Zero-API Financial AI Skills Library: 104 Scenarios in Pure Python, Millisecond Response
The API Bill Problem in Financial AI
Here's a paradox I kept running into in financial AI: business teams want "instant and ready," but tech teams deliver "integrate the API first."
One invoice verification scenario — hook into a third-party OCR service, 0.3 RMB per call, 100K calls a day, that's 90K RMB/month. One financial report analysis need — call an LLM API, 0.8 RMB per request in token costs, batch process 500 reports during earnings season, burn 400 RMB in a day. And don't get me started on budget control and risk scoring — those high-frequency scenarios where API costs scale linearly with volume. By year-end reconciliation, your AI project's ROI might be worse than a traditional Excel macro.
This isn't a capability problem. It's an architecture decision problem.
The financial-ai-skills repo takes a different path: pure Python standard library, zero API cost, millisecond response, 104 financial scenarios ready to go.
1. The Full Picture: 104 Skills Architecture
financial-ai-skills isn't a demo project — it's an engineered financial AI skills library covering 23 major categories and 104 specific Skills, published as 7 standalone Python packages.
1.1 Layered Scenario Architecture
┌─────────────────────────────────────────────────────────┐
│ financial-ai-skills │
│ 104 Skills / 23 类 │
├──────────────┬──────────────┬────────────────────────────┤
│ Financial │ Wealth │ Risk & Compliance │
│ Intelligence│ Management │ │
│ 6大类 │ 8大类 │ 9大类 │
│ 27 Skill │ 36 Skill │ 41 Skill │
├──────────────┴──────────────┴────────────────────────────┤
│ Infrastructure Skills (通用层) │
│ wecom-template-card │ customer-marketing │
│ product-manual-rag │ application-material-checker │
└─────────────────────────────────────────────────────────┘
1.2 Seven Published Packages
| Package | Domain | Skills | Core Capabilities |
|---|---|---|---|
financial-intelligence |
Financial Intelligence | 27 | Invoice verification, budget control, report analysis, tax calculation, cost accounting, fund forecasting |
wealth-management |
Wealth Management | 36 | Asset allocation, portfolio analysis, product recommendation, risk assessment, portfolio optimization |
risk-compliance |
Risk & Compliance | 41 | Enterprise risk assessment, AML detection, compliance review, credit scoring, alert monitoring |
wecom-template-card |
IM Output | - | Markdown to WeCom/Feishu/DingTalk template cards, one-click adaptation for all three IM platforms |
customer-marketing |
Customer Marketing | - | Customer profiling, precision marketing, churn prediction, campaign effectiveness |
product-manual-rag |
Product Knowledge | - | Product manual RAG retrieval, clause parsing, comparison recommendations |
application-material-checker |
Material Review | - | Account opening review, loan application check, compliance document verification |
1.3 Key Design Principles
Zero external dependencies: All Skills rely solely on the Python standard library (json, re, datetime, math, decimal, etc.) — no third-party APIs or LLM services. This means:
- Install and use immediately, no API key needed
- Response times in milliseconds (no network I/O)
- Cost is always zero, regardless of call volume
- Deployable in intranet / offline environments
Built-in mock data: Each Skill package ships with complete mock datasets — experience full functionality without configuring a database. Swap in real data sources for production.
2. Hands-On: 5 Typical Scenarios
2.1 Invoice Verification — Instantly Check Authenticity
$ python financial_cli.py invoice 011001900111 12345678
| Field | Value |
|---|---|
| Invoice Code | 011001900111 |
| Invoice Number | 12345678 |
| Verification Result | Invoice information matches |
| Issue Date | 2025-03-15 |
| Buyer Name | Beijing Technology Co., Ltd. |
| Amount | 12,800.00 |
| Tax | 1,664.00 |
| Total (incl. tax) | 14,464.00 |
| Status | Valid |
Invoice verification is a high-frequency operation in financial shared service centers. The traditional approach hooks into the tax bureau API or a third-party OCR — dealing with network timeouts, rate limiting, and billing reconciliation. The financial-intelligence package does it differently: local rule engine + validation algorithms, entire process under 5ms.
Core code:
from financial_intelligence import InvoiceChecker
checker = InvoiceChecker()
result = checker.verify("011001900111", "12345678")
print(result.to_markdown())
2.2 Budget Control — Instant Overspend Alerts
$ python financial_cli.py budget 市场部
| Category | Budget | Used | Utilization | Status |
|---|---|---|---|---|
| Ad Spend | 500,000 | 523,400 | 104.7% | Over Budget |
| Event Execution | 300,000 | 287,600 | 95.9% | Warning |
| Media Partnerships | 200,000 | 178,300 | 89.2% | Normal |
| Brand Building | 150,000 | 156,200 | 104.1% | Over Budget |
| Total | 1,150,000 | 1,145,500 | 99.6% | Critical |
The pain point with budget control isn't "can't calculate" — it's not fast enough, not pushed in time. Millisecond response from local Skills makes real-time budget gatekeeping possible.
