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Design and build a 10-30 second motion-design showreel or brand reel in the style of Nate's Opus 5.5 "CLAUDE / MOTION REEL": one brand motif transformed through labeled craft chapters, a persistent HUD, cuts locked to a 120-130 BPM grid, a sub-second flurry, and a held logo lockup that answers the opening. Use when asked for a showreel, sizzle reel, brand reel, motion reel, "a reel like the Opus one", "show off motion design for <brand>", a 15-second brand film cut to music, or to study why a reference reel works and rebuild that energy for another brand.

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Design and build a 10-30 second motion-design showreel or brand reel in the style of Nate's Opus 5.5 "CLAUDE / MOTION REEL": one brand motif transformed through labeled craft chapters, a persistent HUD, cuts locked to a 120-130 BPM grid, a sub-second flurry, and a held logo lockup that answers the opening. Use when asked for a showreel, sizzle reel, brand reel, motion reel, "a reel like the Opus one", "show off motion design for <brand>", a 15-second brand film cut to music, or to study why a reference reel works and rebuild that energy for another brand.

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Produce a high-confidence, CONCISE implementation / architecture / strategy PLAN that follows the OMC plan system end to end. It first runs OMC's real requirements interview (auto-chains the `omc-plan` skill: one question at a time, explore-first, Analyst consult) to produce an initial plan in `.omc/plans/`, then DEEPENS that plan with a 3-round ITERATIVE fusion loop — each round an Opus 4.8 panelist (Agent subagent) and a GPT-5.5 panelist (via codex) critique/improve it IN PARALLEL and BLIND, then Opus 4.8 (the orchestrator) judges and synthesizes one tighter plan that seeds the next round, stopping early on convergence. Writes the final dense plan back to the same `.omc/plans/<slug>.md`, then offers the OMC handoff (`/omc-plan --review` → `/team` or `/ralph`). Use for high-stakes planning, architecture decisions, strategy calls, and hard debugging — especially when the user runs /plan or asks how to approach a non-trivial build. Reuses the `fusion` skill's run_codex.sh + judge rubric. NOT for trivial tasks (single pass is enough).

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Deploy a 2-layer parallel agent hierarchy for large, parallelizable work — big refactors, multi-file migrations, codebase-wide audits, bulk generation. A top-tier commander (Fable or Opus) orchestrates the swarms; the user picks a power level (Max Power / Heavy / Balanced / Economy) that sets the Opus/Sonnet/Haiku model mix per layer. Layer 1 is 3-50+ specialist agents, each with its own full context window; Layer 2 is 2+ sub-agents per member. Includes git safety, tiered sizing, a pre-deploy gate, phantom-completion checks, and multi-wave follow-up.

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Audio and video processing with ffmpeg. Use when: user asks to convert, trim, merge, compress, or transcode video or audio files; extract audio from video; create GIFs or animated WebP from video; add subtitles or watermarks to video; change video resolution, framerate, or codec; normalize audio loudness; extract frames from video; concatenate clips; create thumbnails from video; strip or add audio tracks; convert between audio formats (MP3, AAC, FLAC, Opus, WAV); adjust volume; apply video filters; stabilize shaky video; generate waveform or spectrum visualizations; probe media file metadata. Triggers on 'ffmpeg', 'video', 'audio', 'transcode', 'MP4', 'MKV', 'WebM', 'MP3', 'AAC', 'FLAC', 'Opus', 'WAV', 'GIF from video', 'extract audio', 'add subtitles', 'video to gif', 'compress video', 'trim video', 'merge videos', 'normalize audio', 'framerate', 'resolution', 'bitrate', 'codec', 'ffprobe', 'waveform', 'spectrogram'.

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Expert guide for intelligent model cascading and routing — complexity-scored task routing from Flash/Haiku to Sonnet/Opus/Astra, dynamic escalation with quality gates, 40-60% token cost reduction while maintaining output quality / Panduan ahli untuk kaskade dan routing model cerdas — routing tugas berbasis skor kompleksitas dari Flash/Haiku ke Sonnet/Opus/Astra, eskalasi dinamis dengan gerbang kualitas, pengurangan biaya token 40-60% dengan kualitas output terjaga.

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Build one-page HTML/PDF resumes from copy-paste YAML using one of 12 official templates. HARD GATES: deliver exactly one A4 page; never invent a layout — copy assets/templates/<id> into output/<id>/ and edit that copy only. Keep a full inventory in raw.data.yaml. For each job, COPY selected entries into resume.data.yaml (polish there), then apply that file to a template. Apply pasted YAML by replacing visible text in the official resume.html, then run QA. Default template is basic-a4. Use when creating, iterating, polishing copy, migrating, exporting, or designing resume/CV HTML/PDF. For polishing resume copy, prefer writing-oriented models (Claude Fable 5 / Claude Opus 4.6-4.7 or Opus 5 / Kimi K3 / GLM-5.1-5.3-max / Qwen 3.8 Max); do not use GPT-5.6 Terra/Luna or GPT-5.5 Instant.

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Runs a deliberative two-model committee of GPT-6 Astra and Claude Opus under a selectable chair, Fable by default, with `/model-committee-astra`, `/model-committee-opus`, and `/model-committee-sol` selecting the others. Use when a consequential decision has several defensible options and two model families should propose, critique, revise, and cross-rank them against a predeclared rubric before converging, as in research design, interpretation, architecture, manuscript strategy, or evaluation design. Not for factual lookups, independent-coder reliability, brainstorming, routine implementation, or final professional judgment.

