236건 · 관련도순
窓際族物語の動画を、チャプター毎のキーフレーム画像を作らずに「プロンプト＋キャラクターシート＋セリフ音声」だけでMiniMax H3（R2V）に生成させるワークフロー。ユーザーから「画像なしで動画を作って」「キーフレームなしで動画を作って」「シートだけで動画を作って」と指示されたときに必ず使用する。大まかな指示→チャプター分割案→チャプター毎の動きを人間に確認→全チャプター確定→プロンプト作成の順で進め、確定前にプロンプトを書かない。画風の注意文はチャプターに映るものだけを最小限で書き、モブキャラはラン専用のキャラクターシートを作って渡す。動画生成の実行先は/colab-video（Colab）または/local-videoステップ7（ローカルCUDA）。
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窓際族物語の動画をクラウドを使わずフルローカルで生成するワークフロー（MiniMax H3 + ComfyUI）。ユーザーから「ローカルで動画作成して」「ローカルLLMで動画を作って」と指示されたとき、MiniMax H3での動画生成・修正を頼まれたときに必ず使用する。台本→Irodori-TTS/VOICEVOX音声→draw-things-cliキーフレーム一括生成→Qwen3-VL（Ollama）による画像検証→チャプター毎のH3動画生成→ffmpeg結合まで全工程をローカルで完結させる。
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動画制作スキル（/seedance・/local-video）で生成されたキーフレーム・参考画像を台本（script.md）と突き合わせて検証するワークフロー。プロンプトどおりの内容になっているか、小道具の状態（ビールの残量等）や機構小物の位置（ドアノブ・蝶番）が物理的に正しいか、場面の時間帯・天気が急変していないか（昼→夜ジャンプ等）をQwen3-VL（Ollama）とClaudeの目視の二重チェックで検証する。ユーザーから「画像を検証して」「キーフレームをチェックして」と指示されたとき、キーフレーム生成後の検証工程を実行するときに必ず使用する。
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窓際族物語の動画生成工程（/local-videoのステップ7）だけをGoogle Colab（CUDA GPU）で実行する派生ワークフロー。ローカルMacにCUDA GPUが無くMiniMax H3を実行できないとき、ユーザーから「Colabで動画を作って」「H3をColabで回して」「LTXで動画を作って」と指示されたときに必ず使用する。台本・音声・キーフレーム生成・画像検証（ステップ1〜6）と最終結合（ステップ8〜9）はローカルで行い、動画生成だけを同梱ノートブック（H3=h3_colab.ipynb / LTX-2.5=ltx25_colab.ipynb）でColabに切り出す。セリフ（wav駆動リップシンク）はH3、セリフなしI2VチャプターはLTX-2.5も選べる。無料T4は配管検証用、本番生成はL4/A100（Pay As You Go / Colab Pro）。
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窓際族物語の2Dゲームを新規作成し、Seedance（CapCut）で制作した動画をオープニング/イベントのカットシーンとして組み込むワークフロー。ユーザーから2Dゲームの制作を頼まれたとき、添付動画（mp4）をゲームに組み込む・差し替えるよう頼まれたとき、カットシーンの追加・修正を頼まれたときに必ず使用する。
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窓際族物語のストーリー（あらすじ）からSeedance用の台本・動画生成プロンプト・セリフ音声（VOICEVOX/Irodori-TTSボイスクローン）・Codex参考画像を作成するワークフロー。ユーザーからストーリーを渡されたとき、台本やSeedanceプロンプトの作成・修正を頼まれたとき、キャラのセリフ音声の生成を頼まれたとき、クリップの参考画像（キーフレーム）生成を頼まれたときに必ず使用する。
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Annotates bacterial and archaeal genomes (isolates, MAGs, plasmids) with Bakta (active versioned databases, NCBI-compliant output) or Prokka (legacy), producing GFF3/GenBank/EMBL/FASTA with INSDC locus tags. Covers Bakta-vs-Prokka-vs-PGAP-vs-DFAST choice, light-vs-full database tiers, translation-table selection (11/4/25), archaeal and leaderless-gene caveats, the small-ORF blind spot, pseudogene-vs-phase-variation, the pangenome re-annotation trap, and submission compliance. Use when annotating a newly assembled prokaryotic genome, choosing an annotation tool, re-annotating a collection for pangenomics, or preparing annotations for NCBI/DDBJ submission.
