「eda」の検索結果

Use when a redacted, trimmed agent transcript must be appended to a GitHub PR or issue body, with human approval and preview. Not for automated or model-initiated insertion.

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Use when filing an approved task spec as a GitHub issue with a local archive and optional worktree implementer. Don't use for spec drafting without human interrogation and redaction gates.

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。

Build and deploy full-stack web and mobile apps with AWS Amplify Gen2 (TypeScript code-first). Covers auth (Cognito), data (AppSync/DynamoDB including schema modeling, enum types, relationships, authorization rules), storage (S3), functions, APIs, and AI (Amplify AI Kit with Bedrock). Supports React, Next.js, Vue, Angular, React Native, Flutter, Swift, and Android. Always use this skill for Amplify Gen2 topics — even for questions you think you know — it contains validated, version-specific patterns that prevent common mistakes. TRIGGER when: user mentions Amplify Gen2; project has amplify/ directory or amplify_outputs; code imports @aws-amplify packages; user asks about defineBackend, defineAuth, defineData, defineStorage, or npx ampx. SKIP: Amplify Gen1 (amplify CLI v6), standalone SAM/CDK without Amplify (use aws-serverless), direct Bedrock without Amplify AI Kit (use bedrock).

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ES — Critica a fondo una idea, plan, pitch o proyecto entero sin validar ni adular: asume que va a fracasar y lo demuestra por ocho ángulos (premisas, mercado, competencia, viabilidad, números, ejecución, pre-mortem y punto ciego), investiga fracasos reales cuando hay herramientas, y entrega un veredicto franco con la lista priorizada de qué arreglar. Activar con /abogado-del-diablo, "hazme pedazos esto", "critica mi idea sin filtros", "dime por qué va a fallar", "destruye este plan", "segunda opinión brutal", "pre-mortem". EN — Harshly critiques an idea, plan, pitch, or whole project without validating; assumes it will fail and proves it across eight angles, then returns a blunt verdict with a prioritized fix list. Trigger on "devil's advocate", "red team this", "tear this apart", "why will this fail", "brutal second opinion", "pre-mortem".

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Use for ANY bug bounty, penetration test, or web/API/mobile security assessment: recon, testing endpoints, analyzing Burp traffic or any bug-bounty platform's MCP (HackerOne, Intigriti, Bugcrowd, YesWeHack, Immunefi), reviewing APKs, bypassing a WAF, enforcing scope, hunting a specific vuln class, validating findings, writing reports, or when you are stuck on a target and about to give up. Activates automatically for all bug-bounty and pentest sessions. Runs an autonomous, never-surrender predator methodology: refuses to conclude "secure", exhausts each vuln class to depth, proves a real attacker-path with impact and no false positives, tracks coverage so nothing is re-tested or skipped, diagnoses failed tooling from full logs, and hunts at maximum effort with intelligent token use until the goal is found. Activates and follows instructions in ANY language the operator writes in (the skill itself is English). Bundles a per-class web-exploitation KB, a writeup library, and PayloadsAllTheThings.

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Explore Solana's architecture and protocol internals. Covers the SVM execution engine, account model, consensus, transactions, validator economics, data layer, development tooling, and token extensions using the Helius blog, SIMDs, and Agave/Firedancer source code.

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Use when the user explicitly asks for the Inngest REST API v2, raw HTTP, OpenAPI, API docs, API authentication, or an endpoint that the Inngest CLI does not expose. Covers api-docs.inngest.com, llms.txt, the OpenAPI v2 spec, Bearer authentication with API keys or signing keys, production and local base URLs, raw curl/fetch requests, request-shape discovery, pagination, secret redaction, and when to prefer the `inngest-api-cli` skill instead.