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Run one consequential, contestable decision through a deliberative committee of GPT-6 Astra and Claude Opus, chaired by fable (default), astra, opus, or sol. Not for factual lookups, brainstorming, routine implementation, independent-coder reliability, or final professional judgment. Use when several options are defensible and the two model families should propose, critique, revise, and cross-rank under a predeclared rubric.

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Work with a running QDash instance through qdash-client and saved local profiles. Use when an agent needs to inspect chips, metrics, task results, calibration data, provenance, issues, flows, executions, project files, project-scoped QDash API data, or OpenAPI endpoints from QDash using qdash-client, ~/.config/qdash/config.ini, XDG_CONFIG_HOME/qdash/config.ini, QDASH_* environment variables, or a local oqtopus-team/qdash checkout.

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反復レビュー・ループ(codex-review-loop・fable-review-loop・opus-review-loop)が共有する判断ロジックの正本。起動可否ゲート・レビュアーの正体に依存しない大前提・常設観点・レビュー依頼に含める内容と含めてはならない内容・照合仕分け・終了判定・報告を定義する。内部専用でユーザーが直接使うものではない。

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レビュー専用サブエージェント(fable-reviewer・opus-reviewer等)を起動する反復レビュー・ループが共有する起動契約と手順の正本。Agentツールでの同期起動、2ラウンド目以降の同一エージェント継続、レビュー指示文の追加要件、失敗の扱い、1ラウンドを回して結末を出すまでの流れを定義する。内部専用でユーザーが直接使うものではない。

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Measurement-driven model router for Claude Code. Routes Haiku/Sonnet/Opus per task class, logs every routed task with real $ numbers, and A/B tests cheaper tiers before you trust the savings.

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Operating discipline distilled from Claude Fable 5 for sessions and subagents on weaker models (Opus 4.x, Sonnet 5/4.x, Haiku 4.5) doing agentic coding. Load it at the START of any non-trivial task when not running on Fable 5: debugging, refactoring, changes across 2+ files, or unfamiliar code; and instruct a subagent on Opus/Sonnet/Haiku to read this skill with a non-trivial assignment. Triggers include "think like Fable", "fable-mode", "turn on discipline", "work carefully", dispatching a subagent on a weaker model, and repeated mistakes or rework in the session. NOT needed for trivial edits (rename, typo, one-liner) or purely conversational answers.

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Operating discipline distilled from Claude Fable 5 for sessions and subagents on weaker models (Opus 4.x, Sonnet 5/4.x, Haiku 4.5) doing agentic coding. Load it at the START of any non-trivial task when not running on Fable 5. Операционная дисциплина мышления Fable 5 для менее сильных моделей. Загружай в начале любой нетривиальной задачи (дебаг, рефакторинг, ≥2 файлов, незнакомый код), когда работаешь НЕ на Fable 5 — и указывай сабагенту на Opus/Sonnet/Haiku прочитать этот скилл вместе с нетривиальным заданием. Триггеры — «думай как Fable», «fable-mode», «включи дисциплину», «работай аккуратно», запуск сабагента на слабой модели, повторяющиеся ошибки/переделки в сессии. НЕ нужен для тривиальных правок (rename, опечатка, one-liner) и чисто разговорных ответов.

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使用AgentShield检查Claude Code配置，报告硬编码秘密信息、权限过宽和恶意指令注入等风险，生成含严重程度与评级的报告，并自动修复部分问题。

• 启用Claude Code时检查配置
• 权限或MCP配置更改后审计
• 加入仓库时检查已有配置

协助在Claude Code中配置Plankton，在每次编辑文件后自动格式化并检查代码。还涵盖剩余违规的自动修复、复查，以及防止检查规则被放宽的保护措施。

• 每次编辑后自动格式化并检查代码
• 配置剩余检查违规的自动修复
• 防止检查规则被放宽

为主要由AI智能体实施的开发工作制定完成条件，拆分可独立验证的任务，并比较实施前后的评估结果；同时按难度选择模型，记录费用、耗时与重试情况。

• 规划交给AI智能体的开发工作
• 将大型变更拆成可验证的任务
• 比较实施前后的评估结果

使用项目或网站的实际界面、字体与素材制作上线宣传视频，涵盖内容构思、画面、音乐、音效、混音与导出，并生成配套的分享文案。

• 制作项目上线宣传视频
• 用实际界面介绍网站
• 制作竖版或正方形宣传视频

读取项目内容，确定介绍角度和表达风格，制作简短发布视频，并同时生成海报图片与分享文案。可指定横屏、竖屏或方形比例、时长及音频选项。

• 制作项目发布视频
• 准备竖屏或方形介绍视频
• 选择适合项目的视频风格

依据按编程语言分类的资料，协助开发和维护Claude应用。涵盖官方SDK调用、工具集成、模型迁移、费用检查、提示词审查和评估流程。

• 在应用中接入Claude API
• 开发使用自定义工具的智能体
• 为现有Claude集成迁移模型

以文字条形图展示ponytail公开基准测试中的代码量、输出词元、费用和耗时降幅，同时列出质量指标，并与当前任务的实测结果区分。

• 查看代码量和费用的降幅
• 比较运行时间与输出词元
• 同时查看降幅和质量指标

用文字条形图展示Ponytail 5公开基准测试中的代码量、输出词元、费用、时间及检查结果，对比未使用技能的情况，不计算当前项目的节省量。

• 查看公开的代码量与费用对比
• 查看正确性和测试相关测量结果
• 同时查看测量条件与结果

Compare saved benchmark runs over time to track win rate, response times, escalation rate, and LLM baseline costs. Review changes between the first and latest runs, with flags for declining win rates or rising average latency.

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• Check performance trends before a release
• Review benchmark history after expanding the test corpus
• Track latency changes after an agent-booster upgrade