「sse」の検索結果

Assemble a submission-ready replication package to the AEA Data and Code Availability Standard (DCAS) / openICPSR / Social Science Reproduction Platform expectations — standard replication README, dataset manifest, computational-requirements capture, a Table/Figure → script:line map, and a confidential-data deposit plan. Use when user says "build the replication package", "prepare the openICPSR deposit", "make the AEA data and code package", "DCAS compliance", "assemble the deposit for the journal", or after a paper is accepted and the journal's data editor needs the package. NOT a numeric verifier — it calls /audit-reproducibility to confirm claims reproduce before packaging.

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Create or revise Chinese social-media covers through the registered Skill-封面 asset system, reusable styles, presenter identities, ratio-specific layouts, and exact replacement prompts. Use only when the user explicitly says“skill-cover”“Skill-封面”“实战风格”, requests the registered style/gesture assets, asks for simultaneous 3:4 and 4:3 registered-layout output, replaces a reference cover's person with the current digital-human frame, or extends this registered cover system. Do not use for a generic“制作封面/生成封面”request; generic cover requests belong to dog-skill.

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dontbesilent 内容结构化系统。把本地大量文稿、推文、选题、案例和课程稿搭成一个可持续生长的内容结构化工程：先审计内容规模与边界，再建立新工程、复制素材、抽取内容单元、生成主题地图与选题装配稿。 触发方式：/dbs-content-system、/内容结构化系统、「把我的内容做成结构化系统」「把本地素材变成可重组系统」「帮我搭内容资产工程」「我想把旧内容变成可复用资产」 Content structuring system. Audits local content volume, then builds a reusable content knowledge project with units, topic maps, and assembly drafts. Trigger: /dbs-content-system, "build a content structuring system", "turn my archive into reusable assets"

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Creates and retrieves AI video and image-related assets via the Arcads external API (Seedance 2.0, Sora 2, Veo 3.1, Kling, Grok Video, Nano Banana, b-roll, scene, script/actor flows). Loads prompts from the bundled prompting guide and model library, respects HTTP Basic auth from ARCADS_API_KEY, and polls assets/videos until ready. Use when the user mentions Arcads, external-api.arcads.ai, Seedance, Sora2, Veo, Kling, Nano Banana, b-roll, UGC scripts, or generating marketing creative through Arcads.

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Assess and improve the reliability posture of PaaS Applications (Azure Functions and Azure App Service). Scans deployed resources for zone redundancy, ZRS storage, health probes, and multi-region failover. Presents a feature-pivoted checklist, then drives staged remediation (CLI or IaC patches) end-to-end with user confirmation. WHEN: "assess reliability", "check reliability", "zone redundant", "multi-region failover", "high availability", "disaster recovery", "single points of failure", "reliability posture", "resiliency".

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Assess Kubernetes workloads and cluster configuration for AKS Automatic compatibility. Identifies incompatibilities, generates fixes, and guides migration from AKS Standard to AKS Automatic. WHEN: migrate to AKS Automatic, check AKS Automatic readiness, validate manifests for Automatic, assess cluster for Automatic compatibility, fix deployment for Automatic compatibility, identify AKS Automatic migration blockers, is my cluster ready for AKS Automatic.

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Run Azure compliance and security audits with azqr plus Key Vault expiration checks. Covers best-practice assessment, resource review, policy/compliance validation, and security posture checks. WHEN: compliance scan, security audit, BEFORE running azqr (compliance cli tool), Azure best practices, Key Vault expiration check, expired certificates, expiring secrets, orphaned resources, compliance assessment.

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Applies proven testing patterns — Arrange-Act-Assert (AAA), Given-When-Then, Test Data Builders, Object Mother, parameterized tests, fixtures, spies, and test doubles — to help write maintainable, reliable, and readable test suites. Use when the user asks about writing unit tests, integration tests, or end-to-end tests; structuring test cases or test suites; applying TDD or BDD practices; working with mocks, stubs, spies, or fakes; improving test coverage or reducing flakiness; or needs guidance on test organization, naming conventions, or assertions in frameworks like Jest, Vitest, pytest, or similar.

