Emily2040

This skill should be used when creating, improving, or troubleshooting Seedance 2.0 video on any surface - Dreamina, Jimeng, CapCut, Doubao, Volcengine/Ark, BytePlus, Runway's Seedance route, fal, or third-party provider/router surfaces such as EvoLink,…

This skill should be used when the user asks for a compact Seedance 2.0 prompt, short Chinese prompt, prompt compression, 30-100 word output, or removal of unnecessary prompt language.

This skill should be used when the user asks to write, improve, translate, compress, or debug a Seedance 2.0 video prompt; mentions T2V, I2V, V2V, R2V, camera direction, prompt quality, or provides reference assets for a production-ready prompt.

This skill should be used when a Seedance 2.0 prompt is blocked or rejected, when moderation is a suspected cause of a problem, or when the user asks for a content-boundary review or safer alternative. Assess the actual request before offering a clarification.

This skill should be used when the user wants a fast Seedance 2.0 creative brief, a short interview, a compressed intake flow, or a quick director-style clarification before prompt writing.

This skill should be used when a Seedance 2.0 prompt contains generic AI filler, hollow superlatives, vague cinematic language, bloated adjectives, weak verbs, or needs sharper production-specific wording.

This skill should be used when an English Seedance 2.0 prompt needs clearer production wording, less generic prose, or precise vocabulary for camera, lighting, motion, VFX, audio, and constraints. Route blocked prompts through seedance-filter for context and…

This skill should be used when the user asks for Spanish Seedance 2.0 prompt wording, Spanish cinematic vocabulary, or translation of camera, lighting, action, VFX, audio, and production terms into Spanish.

Assess whether a dataset or data domain is ready for AI, analytics, or modeling before committing project budget. Use when triaging table schemas, missingness, label coverage, ownership, freshness, and PII risk to issue a go, fix-first, or stop decision.

Design honest naive, rule-based, and linear baselines alongside feature and component ablations before accepting complex ML models. Use when challenging model complexity against latency, cost, seed stability, and minimum required lift.

Audit whether a data science claim requires causal identification rather than predictive correlation. Use when mapping treatments, outcomes, confounders, colliders, post-treatment controls, and quasi-experimental designs before claiming impact.

Estimate and gate GPU, cloud, and local compute budgets for data science experiments, training runs, and batch or online inference. Use when sizing 10% pilot stages, retry overhead, spot risk, and stop-loss thresholds before launching full-scale runs.

Translate ambiguous business, product, policy, or research asks into measurable data science decision canvases. Use when locking down the decision owner, action threshold, analysis unit, target definition, time horizon, and guardrail metrics.

Run root-cause analysis for broken ML models, metric drops, pipeline failures, and corrupted dashboards. Use when correlating anomaly timestamps across upstream data, schema changes, code deploys, and serving layers to produce a countermeasure memo.

Infer explicit data contracts, primary-key uniqueness rules, value ranges, and freshness SLAs from messy CSV, TSV, or warehouse extracts. Use when turning undocumented upstream tables into enforceable schema and validation tests.

Structure subject-matter expert (SME) annotation, rubric calibration, and edge-case adjudication for data science workflows. Use when measuring inter-rater agreement (Cohen's kappa), resolving label ambiguity, and encoding domain constraints.

Deep verification for work one pass cannot check: audits and reviews of code, documents or data, changes across many files, research reports that reconcile many sources, builds and debugging that must run, and any request to verify thoroughly. Everyday…

一度の確認では済まない作業を、掘り下げて検証するためのスキルです。対象は、コード・ドキュメント・データの監査やレビュー、多数のファイルにまたがる変更、多くの情報源を突き合わせる調査レポート、実際に動かす必要があるビルドやデバッグのほか、徹底した検証を求める依頼全般です。日常的な質問やアドバイス、短い文章の作成はこのスキルの対象外で、プラグインの常時適用ルールに従って対応します。トリガー語（言語を問わない）：审计 调试 彻底验证, 감사 디버깅 철저한 검증, 監査 デバッグ 徹底的な検証。

한 번 검토해서는 확인할 수 없는 작업을 깊이 있게 검증하는 스킬입니다. 코드·문서·데이터 감사와 리뷰, 여러 파일에 걸친 변경, 많은 출처를 서로 대조해 맞춰 보는 리서치 보고서, 직접 실행해 봐야 하는 빌드와 디버깅, 그리고 철저한 검증을 요청하는 모든 경우가 여기에 해당합니다. 일상적인 질문이나 조언, 짧은 글쓰기 요청은 이 스킬이 아니라 플러그인의 상시 적용 규칙(always-on rule)에 따라 처리합니다. 어떤 언어로 요청해도 트리거됩니다(예:…

