorm-beagle with a concept brief. Does NOT write code, plan implementation, scaffold projects, or draft specs.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

AI-PLCの進行中に、ユーザーの「ここをこうしたい」を命令ではなく相談として受け、今のLayer（intent・backlog）と照らして「却下／今のタスク内で修正／Re-Inception／Re-Collection／いつかやる」に振り分け、所感と次に打つコマンドを返す（読み取り専用・実行しない）。「どう思う？」「これやるべき？」「Inceptionからやり直すべき？」「次どのコマンド打てばいい？」で使う。

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Intercepts and analyzes HTTP/HTTPS traffic from mobile applications using Burp Suite proxy to identify insecure API communications, authentication flaws, data leakage, and server-side vulnerabilities. Use when performing mobile application penetration testing, assessing API security, or evaluating client-server communication patterns. Activates for requests involving mobile traffic interception, Burp Suite mobile proxy, API security testing, or mobile HTTPS analysis.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

"Apply the Technology Acceptance Model (Davis, 1989) and Unified Theory of Acceptance and Use of Technology (Venkatesh et al., 2003) to predict technology adoption. Use this skill when the user needs to evaluate user acceptance of a new system, diagnose adoption barriers, design interventions to improve technology uptake, or when they ask 'why aren't users adopting this', 'what drives technology acceptance', or 'how do we increase adoption rates'.".

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use when a task needs the judgment of a Dredge Operator — selecting cutterhead, hopper, or mechanical dredge type for a given sediment/depth/environmental profile, classifying dredged material for open-water vs. confined disposal from sediment-testing results, tuning hydraulic slurry density against pipeline critical velocity, verifying channel controlling depth against project depth and overdepth tolerance, or setting turbidity-monitoring response during active dredging.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Write measurable acceptance criteria for requirements or user stories. Use when the user says "write acceptance criteria", "definition of done", "how do we test this", "fit criteria", "given when then", "Gherkin scenarios", "what does done look like", "testable conditions", "how will we know this works" - even if they don't explicitly say "acceptance criteria".

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use this skill when authoring INVEST-compliant Salesforce user stories from gathered requirements: shaping the As-A/I-Want/So-That stem, writing Given-When-Then acceptance criteria, sizing complexity (S/M/L/XL), splitting stories that are too large for a sprint, and emitting handoff metadata that names the next downstream agent. Trigger keywords: user story, INVEST, story splitting, story sizing, As-A I-Want So-That, story handoff, recommended_agents, backlog item shape. NOT for requirements elicitation or stakeholder interviews (use admin/requirements-gathering-for-sf). NOT for the Given-When-Then technique itself in depth (use admin/acceptance-criteria-given-when-then). NOT for UAT test case design (use admin/uat-test-case-design). NOT for backlog prioritization (use admin/moscow-prioritization-for-sf-backlog).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.