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Create Cursor Agent Skills. Use when authoring a new skill or asking about SKILL.md structure.
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。
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Create Cursor Agent Skills. Use when authoring a new skill or asking about SKILL.md structure.
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
Walk a manager through a full quarterly performance review for a direct report, including peer feedback synthesis, performance/potential ratings, leadership principles assessment, and development planning
Professional UX and landing page audit. Identifies the top 3 critical quality issues, auto-implements fixes, and generates a Loom video transcript to share improvements with the lead. Use when auditing a website or landing page for professionalism and craft.
Help an employee write their own self-assessment for a quarterly performance review — accomplishments, challenges, leadership-principles self-rating, and development-plan progress
Help someone give peer feedback for a colleague using the Non-Violent Communication framework
Use this skill alongside figma-use when the task involves translating an application page, view, or multi-section layout into Figma. Triggers: 'write to Figma', 'create in Figma from code', 'push page to Figma', 'take this app/page and build it in Figma', 'create a screen', 'build a landing page in Figma', 'update the Figma screen to match code', 'convert this modal/dialog/drawer/panel to Figma'. This is the preferred workflow skill whenever the user wants to build or update a full page, modal, dialog, drawer, sidebar, panel, or any composed multi-section view in Figma from code or a description. Discovers design system components, variables, and styles from Code Connect files, existing screens, and library search, then imports them and assembles views incrementally section-by-section using design system tokens instead of hardcoded values.
**MANDATORY prerequisite** — you MUST invoke this skill BEFORE every `use_figma` tool call. NEVER call `use_figma` directly without loading this skill first. Skipping it causes common, hard-to-debug failures. Trigger whenever the user wants to perform a write action or a unique read action that requires JavaScript execution in the Figma file context — e.g. create/edit/delete nodes, set up variables or tokens, build components and variants, modify auto-layout or fills, bind variables to properties, or inspect file structure programmatically.
| name | create-skill |
| description | Create Cursor Agent Skills. Use when authoring a new skill or asking about SKILL.md structure. |
This skill guides you through creating effective Agent Skills for Cursor. Skills are markdown files that teach the agent how to perform specific tasks: reviewing PRs using team standards, generating commit messages in a preferred format, querying database schemas, or any specialized workflow.
Before creating a skill, gather essential information from the user about:
If the user includes exact wording to use in the skill, respect it and use it verbatim in SKILL.md (same words, same order). Do not paraphrase, soften, or expand their copy, and do not add unrequested headings or commentary around it.
If you have previous conversation context, infer the skill from what was discussed. You can create skills based on workflows, patterns, or domain knowledge that emerged in the conversation.
If you need clarification, use the AskQuestion tool when available:
Example AskQuestion usage:
- "Where should this skill be stored?" with options like ["Personal (~/.cursor/skills/)", "Project (.cursor/skills/)"]
- "Should this skill include executable scripts?" with options like ["Yes", "No"]
If the AskQuestion tool is not available, ask these questions conversationally.
Skills are stored as directories containing a SKILL.md file:
skill-name/
├── SKILL.md # Required - main instructions
├── reference.md # Optional - detailed documentation
├── examples.md # Optional - usage examples
└── scripts/ # Optional - utility scripts
├── validate.py
└── helper.sh
| Type | Path | Scope |
|---|---|---|
| Personal | ~/.cursor/skills/skill-name/ | Available across all your projects |
| Project | .cursor/skills/skill-name/ | Shared with anyone using the repository |
IMPORTANT: Never create skills in ~/.cursor/skills-cursor/. This directory is reserved for Cursor's internal built-in skills and is managed automatically by the system.
Every skill requires a SKILL.md file with YAML frontmatter and markdown body:
---
name: your-skill-name
description: Brief description of what this skill does and when to use it
disable-model-invocation: true
---
# Your Skill Name
## Instructions
Clear, step-by-step guidance for the agent.
## Examples
Concrete examples of using this skill.
Default disable-model-invocation: true so the skill only loads when named explicitly. Omit it only when the agent should auto-invoke from ambient context.
| Field | Requirements | Purpose |
|---|---|---|
name | Max 64 chars, lowercase letters/numbers/hyphens only | Unique identifier for the skill |
description | Max 1024 chars, non-empty | Helps agent decide when to apply the skill |
The description is critical for skill discovery. The agent uses it to decide when to apply your skill.
Write in third person (the description is injected into the system prompt):
Be specific and include trigger terms:
Include both WHAT and WHEN:
# PDF Processing
description: Extract text and tables from PDF files, fill forms, merge documents. Use when working with PDF files or when the user mentions PDFs, forms, or document extraction.
# Excel Analysis
description: Analyze Excel spreadsheets, create pivot tables, generate charts. Use when analyzing Excel files, spreadsheets, tabular data, or .xlsx files.
# Git Commit Helper
description: Generate descriptive commit messages by analyzing git diffs. Use when the user asks for help writing commit messages or reviewing staged changes.
# Code Review
description: Review code for quality, security, and best practices following team standards. Use when reviewing pull requests, code changes, or when the user asks for a code review.
The context window is shared with conversation history, other skills, and requests. Every token competes for space.
Default assumption: The agent is already very smart. Only add context it doesn't already have.