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数据科学工程师（赛奇 Sage）。使用 Python 进行数据清洗、探索性数据分析（EDA）、统计建模、机器学习、特征工程与模型评估。输出可复现的分析结论与代码。 触发词：由 copilot-team-lead 主理人调度执行，不直接面向用户。当主理人下发 Phase 1 文件分析或 Phase 2 深度分析任务时激活。

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Use when the user asks about Iranian state-sponsored cyber operations or specific IRGC/MOIS-aligned actors (APT35/Charming Kitten, APT34/OilRig, MuddyWater, Imperial Kitten, etc.), wiper campaigns, front-group hacktivist personas (Handala, Cyber Av3ngers), or anti-Iran operations such as Predatory Sparrow. Self-updating knowledge cell.

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Mosaic environment-sensitive areas (tenant name, user avatar, subscription name / ID, resource group, internal hostnames) on Azure Portal screenshots before publishing to blogs, Qiita, Medium, slides, or demos. Use when sanitizing screenshots, masking env info, or preparing portal captures for public sharing. Keywords: Azure Portal, screenshot, mask, mosaic, sanitize, tenant, subscription, public publishing, blur, redact.

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Observe the user's screen via screenpipe, detect repeated research workflows, match them against existing scientific-agent-skills, and draft new skills (or composition recipes that chain existing ones) for the patterns not yet covered. Use when the user asks to analyze their recent work and propose skills based on what they actually do. Requires the screenpipe daemon (https://github.com/screenpipe/screenpipe) running locally on port 3030 — the skill has no other data source and will refuse to run if screenpipe is unreachable. All detection runs locally; only redacted cluster summaries reach the LLM.

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Audits whether a machine-readable regelrecht law model is faithful to the LETTER of the wettekst — and strictly separates the wettekst (leading) from the toelichting (Nota/Memorie van Toelichting; explanatory, not a norm). Runs in critical passes per article/lid and detects four deviation classes: toelichting-bleed (a modeled criterion that the letter only states as an open norm, or not at all), missing verbatim elements (a word/condition such as a "tenzij" or a qualifier dropped from the model), wrong legal_basis anchoring (an endpoint hung on a "kapstok"-article while the operative norm lives elsewhere), and verbatim-drift in text: (an ingetrokken/older redaction). Also detects positief-lid vs uitsluitings-lid conflicts and checks the chapeau for an explicit derogation rule ("zo nodig in afwijking van het eerste lid") before judging precedence. Classifies every finding as modelfout (fix), wettekst-gevolg (report, do not fix), or letter-vs-toelichting-question (jurist decides what is leading + revision signal). Use when the user wants to know if a model is "dicht bij de letter", mentions letter vs toelichting/bedoeling, fidelity, "natuurgetrouw", or before trusting feature outcomes.

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Extract sensitive training data (PII, API keys, intellectual property, or code) directly from a deployed Large Language Model (LLM). This AI Red Teaming skill focuses on forcing models to regurgitate memorized, unredacted data from their massive internet-scraped datasets through repetition attacks, prefix continuation, and context window manipulation.

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。

Active Directory ACL abuse playbook. Use when exploiting misconfigured AD permissions including GenericAll, WriteDACL, DCSync rights, shadow credentials, LAPS reading, GPO abuse, and BloodHound-guided attack paths.

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Clones a VIDEO ad end to end for a different product or offer, on the Novoads API. The deliverable is a finished rendered MP4 — the WHOLE ad, not its opening, and not a template. Reads the source's style, pacing, camera work, dialogue and tone, adapts it into a Seedance 2.0 prompt, prices it, renders it, files the source beside the clones and diffs each clip's transcript against the approved script. Use for "clone this video ad", "recreate this video for my brand", "make this ad but for my product" when the source is a video, "clone my competitors' VIDEO ads", or a video ad handed over with a product photo. **Among cloners the source's medium decides, not the wording**: "clone this ad" plus a video lands here; a still, or nothing attached, is clone-image-ad, and animating a still as-is is image-to-motion. This pack has no hook-only skill: for the opening alone, clone the whole ad and cut. If it does not move, wrong skill.

