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Sphinx Documentation Configuration (
Credit Lgd Model
by
Tang Weigang
· GitHub ↗
· v0.3.3
· MIT-0
76
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4
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Install in OpenClaw
/install credit-lgd-model
Description
构建并训练 LGD（违约损失率）机器学习模型，支持基于历史违约数据的信用风险量化评估与预测。
README (SKILL.md)
信用违约损失模型 (credit-lgd-model)
构建并训练 LGD（违约损失率）机器学习模型，支持基于历史违约数据的信用风险量化评估与预测。
Pipeline
data_collection -> data_storage -> factor_computation -> target_selection -> trading_execution -> visualization
Top Use Cases (1 total)
Sphinx Documentation Configuration (UC-101)
This file configures the Sphinx documentation builder for the openLGD project, setting up project metadata, version information, and path configuratio Triggers: documentation, sphinx, configuration
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 (14 total)
AP-CREDIT-RISK-001: Empty DataFrame passed to bucketing pipelineAP-CREDIT-RISK-002: Multi-dimensional target array causing WoE shape mismatchAP-CREDIT-RISK-003: OptimalBucketer receiving high-cardinality numerical features
All 14 anti-patterns: references/ANTI_PATTERNS.md
Evidence Quality Notice
[QUALITY NOTICE] This crystal was compiled from blueprint finance-bp-112. Evidence verify ratio = 21.0% and audit fail total = 23. 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 | 14 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-112 blueprint at 2026-04-22T13:00:54.441302+00:00.
See human_summary.md for non-technical overview.
Usage Guidance
This package mixes credit LGD model artifacts with trading/backtesting (ZVT/MACD/Sphinx) and directs the agent to run Python checks, pip installs, and create files under ~/.zvt. Before installing or invoking: 1) Confirm whether you actually want a trading/backtest workflow or a pure LGD credit model — the skill appears to attempt both. 2) If you proceed, run it in an isolated environment (VM/container) because the skill may install packages and write to your home directory. 3) Inspect seed.yaml and references/LOCKS.md yourself to verify the enforced semantic locks (trading rules) are acceptable. 4) Ask the author/owner to clarify scope (remove trading/backtest prompts if only LGD is desired) or provide a trimmed skill that only contains credit-modeling instructions. 5) If you lack trust or need least privilege, avoid allowing the agent to run the precondition install commands automatically; perform those steps manually after review.
Capability Analysis
Type: OpenClaw Skill
Name: credit-lgd-model
Version: 0.3.3
The bundle provides a framework for building federated Loss Given Default (LGD) credit risk models using scikit-learn and Flask. It contains extensive security-focused constraints (e.g., finance-C-085 and finance-C-106 in seed.yaml) that explicitly instruct the AI agent to avoid Remote Code Execution (RCE) vulnerabilities by disabling Flask debug mode and using safe YAML loading. The architecture focuses on local data processing and parameter averaging, which is consistent with the stated purpose of federated learning, and no evidence of data exfiltration or malicious intent was found.
Capability Tags
Capability Assessment
Purpose & Capability
The name/description state 'LGD (违约损失率) machine learning model' for credit risk, but SKILL.md, human_summary, seed.yaml and many reference files repeatedly reference ZVT, backtesting, trading pipelines (MACD, trading_execution) and Sphinx documentation. Semantic locks and preconditions include trading rules (execute sell before buy, next-bar execution, MACD param locks) that are unrelated to a pure LGD credit model. This mixed purpose is incoherent: a credit-LGD modelling skill normally would not ask about markets/strategies or enforce trading semantics unless it's intentionally a dual-purpose blueprint.
Instruction Scope
Although instruction-only, the SKILL.md and referenced files direct the agent to run precondition checks that execute Python commands (e.g. 'python3 -c "import zvt..."'), to install packages via pip if checks fail, and to create/write files under a ZVT_HOME (~/.zvt). It also instructs the agent to reload seed.yaml before decisions and to follow an execution protocol. These runtime actions access the host filesystem and package manager and go beyond merely describing modelling steps; they also include trading/backtest question prompts. The instructions are not strictly scoped to LGD estimation and give broad discretion to run installs and filesystem touches.
Install Mechanism
There is no formal install spec (lowest static install risk). However, SKILL.md and seed.yaml explicitly instruct running pip installs and zvt initialization as preconditions if checks fail. Those commands would pull packages from external registries at runtime even though no install recipe is declared in the registry metadata — this is an implicit install path the agent may perform.
Credentials
The skill declares no required environment variables or credentials (good). Still, runtime preconditions reference ZVT_HOME and instruct touching files under that directory (~/.zvt) and checking writability. That implies the agent will read/write local configuration directories. No secrets are requested, but the skill expects filesystem access which is proportionally reasonable for a ZVT/backtest setup but unexpected for a pure LGD model.
Persistence & Privilege
The skill does not request always:true or other elevated persistent privileges. It is user-invocable and allows model invocation (normal). It does not declare modifications to other skills or system-wide agent settings in the metadata. Seed.yaml indicates the host should reload it during execution, but that is a documentation/execution protocol instruction, not a registry-level persistence flag.
How to Use
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install credit-lgd-model - After installation, invoke the skill by name or use
/credit-lgd-model - 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 信用违约损失模型; 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
Metadata
Frequently Asked Questions
What is Credit Lgd Model?
构建并训练 LGD（违约损失率）机器学习模型，支持基于历史违约数据的信用风险量化评估与预测。 It is an AI Agent Skill for Claude Code / OpenClaw, with 76 downloads so far.
How do I install Credit Lgd Model?
Run "/install credit-lgd-model" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is Credit Lgd Model free?
Yes, Credit Lgd Model is completely free, licensed under MIT-0. You can download, install and use it at no cost.
Which platforms does Credit Lgd Model support?
Credit Lgd Model is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).
Who created Credit Lgd Model?
It is built and maintained by Tang Weigang (@tangweigang-jpg); the current version is v0.3.3.
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