Challenge each piece of information:
Good (concise):
## Extract PDF text
Use pdfplumber for text extraction:
\`\`\`python
import pdfplumber
with pdfplumber.open("file.pdf") as pdf:
text = pdf.pages[0].extract_text()
\`\`\`
Bad (verbose):
## Extract PDF text
PDF (Portable Document Format) files are a common file format that contains
text, images, and other content. To extract text from a PDF, you'll need to
use a library. There are many libraries available for PDF processing, but we
recommend pdfplumber because it's easy to use and handles most cases well...
For optimal performance, the main SKILL.md file should be concise. Use progressive disclosure for detailed content.
Put essential information in SKILL.md; detailed reference material in separate files that the agent reads only when needed.
# PDF Processing
## Quick start
[Essential instructions here]
## Additional resources
- For complete API details, see [reference.md](reference.md)
- For usage examples, see [examples.md](examples.md)
Keep references one level deep - link directly from SKILL.md to reference files. Deeply nested references may result in partial reads.
Match specificity to the task's fragility:
| Freedom Level | When to Use | Example |
|---|---|---|
| High (text instructions) | Multiple valid approaches, context-dependent | Code review guidelines |
| Medium (pseudocode/templates) | Preferred pattern with acceptable variation | Report generation |
| Low (specific scripts) | Fragile operations, consistency critical | Database migrations |
Provide output format templates:
## Report structure
Use this template:
\`\`\`markdown
# [Analysis Title]
## Executive summary
[One-paragraph overview of key findings]
## Key findings
- Finding 1 with supporting data
- Finding 2 with supporting data
## Recommendations
1. Specific actionable recommendation
2. Specific actionable recommendation
\`\`\`
For skills where output quality depends on seeing examples:
## Commit message format
**Example 1:**
Input: Added user authentication with JWT tokens
Output:
\`\`\`
feat(auth): implement JWT-based authentication
Add login endpoint and token validation middleware
\`\`\`
**Example 2:**
Input: Fixed bug where dates displayed incorrectly
Output:
\`\`\`
fix(reports): correct date formatting in timezone conversion
Use UTC timestamps consistently across report generation
\`\`\`
Break complex operations into clear steps with checklists:
## Form filling workflow
Copy this checklist and track progress:
\`\`\`
Task Progress:
- [ ] Step 1: Analyze the form
- [ ] Step 2: Create field mapping
- [ ] Step 3: Validate mapping
- [ ] Step 4: Fill the form
- [ ] Step 5: Verify output
\`\`\`
**Step 1: Analyze the form**
Run: \`python scripts/analyze_form.py input.pdf\`
...
Guide through decision points:
## Document modification workflow
1. Determine the modification type:
**Creating new content?** → Follow "Creation workflow" below
**Editing existing content?** → Follow "Editing workflow" below
2. Creation workflow:
- Use docx-js library
- Build document from scratch
...
For quality-critical tasks, implement validation loops:
## Document editing process
1. Make your edits
2. **Validate immediately**: \`python scripts/validate.py output/\`
3. If validation fails:
- Review the error message
- Fix the issues
- Run validation again
4. **Only proceed when validation passes**
Pre-made scripts offer advantages over generated code:
## Utility scripts
**analyze_form.py**: Extract all form fields from PDF
\`\`\`bash
python scripts/analyze_form.py input.pdf > fields.json
\`\`\`
**validate.py**: Check for errors
\`\`\`bash
python scripts/validate.py fields.json
# Returns: "OK" or lists conflicts
\`\`\`
Make clear whether the agent should execute the script (most common) or read it as reference.
scripts/helper.pyscripts\helper.py# Bad - confusing
"You can use pypdf, or pdfplumber, or PyMuPDF, or..."
# Good - provide a default with escape hatch
"Use pdfplumber for text extraction.
For scanned PDFs requiring OCR, use pdf2image with pytesseract instead."
# Bad - will become outdated
"If you're doing this before August 2025, use the old API."
# Good - use an "old patterns" section
## Current method
Use the v2 API endpoint.
## Old patterns (deprecated)
<details>
<summary>Legacy v1 API</summary>
...
</details>
Choose one term and use it throughout:
processing-pdfs, analyzing-spreadsheetshelper, utils, toolsWhen helping a user create a skill, follow this process:
Gather information about:
If you have access to the AskQuestion tool, use it for efficient structured gathering. Otherwise, ask conversationally.
Here's a complete example of a well-structured skill:
Directory structure:
code-review/
├── SKILL.md
├── STANDARDS.md
└── examples.md
SKILL.md:
---
name: code-review
description: Review code for quality, security, and maintainability following team standards. Use when reviewing pull requests, examining code changes, or when the user asks for a code review.
---
# Code Review
## Quick Start
When reviewing code:
1. Check for correctness and potential bugs
2. Verify security best practices
3. Assess code readability and maintainability
4. Ensure tests are adequate
## Review Checklist
- [ ] Logic is correct and handles edge cases
- [ ] No security vulnerabilities (SQL injection, XSS, etc.)
- [ ] Code follows project style conventions
- [ ] Functions are appropriately sized and focused
- [ ] Error handling is comprehensive
- [ ] Tests cover the changes
## Providing Feedback
Format feedback as:
- 🔴 **Critical**: Must fix before merge
- 🟡 **Suggestion**: Consider improving
- 🟢 **Nice to have**: Optional enhancement
## Additional Resources
- For detailed coding standards, see [STANDARDS.md](STANDARDS.md)
- For example reviews, see [examples.md](examples.md)
Before finalizing a skill, verify: