Create reproducible animated infographic GIFs from structured data or measured results, with audience-language labels, explicit timing semantics, representative-frame QA, and a static fallback. Use when asked for GIF生成, animated infographic, 動的インフォグラフィック, 結果が流れる図, animated benchmark, or a blog/SNS animation whose displayed metrics must remain traceable to source data.
インストール方法を見るインストールする前に、エージェントに与えられる指示の中身を確認できます。
対象外: 静止PNGだけのインフォグラフィック、既存動画の分析、編集可能な構成図。
illustrative: 動きは演出。処理速度を表さない。measured: frame timingを実測timestampから作る。mixed: 数値は実測、カードや棒の動きは演出。1200x675、必要なら静止fallbackも作る。Freeze the evidence
1 request、serial total、fresh-process end to endなど、何を測った値か決める。Design the story
この記事での整理などscopeを示し、製品包含や成熟度に見える矢印・入れ子を避ける。Localize before rendering
段階で採点ではなく定義した基準のどの段階に近いか。Make timing explicit inside the asset
illustrative: カード移動は演出（実測速度ではありません）を表示する。measured: 測定境界と単位を表示し、frame durationを実測値から作る。mixed: 表示値は実測 / 棒・カードの動きは演出の両方を書く。Generate reproducibly
Validate mechanically
python scripts/helper.py output.gif `
--expected-width 1200 --expected-height 675 `
--timing-mode illustrative `
--contact-sheet output-contact.jpg `
--report output-validation.json
--timing-mode measured also requires --measured-ms <total>; --duration-tolerance defaults to 0.1. Pillow is required.Inspect visually
model speedではなくobserved end-to-end timeとして扱う。まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
Design, review, and debug agent workflows, and decide when a request should use a prompt, instruction, skill, agent, or hook before escalating to multi-agent design. Use for .agent.md / .instructions.md / .prompt.md / AGENTS.md work, workflow architecture, orchestration planning, scheduled automation model allocation, or when agent workflows may be overkill. Triggers on 'agent workflow', 'create agent', 'automation models', 'ワークフロー設計', 'orchestrator'.
Design, run, and review fair performance, token, and cost comparisons across GitHub Copilot CLI, Codex CLI, and direct model APIs. Use for AI CLI benchmark, Copilot vs Codex, prompt-cache diagnosis, cache read/write analysis, fresh-process latency measurement, token-cost comparison, or when a benchmark must separate provider caching from local CLI caches.
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。
Analyze historical VS Code GitHub Copilot Chat sessions by model, reasoning effort, AIU, time, reliability, workflow behavior, and external quality evidence, or safely prune workspace-scoped local chat history by age. Use for session analysis, repeated-task retrospectives, model evaluation, cost/performance analysis, and old session cleanup.
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。
Create polished, validated architecture, workflow, sequence, data-flow, and lifecycle/state diagrams as explorable standalone HTML with inline SVG, dark/light themes, optional trace motion, and PNG/JPEG/WebP/SVG/WebM export. Accept plain-language requirements or pasted Mermaid flowchart, sequenceDiagram, and stateDiagram input; inspect repository evidence when the diagram must reflect real code. Use when the user asks to visualize system architecture, infrastructure, cloud/security/network topology, technical workflows, API call sequences, request lifecycles, data pipelines, ETL/ELT, data lineage, state machines, or to convert/beautify Mermaid. Also use for Archify, interactive or animated HTML diagrams, インタラクティブ図, 動く構成図. When an editable .drawio file is required, use the draw.io workflow instead and treat Archify HTML only as a supplement.
Generate ASCII-art text banners for terminals, READMEs, and CLI startup messages. Use when a user asks for an ASCII banner, text art, バナー, アスキーアート, or a decorative header for a script/README.
Generate Azure environment monthly report (Markdown + PowerPoint) from Azure Advisor and Cost Management API. Use when creating Advisor recommendations report, cost trend analysis, or security/reliability assessment for a customer subscription. Triggers on 'Advisor report', 'Azure monthly report', 'subscription report', 'Azure環境レポート', '簡易月次レポート'.