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Optimize Agent Skill description fields for maximum routing precision and discovery accuracy. Use when a skill is not being activated correctly, descriptions overlap between skills, or improving Tool Coverage score.
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
SOC 職業分類に基づく
Formulates an AI for Science research plan. Proposes how to integrate AI into the researcher's domain through web research and literature survey, and generates a concrete 研究計画書 (research plan document). Use when user doesn't know how to apply AI to their 研究テーマ (research theme), wants to create a 研究プラン (research plan), or wants to learn about AI for Science latest trends (最新動向).
Support post-award research management and reporting for SPReAD. Guide grant application procedures, interim progress review (3 months), final outcome report (6 months), accounting report preparation, budget reallocation between expense categories, research plan change approval requests, publication acknowledgment templates, and research data management implementation. Use when you want to know post-award procedures, write an interim report, create a final report, change budget allocations, reallocate between expense categories, change research plans, or learn how to write publication acknowledgments.
Draft the SPReAD proposal (research plan document). Integrate the research plan, Azure architecture design, and cost estimate to generate a proposal aligned with the 公募要領 requirements. Use when writing the proposal, finalizing the research plan document, or preparing submission documents.
Estimate Azure costs based on architecture design. Generate monthly and full research period (~180 days) cost estimates and produce cost breakdowns required for SPReAD budget applications. Use when estimating Azure costs, planning budgets, or filling in expense sections of the proposal.
Designs a research infrastructure architecture on Microsoft Azure based on the research plan. Generates a comprehensive design document including GPU clusters, ML pipelines, data storage, and network configuration. References Microsoft Learn MCP for optimal configurations. Use when user wants to design Azure構成 (Azure architecture), build GPUクラスタ (GPU cluster), or design MLパイプライン (ML pipeline).
Generate step-by-step experiment procedure documents for running AI research on deployed Azure infrastructure. Covers environment setup, data preparation, training execution, inference, result evaluation, and checkpoint/reproducibility management. Use when you want to create an experiment procedure guide, run experiments on Azure ML, execute GPU training jobs, set up experiment environments, or manage reproducibility. 実験手順書を作りたい、Azure上で実験を実行したい、学習ジョブを走らせたい、 実験環境をセットアップしたい、再現性を管理したい場合に使用。
| name | description-optimizer |
| description | Optimize Agent Skill description fields for maximum routing precision and discovery accuracy. Use when a skill is not being activated correctly, descriptions overlap between skills, or improving Tool Coverage score. |
| metadata | {"author":"coreclaw","version":"1.0"} |
スキルの description フィールドを最適化し、ルーティング精度と発見精度を最大化するスキル。
現在の description を分析:
同一リポジトリ内の他スキルの description と比較:
改善版 description を生成:
改善前後を比較表示して確認
# Description Analysis: <skill-name>
## 現在の description
> <current description>
## 問題点
- [ ] 「何をするか」が不明確
- [ ] 「Use when」が欠落
- [ ] キーワードが汎用的すぎる
- [ ] 他スキルとキーワード競合: <competing-skill>
## 競合分析
| スキル | 重複キーワード | 影響度 |
|--------|---------------|--------|
| <name> | <keywords> | 🔴/🟡/🟢 |
## 改善案
> <optimized description>
## 変更理由
- <理由1>
- <理由2>
同一カテゴリのスキルが複数ある場合、対象物で差別化する:
description は人間向けではなく LLM のルーティングエンジン向けに書く。自然な日本語よりキーワード密度を優先するdescription に同じ動詞+目的語の組み合わせがあると、どちらが選ばれるか不安定になるname は64文字以内