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Convert Logs to AML Simulation Data (
Split Accounts by Bank ID (
Combine AML Simulation Outputs (
Aml Data Generator
by
Tang Weigang
· GitHub ↗
· v0.3.3
· MIT-0
122
Downloads
0
Stars
0
Active Installs
5
Versions
Install in OpenClaw
/install aml-data-generator
Description
生成符合AMLSim格式的合成交易数据，将交易日志转换为用于反洗钱检测系统测试的模拟数据集，支持按银行ID分割账户、合并多源输出并生成交易网络图。
README (SKILL.md)
AML 数据生成 (aml-data-generator)
生成符合AMLSim格式的合成交易数据，将交易日志转换为用于反洗钱检测系统测试的模拟数据集，支持按银行ID分割账户、合并多源输出并生成交易网络图。
Pipeline
data_collection -> data_storage -> factor_computation -> target_selection -> trading_execution -> visualization
Top Use Cases (13 total)
Convert Logs to AML Simulation Data (UC-101)
Convert transaction log files into synthetic AML simulation data for testing anti-money laundering detection systems Triggers: convert logs, synthetic data, AML simulation
Split Accounts by Bank ID (UC-102)
Partition account CSV files by bank identifier for bank-specific analysis and processing Triggers: split accounts, bank ID, partition data
Combine AML Simulation Outputs (UC-103)
Aggregate multiple AMLSim output files into a consolidated dataset for comprehensive analysis Triggers: combine outputs, merge data, AMLSim aggregation
For all 13 use cases, see references/USE_CASES.md.
Execute trigger: When user intent matches intent_router.uc_entries[].positive_terms AND user uses action verb (run/execute/跑/执行/backtest/fetch/collect)
What I'll Ask You
- Target market: A-share (default), HK, or crypto? (US stocks in ZVT are half-baked — stockus_nasdaq_AAPL exists but coverage is thin)
- Data source / provider: eastmoney (free, no account), joinquant (account+paid), baostock (free, good history), akshare, or qmt (broker)?
- Strategy type: MACD golden-cross, MA crossover, volume breakout, fundamental screen, or custom factor?
- Time range: start_timestamp and end_timestamp for backtest period
- Target entity IDs: specific stocks (stock_sh_600000) or index components (SZ1000)?
Semantic Locks (Fatal)
| ID | Rule | On Violation |
|---|---|---|
SL-01 |
Execute sell orders before buy orders in every trading cycle | halt |
SL-02 |
Trading signals MUST use next-bar execution (no look-ahead) | halt |
SL-03 |
Entity IDs MUST follow format entity_type_exchange_code | halt |
SL-04 |
DataFrame index MUST be MultiIndex (entity_id, timestamp) | halt |
SL-05 |
TradingSignal MUST have EXACTLY ONE of: position_pct, order_money, order_amount | halt |
SL-06 |
filter_result column semantics: True=BUY, False=SELL, None/NaN=NO ACTION | halt |
SL-07 |
Transformer MUST run BEFORE Accumulator in factor pipeline | halt |
SL-08 |
MACD parameters locked: fast=12, slow=26, signal=9 | halt |
Full lock definitions: references/LOCKS.md
Top Anti-Patterns (15 total)
AP-REGTECH-001: Missing attribute initialization on data structuresAP-REGTECH-002: Self-loops in transaction graphs violate domain rulesAP-REGTECH-003: Unvalidated floating-point inputs cause runtime crashes
All 15 anti-patterns: references/ANTI_PATTERNS.md
Evidence Quality Notice
[QUALITY NOTICE] This crystal was compiled from blueprint finance-bp-060. Evidence verify ratio = 15.9% and audit fail total = 22. Generated results may have uncaptured requirement gaps. Verify critical decisions against source files (LATEST.yaml / LATEST.jsonl).
Reference Files
| File | Contents | When to Load |
|---|---|---|
| references/seed.yaml | V6+ 全量权威 (source-of-truth) | 有行为/决策争议时必读 |
| references/ANTI_PATTERNS.md | 15 条跨项目反模式 | 开始实现前 |
| references/WISDOM.md | 跨项目精华借鉴 | 架构决策时 |
| references/CONSTRAINTS.md | domain + fatal 约束 | 规则冲突时 |
| references/USE_CASES.md | 全量 KUC-* 业务场景 | 需要完整示例时 |
| references/LOCKS.md | SL-* + preconditions + hints | 生成回测/交易代码前 |
| references/COMPONENTS.md | AST 组件地图（按 module 拆分） | 查 API 时 |
Compiled by Doramagic crystal-compilation-v6.1 from finance-bp-060 blueprint at 2026-04-22T13:00:18.242568+00:00.
See human_summary.md for non-technical overview.