对单轮检查无法核实的工作做深度验证：代码、文档或数据的审计与审查；跨越大量文件的改动；需要比对众多来源、理清彼此出入的调研报告；必须实际运行的构建与调试；以及任何要求彻底验证的请求。日常提问、建议和简短写作不使用本技能，改按插件的常驻规则处理。任何语言的请求均可触发：审计 调试 彻底验证；감사 디버깅 철저한 검증；監査 デバッグ 徹底的な検証。

一度の確認では済まない作業を、掘り下げて検証するためのスキルです。対象は、コード・ドキュメント・データの監査やレビュー、多数のファイルにまたがる変更、多くの情報源を突き合わせる調査レポート、実際に動かす必要があるビルドやデバッグのほか、徹底した検証を求める依頼全般です。日常的な質問やアドバイス、短い文章の作成はこのスキルの対象外で、プラグインの常時適用ルールに従って対応します。トリガー語（言語を問わない）：审计 调试 彻底验证, 감사 디버깅 철저한 검증, 監査 デバッグ 徹底的な検証。

한 번 검토해서는 확인할 수 없는 작업을 깊이 있게 검증하는 스킬입니다. 코드·문서·데이터 감사와 리뷰, 여러 파일에 걸친 변경, 많은 출처를 서로 대조해 맞춰 보는 리서치 보고서, 직접 실행해 봐야 하는 빌드와 디버깅, 그리고 철저한 검증을 요청하는 모든 경우가 여기에 해당합니다. 일상적인 질문이나 조언, 짧은 글쓰기 요청은 이 스킬이 아니라 플러그인의 상시 적용 규칙(always-on rule)에 따라 처리합니다. 어떤 언어로 요청해도 트리거됩니다(예:…

对单轮检查无法核实的工作做深度验证：代码、文档或数据的审计与审查；跨越大量文件的改动；需要比对众多来源、理清彼此出入的调研报告；必须实际运行的构建与调试；以及任何要求彻底验证的请求。日常提问、建议和简短写作不使用本技能，改按插件的常驻规则处理。任何语言的请求均可触发：审计 调试 彻底验证；감사 디버깅 철저한 검증；監査 デバッグ 徹底的な検証。

Use for WebGL2 renderer architecture, GLSL ES 3.00 shader review, GPU profiling, FBO/postprocess pipeline design, context-loss debugging, DPR/fill-rate budgeting, visual regression, and WebGL-to-WebGPU migration (支持中文 WebGL2 渲染架构与着色器优化 / 日本語 WebGL2 設計・最適化 /…

WebGL 2.0 レンダラー設計、GLSL ES 3.00 シェーダーレビュー、GPU プロファイリング、FBO/ポストプロセスパイプライン設計、コンテキストロスト対策、DPR/フィルレート最適化、視覚回帰テスト、WebGL から WebGPU への移行計画のためのモジュラー型スキル。

WebGL 2.0 렌더러 아키텍처 설계, GLSL ES 3.00 셰이더 리뷰, GPU 프로파일링, FBO/포스트프로세스 파이프라인 설계, 컨텍스트 손실 디버깅, DPR/필레이트 예산 산정, 시각적 회귀 테스트 및 WebGL-to-WebGPU 마이그레이션 계획을 위한 모듈형 스킬.

用于 WebGL 2.0 渲染器架构设计、GLSL ES 3.00 着色器审查、GPU 性能分析、FBO/后处理管线设计、上下文丢失调试、DPR/填充率预算、视觉回归测试及 WebGL 到 WebGPU 迁移规划。

Model-aware image prompting for Nano Banana Pro and Nano Banana 2. Covers generation, editing, continuity, text-in-image, style routing, and structured JSON output.

Model-aware image prompt engineering for any agent or image generation workflow. Use when writing, improving, translating, debugging, or evaluating prompts for OpenAI image models, Gemini Nano Banana, Midjourney, FLUX, Qwen-Image, Z-Image, Stable Diffusion,…

Route to a portable frontend skill for grounded UI critique, page planning, component specs, and implementation guidance.

Use when a frontend request needs grounded UI critique, page planning, component specs, accessibility review, or implementation guidance without generic design filler.

Builds field maps from multi-source academic or technical corpora: expert mental models, debates, assumptions, prerequisites, oral-exam questions, and sprint study plans. Use for exam prep, research onboarding, or rapid topic mastery from multiple sources.…

Plan, resume, verify, and safely parallelize substantial code work.