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Identifies non-coding RNAs (tRNA, rRNA, snoRNA, snRNA, riboswitches, sRNAs) using Infernal covariance-model search against Rfam, tRNAscan-SE 2.0 for tRNA, barrnap for rRNA, and ARAGORN for tmRNA, plus the small-RNA-seq boundary for miRNA and the transcript-assembly boundary for lncRNA. Covers the structure-conserved-not-sequence-conserved principle (why BLAST fails), GA-threshold and clan-competition correctness, tRNAscan-SE domain modes and pseudogene flags, rDNA copy-number collapse, and why homology annotation is a recall floor. Use when performing genome-wide ncRNA annotation, choosing the right tool for an RNA class, or interpreting ncRNA counts.
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Project gene annotations across genomes using TOGA (Kirilenko 2023 whole-genome-alignment chain-based projection with intactness classification), CESAR 2.0 (Sharma & Hiller 2017 codon-aware exon projection), LiftOff (Shumate & Salzberg 2020 reference-based annotation transfer), Liftover (UCSC), GeMoMa (Keilwagen 2019 evidence-based projection), and Comparative Annotation Toolkit (CAT). Use when transferring annotations from a well-annotated reference to query genome(s), classifying gene-loss vs gene-intact across many genomes at scale, building Zoonomia-style comparative annotations across hundreds of mammals or birds (Kirilenko 2023), detecting pseudogenization, projecting alternative isoforms, or selecting between WGA-anchored (TOGA) vs ortholog-based (LiftOff) annotation transfer strategies.
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Systematic exploratory QA testing of web applications — find bugs, capture evidence, and generate structured reports
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Systematic exploratory QA testing of web applications — find bugs, capture evidence, and generate structured reports
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Identifies non-coding RNAs (tRNA, rRNA, snoRNA, snRNA, riboswitches, sRNAs) using Infernal covariance-model search against Rfam, tRNAscan-SE 2.0 for tRNA, barrnap for rRNA, and ARAGORN for tmRNA, plus the small-RNA-seq boundary for miRNA and the transcript-assembly boundary for lncRNA. Covers the structure-conserved-not-sequence-conserved principle (why BLAST fails), GA-threshold and clan-competition correctness, tRNAscan-SE domain modes and pseudogene flags, rDNA copy-number collapse, and why homology annotation is a recall floor. Use when performing genome-wide ncRNA annotation, choosing the right tool for an RNA class, or interpreting ncRNA counts.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Annotates bacterial and archaeal genomes (isolates, MAGs, plasmids) with Bakta (active versioned databases, NCBI-compliant output) or Prokka (legacy), producing GFF3/GenBank/EMBL/FASTA with INSDC locus tags. Covers Bakta-vs-Prokka-vs-PGAP-vs-DFAST choice, light-vs-full database tiers, translation-table selection (11/4/25), archaeal and leaderless-gene caveats, the small-ORF blind spot, pseudogene-vs-phase-variation, the pangenome re-annotation trap, and submission compliance. Use when annotating a newly assembled prokaryotic genome, choosing an annotation tool, re-annotating a collection for pangenomics, or preparing annotations for NCBI/DDBJ submission.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Project gene annotations across genomes using TOGA (Kirilenko 2023 whole-genome-alignment chain-based projection with intactness classification), CESAR 2.0 (Sharma & Hiller 2017 codon-aware exon projection), LiftOff (Shumate & Salzberg 2020 reference-based annotation transfer), Liftover (UCSC), GeMoMa (Keilwagen 2019 evidence-based projection), and Comparative Annotation Toolkit (CAT). Use when transferring annotations from a well-annotated reference to query genome(s), classifying gene-loss vs gene-intact across many genomes at scale, building Zoonomia-style comparative annotations across hundreds of mammals or birds (Kirilenko 2023), detecting pseudogenization, projecting alternative isoforms, or selecting between WGA-anchored (TOGA) vs ortholog-based (LiftOff) annotation transfer strategies.