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Build a PixiJS v8 render layer: create the async Application, load textures with Assets, compose the scene graph with Container and Sprite, drive the ticker loop, wire pointer events, and group draws with render groups. Use when building or debugging PixiJS v8 — when the user mentions PixiJS, Pixi, Application, app.stage, Container, Sprite, Assets.load, app.ticker, or eventMode. Pins the v8 async init() API.

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Build Unreal Engine 5 gameplay with Blueprint visual scripting: Blueprint Classes, the Event Graph and Construction Script, variables/functions/macros, and Blueprint communication (Cast, Interfaces, Event Dispatchers). Use when working in Blueprints, wiring an event graph, deciding how Blueprints talk to each other, or when the user mentions Blueprint, BP, event graph, construction script, or a Blueprint .uasset.

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Design data-driven Godot 4.7 games with custom Resource classes: define typed data with class_name + @export, save/load .tres/.res files, instance and duplicate resources, and load on demand with ResourceLoader (incl. threaded loading). Use when modeling items/stats/configs as data in a Godot project, creating .tres resources, or working with custom Resource subclasses and ResourceLoader/ResourceSaver.

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Google Maps scraper for lead generation. Turns a business type and a place into a clean CSV of local businesses by driving the gosom/google-maps-scraper engine, running locally in Docker as a REST API on localhost:8080. Returns 8 lead fields by default (name, phone, email, website, category, address, rating and review count), plus optional Instagram, Facebook and LinkedIn profiles or all ~34 raw fields. Use when the user wants a lead list, "a list of [businesses] in [place]", local business data, contact enrichment for places (emails, phones, socials), or a whole-city run past Google's ~120 results per search. Do not use for scraping Instagram, TikTok, YouTube, X or any other social media platform directly.

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This skill should be used when comparing two videos to analyze compression results or quality differences. Generates interactive HTML reports with quality metrics (PSNR, SSIM) and frame-by-frame visual comparisons. Triggers when users mention "compare videos", "video quality", "compression analysis", "before/after compression", or request quality assessment of compressed videos.

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Use when asked 等保怎么做, 等保 2.0 定级, 等保备案和测评流程, 等保三级要求, 我们的系统需要过等保吗, or prepare an information system for China's Multi-Level Protection Scheme (MLPS 2.0, 网络安全等级保护). Produces a grading recommendation (定级) with the reasoning for the expert review, the filing (备案) and assessment (测评) steps and their usual timing, a gap checklist across the ten control areas of GB/T 22239-2019, and a remediation plan, marked as preparation for a licensed assessor.

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Use when asked 个人信息保护影响评估怎么做, 写一份 PIA, 处理敏感个人信息要评估吗, 自动化决策评估, PIPL 第五十五条, or assess a processing activity under China's Personal Information Protection Law. Produces a personal information protection impact assessment (个人信息保护影响评估) covering the PIPL Article 55 triggers, the Article 56 questions (lawful, legitimate and necessary; impact and risk; adequacy of protection), a risk register with mitigations and owners, and a record kept for at least three years.

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Write a UK Civil Service or NHS application: behaviour statements in the Success Profiles format, a personal statement mapped to the essential criteria, and a CV in the form these schemes expect. Use when asked to write my civil service application, answer Success Profiles behaviours, write a 250-word behaviour example, write my NHS supporting information, or apply on Civil Service Jobs or NHS Jobs. Produces each behaviour statement within the word limit using a situation, task, action, result structure, a criteria-mapped personal statement, and a check against the advert's essential criteria.

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Conduct a structured ethical review of an AI or ML feature, model, or product. Use when preparing to deploy an AI system, assessing algorithmic risk, auditing a model for bias, or producing a responsible AI impact assessment. Produces a structured ethics review covering fairness, transparency, privacy, safety, accountability, and societal impact with a risk tier score, pre-deployment checklist, and prioritised mitigations.

