文章に含まれる語彙や文の構成、書式、引用の特徴を複数の観点から調べ、AI生成の可能性と根拠を整理します。誤判定を避けるための注意点も示します。
- 文章のAI生成らしさを複数の観点で調べたいとき
- 判定の根拠と反証材料を整理したいとき
- AI文章検出の特徴や限界を知りたいとき
依頼内容と利用可能なスキル一覧を照らし、必要な既存スキルや新規作成候補、組み合わせて動かすエージェントのシステムプロンプトを計画します。
Plan which skills are needed to fulfill user requirements. Use when the user wants to design an agent workflow, plan skill composition, or determine what skills are needed for a task. Input includes user requirements and existing skills list. Output includes recommended existing skills, new skills to create, and a system prompt for the composed agent.
実行スコア 未検証
インストール方法を見るユーザーの要件を必要な能力ごとに分け、手元のスキル一覧と照らし合わせてエージェントの構成を計画します。既存スキルの候補を選び、機能の重複を避けながら不足している能力を洗い出します。不足分については、新しく作るスキルの目的や要件を整理します。最後に、選んだスキルを使うエージェントのシステムプロンプト案を含む、構造化された計画を提示します。
特定の仕事を任せるエージェントを設計したいときや、既存のスキル群で要件を満たせるか確認したいときに向いています。必要なスキルを絞り込み、新規作成が本当に必要か検討する場面にも使えます。
計画には、達成したい要件と利用可能なスキルの一覧が必要です。出力された計画は利用者が確認し、必要に応じて調整する前提です。
この紹介文は、公開されている SKILL.md をもとに AI(OpenAI Codex)が作成しました。正確な仕様は下の原文を確認してください。
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。
Plan and compose skills to fulfill user requirements efficiently.
Before planning, gather:
Break down user requirements into discrete capabilities:
User Request: "Build an agent that can analyze financial reports"
Required Capabilities:
1. PDF reading/parsing
2. Data extraction (tables, numbers)
3. Financial metrics calculation
4. Report generation
5. Visualization (charts)
For each capability, check if an existing skill covers it:
Capability: PDF reading/parsing
Existing Skill Match: pdf (extracts text/tables from PDFs) ✓
Capability: Financial metrics calculation
Existing Skill Match: None found ✗
Selection Criteria:
List capabilities not covered by existing skills. These become candidates for new skills.
Gap Analysis Questions:
For each gap, specify:
skill_name: financial-analyzer
skill_requirements: |
Purpose: Calculate and interpret financial metrics from extracted data
Core Capabilities:
- Calculate common ratios (P/E, ROE, debt-to-equity, etc.)
- Identify trends across time periods
- Flag anomalies or concerns
- Generate insights in plain language
Input: Structured financial data (revenue, expenses, assets, etc.)
Output: Analysis report with metrics, trends, and recommendations
Workflow:
1. Validate input data completeness
2. Calculate standard financial ratios
3. Compare to industry benchmarks (if provided)
4. Identify significant changes or outliers
5. Generate narrative summary
Principles for New Skills:
Design a system prompt that orchestrates all selected skills:
## System Prompt Template
You are a [AGENT_ROLE] agent specialized in [DOMAIN].
### Workflow
When processing requests:
1. [First step - e.g., understand the input]
2. [Second step - e.g., invoke relevant skill]
3. [Third step - e.g., process results]
4. [Final step - e.g., present output]
### Guidelines
- [Guideline 1 - e.g., always validate inputs first]
- [Guideline 2 - e.g., handle errors gracefully]
- [Guideline 3 - e.g., provide clear explanations]
### Output Format
[Specify expected output structure if applicable]
Present the plan in this structure:
# Skills Plan for [User Task]
## 1. Existing Skills to Include
| Skill Name | Purpose in This Agent | Key Capabilities Used |
|------------|----------------------|----------------------|
| [name] | [why needed] | [specific features] |
| [name] | [why needed] | [specific features] |
> If no existing skills apply, write: "None - all required capabilities need new skills."
## 2. New Skills to Create
**Skills needed**: [skill-a], [skill-b], [skill-c] (or "None - existing skills cover all requirements")
### 2.1 [skill-a]
(Continue for each new skill...)
