Audit websites for accessibility issues and WCAG compliance. Use when checking accessibility, fixing a11y issues, or ensuring WCAG compliance.
中文简介正在准备中。当前显示原文说明。
Extract and analyze data from invoices, receipts, bank statements, and financial documents. Categorize expenses, track recurring charges, and generate expense reports. Use when user provides financial PDFs or images.
中文简介正在准备中。当前显示原文说明。
查看安装方法安装前可以查看交给 AI 代理的指令内容。
Extract structured data from financial documents with automatic categorization and analysis.
Activate when the user:
Identify Document Type
Extract Core Information
For Invoices:
For Receipts:
For Bank/Credit Card Statements:
Categorize Expenses
Identify Patterns
Generate Structured Output
# Financial Document Analysis
## Document Details
- **Type**: Invoice / Receipt / Statement
- **Date**: [Date]
- **Vendor/Merchant**: [Name]
- **Document Number**: [Number]
- **Total Amount**: $X,XXX.XX
## Line Items
| Description | Quantity | Unit Price | Total |
|-------------|----------|------------|-------|
| [Item] | X | $XX.XX | $XX.XX |
## Financial Summary
- **Subtotal**: $X,XXX.XX
- **Tax**: $XXX.XX
- **Total**: $X,XXX.XX
- **Payment Method**: [Method]
## Expense Categorization
| Category | Amount | Items |
|----------|--------|-------|
| Software | $XXX | Slack, GitHub |
| Office | $XX | Supplies |
## Insights
- Tax-deductible business expenses: $X,XXX
- Recurring charges detected: 3 subscriptions ($XXX/month)
- Foreign transaction fees: $XX
## Flagged Items
- [ ] Large expense ($X,XXX) - verify approval
- [ ] Duplicate charge detected on [date]
## Export Data (CSV Format)
```csv
Date,Vendor,Description,Category,Amount,Tax Deductible
2025-01-15,Adobe,Creative Cloud,Software,52.99,Yes
## Examples
**User**: "Extract data from this invoice PDF"
**Response**: Parse PDF → Extract vendor info, line items, totals → Categorize as business expense → Format as structured data → Generate CSV export
**User**: "Analyze my bank statement and categorize expenses"
**Response**: Extract all transactions → Categorize each (dining, software, travel) → Identify recurring charges → Calculate totals by category → Flag unusual transactions → Generate spending report
**User**: "Parse these 10 receipts and create an expense report"
**Response**: Process each receipt → Extract merchant, date, amount, items → Categorize expenses → Calculate totals → Generate consolidated report → Create CSV for expense submission
## Best Practices
- Preserve exact amounts (don't round)
- Maintain currency symbols and formats
- Note when data is unclear or illegible
- Flag suspicious or duplicate transactions
- Provide tax-relevant categorization
- Use standard expense categories
- Generate export-ready formats (CSV, JSON)
- Protect sensitive info (mask account numbers)
- Identify missing information (no date, unclear vendor)
- Calculate totals and verify against document
- Note discrepancies or calculation errors
- Include exchange rates for foreign currency
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Audit websites for accessibility issues and WCAG compliance. Use when checking accessibility, fixing a11y issues, or ensuring WCAG compliance.
中文简介正在准备中。当前显示原文说明。
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.
中文简介正在准备中。当前显示原文说明。
Use to test whether an AI agent, prompt, or skill actually works before or after it ships — build a small eval set, score runs, and catch regressions when the prompt or model changes. Trigger when the user asks whether a prompt is good, wants to compare models or versions, sees inconsistent agent output, or is about to put an agent in front of customers.
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Deploys swarms of sub-agents for massive parallel data processing tasks. Unlike agent-army (which is for code changes), this is for DATA tasks -- processing 1000 documents, analyzing datasets, bulk content generation. Configurable swarm size, task distribution, result aggregation, progress tracking, and error recovery.
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Designs and deploys custom agent teams for specific business workflows. Interactive discovery of business processes, then generates complete team configurations with specialized agent roles, tool access, communication protocols, and handoff rules.
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Agent-to-Agent (A2A) communication protocol. Connect two or more Claude agents that pass messages, share context, delegate tasks, and collaborate. Implements structured handoffs, shared memory, and multi-agent conversations.
中文简介正在准备中。当前显示原文说明。