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Verify every visual-evidence artifact (screenshot/GIF/recording) attached to a PR carries a provenance manifest binding it to real daemon-backed truth — daemon port, run id, transcript head hash, agent node id, commit, and an honest real/fixture/mock source label — and that operator-control-panel PRs cover the required set of proof states. Use when gating a PR's proof artifacts before merge, auditing whether a "LIVE" visual claim is backed by a real daemon run versus a reused or fixture-backed mock, or defining the required state-coverage set for a control-panel change. NOT for the capture technique itself — headless Playwright, `screencapture -x -l`, non-interruptive capture (use port-daddy-agent-skill's visual-evidence doctrine), designing the receipt body schema an artifact attaches to (use agent-work-receipt-designer), or the broader dogfood/Potemkin product-quality bar (use multi-agent-authoring-product-bar).
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Adopt a virtual pet as an AI agent. Name it, feed it, watch it grow. 64+ species from cats and dogs to AI-native creatures. Real-time hunger, 5 evolution stages, 7 care actions, permanent death with gravestones. Pixel art portraits that evolve at each life stage. No crypto — just care. Your pet is waiting.
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Roleplay the most difficult, tech-resistant user for your product. Browse the app as that persona, find every UX pain point, then filter complaints through a pragmatism layer to separate real problems from noise. Creates actionable tickets from genuine issues only.
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[Extended thinking: This workflow implements a sophisticated debugging and resolution pipeline that leverages AI-assisted debugging tools and observability platforms to systematically diagnose and resolve production issues. The intelligent debugging strategy combines automated root cause analysis with human expertise, using modern 2024/2025 practices including AI code assistants (GitHub Copilot, Claude Code), observability platforms (Sentry, DataDog, OpenTelemetry), git bisect automation for ...
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Deploys canary files (honeytokens) across file systems to detect ransomware encryption activity in real time. Uses strategically placed decoy documents monitored via file integrity monitoring or OS-level watchdogs to trigger alerts when ransomware modifies or encrypts them. Activates for requests involving ransomware canary deployment, honeyfile setup, deception-based ransomware detection, or file integrity monitoring for encryption.
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Tiered web access router. TIER 1 insane-search: headless extraction plus WAF bypass via curl_cffi, yt-dlp, Jina Reader, public APIs, Playwright fallback for blocked/403/Cloudflare pages, articles, YouTube subtitles. TIER 1.5 agent-reach: platform-native readers for Chinese/social platforms: Xiaohongshu, Douyin, Weibo, Bilibili, V2EX, WeChat, plus Twitter/Reddit/LinkedIn/GitHub. TIER 2 aside-browser FIRST (user's real logged-in browser: aside exec agent, aside repl Playwright), then CloakBrowser + agent-browser as the stealth fallback: source-patched stealth Chromium (CloakBrowser) driven over CDP by the agent-browser CLI for clicks, forms, screenshots, video, cookie login, QA/dogfood, Electron apps. Triggers: browse, stealth browser, CloakBrowser, agent-browser, cloak launch, bypass bot detection, blocked site, scrape, fill form, screenshot, import cookies, play youtube, podcast transcript. NOT for simple searches (web-search), fetches (webfetch), or messaging/workspace platforms — Slack/Discord/Notion/Gmail have Tier-1 CLIs in external-communication; come here only as its fallback.
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