Usage Guidance
This skill is internally inconsistent: it says it's an AMLSim data generator but embeds trading/backtest references and runtime checks for the ZVT ecosystem that are unrelated. Before installing or invoking it: 1) Ask the publisher for provenance and clarify whether ZVT/backtest functionality is intentionally required. 2) Do not allow the agent to run precondition commands or pip installs automatically — run those manually in a sandbox if needed. 3) Inspect references/seed.yaml and SKILL.md yourself (or in an isolated VM) to confirm there are no hidden network endpoints or install recipes. 4) Refuse granting filesystem or environment-wide permissions (do not expose home dir or set ZVT_HOME) unless you understand and accept the scope. 5) If you need only AML data conversion, request a trimmed version that removes trading-related preconditions and the 'must re-read seed.yaml' behavioral mandate. If you want me to produce concrete remediation suggestions (exact lines to remove or sandboxed commands to run), say so and I will produce them.
Capability Analysis
Type: OpenClaw Skill
Name: aml-data-generator
Version: 0.3.3
The aml-data-generator skill bundle is a legitimate tool designed to generate synthetic Anti-Money Laundering (AML) transaction data and perform financial backtesting using the 'zvt' library. The bundle contains extensive operational instructions (Semantic Locks and Constraints) and execution protocols in seed.yaml and SKILL.md, which are strictly aligned with ensuring financial domain accuracy, such as preventing look-ahead bias (SL-02) and maintaining graph topology integrity (finance-C-001). While it includes shell-based preconditions and installation recipes for dependencies like numpy and networkx, these are standard requirements for its stated purpose. No evidence of malicious intent, data exfiltration, or harmful prompt injection was found.
Capability Tags
Capability Assessment
Purpose & Capability
The name/description focus on AMLSim synthetic transaction data, graph generation, log conversion and data merging; however the human_summary and SKILL.md include several references to a trading/quant stack (ZVT, A-share backtests, MACD/backtest use-cases). SKILL.md also declares requirements (Python 3.12+ / uv) in text but the registry metadata lists no required binaries/env. This mixing of AML simulation and trading frameworks is incoherent: a pure AML data generator should not need ZVT/backtest semantics.
Instruction Scope
The runtime instructions and seed.yaml execution_protocol direct the agent to run precondition checks that execute Python code (import zvt, check kdata, init zvt dirs), test writing to ~/.zvt, and suggest pip installing packages (zvt). Seed.yaml also requires the agent to re-read seed.yaml before behavioral decisions and to follow a multi-step execution protocol. These steps involve reading environment state, touching files, and potentially installing packages — actions outside the stated remit of converting/generating AMLSim data.
Install Mechanism
No formal install spec is declared (instruction-only), which minimizes explicit install-time risk. However SKILL.md and seed.yaml contain textual 'preconditions' that instruct runtime package installs (pip install zvt) and workspace/install_recipe steps. Runtime/self-initiated installs are not reflected in the registry metadata and could cause unexpected code to be pulled in when the agent executes the instructions.
Credentials
The skill declares no required env vars or credentials, but its instructions reference and test environment state (ZVT_HOME, ~/.zvt), and ask the agent to create and remove files in the user's zvt home. It also implies network access for pip installs. Requesting access to user filesystem and package installation is disproportionate for an AML data transformer and is not justified by the SKILL.md description.
Persistence & Privilege
always:false (good). The skill allows autonomous invocation (default), which by itself is normal. Seed.yaml includes a strong execution rule that agents MUST re-read seed.yaml and obey its protocol on 'any behavioral decision' — this is a behavioral persistence mechanism (not a platform-level always:true) that could subtly influence agent behavior across interactions. The skill does not explicitly modify other skills' configs, but the implicit requirement to re-read seed.yaml increases its behavioral footprint.
How to Use
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install aml-data-generator - After installation, invoke the skill by name or use
/aml-data-generator - Provide required inputs per the skill's parameter spec and get structured output
Version History
v0.3.3
v0.3.3: bilingual metadata injected. H1 shows AML 数据生成; tagline replaced with skill-specific Chinese hook; tags upgraded to Level 1-4.
v0.3.1
Remove install.sh — knowledge-only bundle. Host AI consumes directly from URL; no user-side installation needed. Fixes ClawHub suspicious flag.
v0.3.0
Doramagic crystal portfolio v0.3.0. Full 5-layer bp-009 standard. github.com/tangweigang-jpg/doramagic-skills
v0.2.0
Doramagic crystal portfolio v0.2.0. Full 5-layer bp-009 standard. github.com/tangweigang-jpg/doramagic-skills
v0.1.0
Doramagic crystal v0.2.0 portfolio. Compiled from finance blueprint. Source: github.com/tangweigang-jpg/doramagic-skills
Metadata
Frequently Asked Questions
What is Aml Data Generator?
生成符合AMLSim格式的合成交易数据，将交易日志转换为用于反洗钱检测系统测试的模拟数据集，支持按银行ID分割账户、合并多源输出并生成交易网络图。 It is an AI Agent Skill for Claude Code / OpenClaw, with 122 downloads so far.
How do I install Aml Data Generator?
Run "/install aml-data-generator" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is Aml Data Generator free?
Yes, Aml Data Generator is completely free, licensed under MIT-0. You can download, install and use it at no cost.
Which platforms does Aml Data Generator support?
Aml Data Generator is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).
Who created Aml Data Generator?
It is built and maintained by Tang Weigang (@tangweigang-jpg); the current version is v0.3.3.
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