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Search LCSC Electronics for electronic components — find parts by LCSC number (Cxxxxx) or MPN, check stock/pricing, download datasheets, analyze specifications. Sister company to JLCPCB, same parts library. Sync and maintain a local datasheets directory for a KiCad project, or use batch MPN-list seeding (`--mpn-list`) for bulk workflows without a project. No API key needed — uses the free jlcsearch community API. Use this skill when the user mentions LCSC, JLCPCB parts library, JLCPCB assembly parts, production sourcing, Cxxxxx part numbers, needs to find LCSC equivalents for parts, is preparing a BOM for JLCPCB assembly, or wants to download datasheets and LCSC is available. For package cross-reference tables and BOM workflow, see the `bom` skill.

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Systematic strategic planning framework for philosophy and interdisciplinary academic papers targeting preprint platforms (PhilArchive, arXiv, PhilSci-Archive). Use when users want to: (1) plan a paper on a specific topic, (2) identify research gaps and assess originality, (3) develop optimized paper outlines, (4) prepare for preprint submission, or (5) understand platform requirements and writing standards. Triggered by phrases like 'plan a paper on,' 'help me design a paper about,' 'identify research gaps in,' 'is this idea original,' or when users need structured research planning. The skill guides through three phases: Platform Analysis (identifying target venue and studying sample papers), Theoretical Framework (AI-driven literature search and gap identification), and Outline Optimization (structured design with reviewer-perspective self-assessment). Each phase includes quality evaluation standards and validation checkpoints. Output: optimized detailed outline ready for systematic writing (use with academic-paper-composer skill).

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Manage Cloudinary media: upload images and videos, list and inspect assets, update metadata, delete assets, and check plan usage. Trigger phrases: cloudinary, upload image, cloudinary usage.

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Gates whether a new test should exist and forces it to be efficient, protecting CI from low-value test bloat. Use before any change to what a pytest, Jest, or Playwright test asserts or sets up, down to one fixture or one assertion added to an existing block. Front-loads the value bar (every test must catch a realistic regression no existing test already catches; extend the nearest existing test before writing a new standalone one; test behavior through the public interface, not implementation details; collapse near-duplicates into parameterized cases) and the efficiency bar (deterministic, isolated, fast; pick the cheapest test level; Django TestCase over TransactionTestCase; no sleeps, no real network; no time bombs from absolute dates left to age against the real clock; no database a test never uses). Includes a "don't write it" decision tree. For fixing an existing flaky test use `/fixing-flaky-tests`; after this gate says a Playwright test is warranted, use `/playwright-test` for mechanics.

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。

Search LCSC Electronics for electronic components — find parts by LCSC number (Cxxxxx) or MPN, check stock/pricing, download datasheets, analyze specifications. Sister company to JLCPCB, same parts library. Sync and maintain a local datasheets directory for a KiCad project, or use batch MPN-list seeding (`--mpn-list`) for bulk workflows without a project. No API key needed — uses the free jlcsearch community API. Use this skill when the user mentions LCSC, JLCPCB parts library, JLCPCB assembly parts, production sourcing, Cxxxxx part numbers, needs to find LCSC equivalents for parts, is preparing a BOM for JLCPCB assembly, or wants to download datasheets and LCSC is available. For package cross-reference tables and BOM workflow, see the `bom` skill.

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Use for AI-assisted Unity work that needs live repo discovery, project-derived routing, runtime-owner proof, runtime-visible output hard stops, runtime numeric proof for repeated visible-output failures, state-step guards, multi-agent scope ownership, modular C#/asmdef safety, UI/scene/visual asset gates, data-first content changes, validation, cleanup proof, or durable workflow rules. Best when agents must prove the actual folder/module/scene/prefab/runtime owner before editing, especially runtime UI, generated assets, code graphs, tutorial/state flows, guided selection/action flows, overlay/dim source-bound mistakes, coordinate conversions, focus/highlight/marker/HUD alignment, predictive lead aiming, ballistic trajectories, spatial IoU verification, or repeated "fix still not visible" failures.

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Use when reviewing a DataFusion Comet pull request that crosses the JVM/native boundary, touching Arrow C Data or C Stream interface code, batch export and import, CometExecIterator, ScanExec, NativeUtil, CometVector subclasses, or jni_api. Load alongside review-comet-pr.

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。