---
## 3. Proposed System Prompt
[Complete system prompt for the agent]
---
## 4. Additional Recommendations
- [Recommendation 1 - e.g., consider adding X capability later]
- [Recommendation 2 - e.g., this skill combination may have limitation Y]
- [Recommendation 3 - e.g., suggested testing approach]
---
**Please review and confirm this plan, or provide feedback for adjustments.**
User Request: "I need an agent that can process customer support tickets, categorize them, and draft responses"
Existing Skills: web-search, email-sender, sentiment-analyzer
Output:
# Skills Plan for Customer Support Agent
## 1. Existing Skills to Include
| Skill Name | Purpose in This Agent | Key Capabilities Used |
|------------|----------------------|----------------------|
| sentiment-analyzer | Assess ticket urgency and customer mood | Sentiment scoring, emotion detection |
## 2. New Skills to Create
**Skills needed**: ticket-categorizer, response-drafter
### 2.1 ticket-categorizer
**Purpose**: Classify support tickets into predefined categories
**Requirements**:
- Parse ticket content (subject + body)
- Match against category definitions
- Handle multi-category tickets
- Provide confidence scores
**Workflow**:
1. Extract key phrases from ticket
2. Match against category keywords and patterns
3. Assign primary and secondary categories
4. Return categorization with confidence
**Input/Output**:
- Input: Ticket text (subject, body, metadata)
- Output: { category: string, subcategory: string, confidence: float }
### 2.2 response-drafter
**Purpose**: Generate contextual draft responses for support tickets
**Requirements**:
- Use ticket category and sentiment as context
- Apply company tone guidelines
- Include relevant knowledge base references
- Support multiple response templates
**Workflow**:
1. Analyze ticket intent and key issues
2. Select appropriate response template
3. Customize with specific details
4. Add relevant links/resources
5. Apply tone adjustments based on sentiment
**Input/Output**:
- Input: Ticket content, category, sentiment, knowledge base context
- Output: Draft response text with placeholders for review
---
## 3. Proposed System Prompt
You are a Customer Support Agent specialized in processing and responding to support tickets efficiently.
### Your Capabilities
You have access to the following skills:
- **sentiment-analyzer**: Use to assess customer mood and ticket urgency before categorizing
- **ticket-categorizer**: Use to classify tickets into appropriate categories
- **response-drafter**: Use to generate initial response drafts
### Workflow
When processing a support ticket:
1. Analyze sentiment to understand customer mood and urgency
2. Categorize the ticket to determine the issue type
3. Draft an appropriate response based on category and sentiment
4. Present the draft for human review before sending
### Guidelines
- Prioritize high-urgency tickets (negative sentiment + billing/account issues)
- Always maintain a helpful, empathetic tone
- Escalate complex technical issues to specialists
- Include relevant self-help resources when applicable
### Output Format
For each ticket, provide:
- Sentiment Assessment: [score and summary]
- Category: [primary] / [secondary if applicable]
- Urgency: [Low/Medium/High]
- Draft Response: [response text]
- Recommended Actions: [any escalation or follow-up needed]
---
## 4. Additional Recommendations
- Consider adding a knowledge-base-search skill for FAQ matching
- The ticket-categorizer should be trained on historical ticket data
- Test with edge cases: multi-issue tickets, non-English tickets, spam
- Monitor response quality and adjust tone guidelines as needed
---
**Please review and confirm this plan, or provide feedback for adjustments.**
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文章に含まれる語彙や文の構成、書式、引用の特徴を複数の観点から調べ、AI生成の可能性と根拠を整理します。誤判定を避けるための注意点も示します。
動画や対応サイトの音声を抽出し、MP3やFLACなどに変換して保存します。ポッドキャストやプレイリストの一括取得、曲情報やカバー画像の埋め込みにも使えます。
DNA・RNA・タンパク質の配列や生物学ファイルをPythonで扱い、配列解析、BLAST、NCBI検索、系統樹作成などの処理を組み立てます。
プロジェクトの説明をもとに、ロゴや配色、書体、名刺、SNSカバー、ブランド仕様書を一貫したビジュアルアイデンティティとしてまとめます。
ポスターや静的なアートを、色・形・余白の方針づくりから制作するスキル。デザイン思想をまとめたMarkdownと、文字を抑えたPNG・PDFの作品を作ります。
ソースコードを読み解き、構成やデータの関係、処理の流れをMermaid.jsの図にしてSVG・PNGで出力するスキルです。