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Use when a task needs the judgment of an Environmental Compliance Inspector — reviewing discharge monitoring reports or emissions data against permit limits, classifying a facility's hazardous waste generator status, calculating a penalty for a documented violation, deciding whether an exceedance warrants a Notice of Violation versus informal correction, or scoping a multimedia (air/water/waste) inspection.

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 a task needs the judgment of an ICU/critical care RN — titrating a vasoactive drip within an ordered range and deciding when to escalate to a second agent, coordinating daily sedation-awakening and spontaneous-breathing trials on a ventilated patient, interpreting a hemodynamic/lactate trend against a resuscitation target, or working the ABCDEF bundle to prevent post-ICU complications.

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 a task needs the judgment of a Computer Hardware Engineer — designing a power delivery network and selecting decoupling capacitors for a digital IC's core rail, budgeting a high-speed signal's rise time against trace impedance and via stub length, closing static timing on a board-level DDR or SerDes interface, sizing thermal resistance for a package's TDP, or reviewing a PCB layout for signal- or power-integrity risk before fab release.

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 a task needs the judgment of an Acute Care Nurse — tracking early warning score trends across a higher patient ratio than ICU to catch silent deterioration before crisis, distinguishing expected post-operative recovery variation from a true complication requiring escalation, triaging competing needs across multiple simultaneous patients, balancing analgesia against oversedation risk by real-time response rather than fixed dosing, and verifying genuine discharge readiness rather than checking a form.

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。

Measuring emotion from behavior and physiology — facial expression, voice, EDA, and self-report integration.

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。

Audit adaptive learning and student personalization systems for pedagogical quality. Triggers: you need to evaluate learning path algorithms (branching, remediation, acceleration).

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Judges whether a user's input suits the Vibe Creating style of video-prompt writing, and when it does, distills single-scene prompts, multi-shot descriptions, emotional imagery, or mixed input into prompts that are easier for a video model to generate from — while preserving any user-specified dialogue, voiceover, music, sound effects, and other hard constraints. Use when a user wants to turn an idea, story, feeling, or rough/over-specified prompt into a strong text-to-video prompt (Seedance, Sora, Kling, Veo, Runway, etc.), or asks to "rewrite", "improve", "clean up", or "vibe-ify" a video prompt. Do NOT use for long narrative films that need precise word-for-word dialogue sync, industrial shot lists meant to be executed verbatim, or functional/UI demos and step-by-step tutorials.

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 provides a plot summary, scene idea, character relationship, emotional beat, or short video concept and wants a cinematic AI video prompt. First diagnose the story, then rewrite it into a model-ready prompt for Kling, Seedance, Veo, Sora, Runway, Jimeng, or general AI video models.

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。

為使用者產生高品質的 AI 生圖、生影片、生音樂提示詞，並在需要時透過瀏覽器自動化實際送到目標平台。涵蓋 OiiOii、Kling 3.0/O-series、Seedance 2.0/2.5、Suno v5.5、Seedream 5.0/4.0、Vidu Q3、Midjourney V8.1、Flux 1.1 Pro / Kontext、Runway Gen-4.5 / Aleph、Google Veo 3.1、Ideogram 3、Nano Banana Pro、Stable Diffusion 3.5（⚠️ OpenAI Sora 2 已於 2026-04-26 停運，API 撐到 2026-09-24，預設改推 Runway/Veo/Kling）。只要使用者提到「AI 生圖」「AI 影片」「AI 音樂」「做 MV」「做 storyboard」「寫 prompt 給 XXX」「我想用 Kling/Suno/Midjourney/Runway/Veo...」「幫我操作 OiiOii / 即夢 / 可靈」「txt2img / img2video / 文生圖 / 文生影片 / 圖生影片」「角色一致性」「多鏡頭分鏡」「運鏡」「結果有瑕疵 / 不夠精緻 / 怎麼修」，或任何跟上述平台或影像/影片/音樂生成工作流相關的任務，都要用這個 skill。即使他們沒講明平台，只要任務是要餵給某個生成模型的 prompt，就用這個 skill 幫他們選對的平台、寫對的格式。

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。