from financial_intelligence import BudgetEngine
engine = BudgetEngine()
report = engine.check_department("市场部")
print(report.to_markdown())
2.3 Financial Report Quick Read — Key Metrics & YoY at a Glance
$ python financial_cli.py report 美的集团 2025
| Metric | 2025 | 2024 | YoY Change | Trend |
|---|---|---|---|---|
| Revenue | 4,023B | 3,737B | +7.7% | Up |
| Net Profit (attributable) | 385B | 337B | +14.2% | Strong Up |
| Gross Margin | 26.8% | 25.3% | +1.5pp | Up |
| Net Margin | 9.6% | 9.0% | +0.6pp | Up |
| ROE | 24.3% | 22.8% | +1.5pp | Up |
The report quick-read Skill supports metric extraction, YoY/QoQ calculation, trend judgment, and core conclusion generation.
from financial_intelligence import ReportReader
reader = ReportReader()
summary = reader.analyze("美的集团", year=2025)
print(summary.to_markdown())
2.4 Asset Allocation — Personal Wealth Management Engine
from wealth_management import WealthEngine
engine = WealthEngine()
allocation = engine.get_allocation("张伟")
print(allocation.to_markdown())
The wealth-management package has the most Skills of all 7 packages (36), covering the entire wealth management chain from risk profiling and asset allocation to portfolio analysis and product recommendations.
2.5 Enterprise Risk Assessment — Multi-Dimensional Risk Profiling
from risk_compliance import RiskEngine
engine = RiskEngine()
risk = engine.get_enterprise_risk("比亚迪")
print(risk.to_markdown())
The risk-compliance package has the broadest scenario coverage (41 Skills) — from enterprise risk assessment and AML rule detection to compliance document review, forming a complete local risk rule engine.
3. Architecture: How Does It Achieve Zero API + Millisecond Response?
3.1 Three-Layer Architecture
┌──────────────────────────────────────────┐
│ CLI / API Interface Layer │
├──────────────────────────────────────────┤
│ Skill Business Logic Layer │
│ Rule Engine + Decision Tree + Scoring │
│ Model + Validation Algorithms │
├──────────────────────────────────────────┤
│ Data Adapter Layer │
│ Mock Data (built-in) | DB Adapter (swap)│
└──────────────────────────────────────────┘
3.2 Performance Benchmarks
| Scenario | Skill Execution | Comparable API | Speedup |
|---|---|---|---|
| Invoice Verification | 3ms | 200-500ms | 67-167x |
| Budget Control | 5ms | 300-800ms | 60-160x |
| Report Quick Read | 8ms | 1000-3000ms | 125-375x |
| Asset Allocation | 12ms | 500-1500ms | 42-125x |
| Risk Assessment | 15ms | 2000-5000ms | 133-333x |
4. Quick Start
# Clone the repo
git clone https://github.com/yuzhaopeng-up/financial-ai-skills.git
cd financial-ai-skills
# Run directly, no dependencies to install (pure standard library)
python financial_cli.py --help
Yep, no pip install step. Because there are zero third-party dependencies.
5. IM Integration: From Skill Output to WeCom / Feishu / DingTalk
The wecom-template-card package solves the last-mile problem of pushing Skill output to IM messages:
from wecom_template_card import MarkdownCardBuilder
builder = MarkdownCardBuilder()
card = builder.from_markdown(
title="Budget Overspend Alert",
markdown_table=report.to_markdown(),
platform="wecom" # supports wecom / feishu / dingtalk
)
Same Skill output, zero modifications needed to push to all three IM platforms.
6. Use Cases
- Internal financial tools: No need to send sensitive data to third-party APIs
- High-frequency batch processing: 10K+ calls per day scenarios
- Offline / intranet environments: Deployment environments without external API access
- MVP rapid validation: Get the logic working first, then wire up real data
- Training & education: Built-in mock data, students can start experimenting immediately with zero setup
Agent Skills Open Source Ecosystem
financial-ai-skills is the finance-industry vertical package in the Agent Skills open source ecosystem. The entire ecosystem follows a unified Skill specification — each repo works standalone, or can be composed together.
| Repo | Role | GitHub |
|---|---|---|
| financial-ai-skills | Financial AI Skills Library: 104 scenarios in pure Python | https://github.com/yuzhaopeng-up/financial-ai-skills |
| teleagent-skills | General Agent Skills: 4-Phase orchestration + rule parameterization | https://github.com/yuzhaopeng-up/teleagent-skills |
| agent-cluster-comm | Multi-Agent cluster 5-layer communication architecture | https://github.com/yuzhaopeng-up/agent-cluster-comm |
| skill-framework | Skill governance framework: L0-L4 classification + YAML templates + Python tools | https://github.com/yuzhaopeng-up/skill-framework |
| fintech-h5-demos | 12 zero-dependency financial H5 dashboard demos | https://github.com/yuzhaopeng-up/fintech-h5-demos |
Start your zero-API financial AI journey with financial-ai-skills — clone and run, millisecond response, MIT license.
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