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Insurance Loss Reserving
by
Tang Weigang
· GitHub ↗
· v0.3.2
· MIT-0
106
Downloads
0
Stars
0
Active Installs
3
Versions
Install in OpenClaw
/install insurance-loss-reserving
Description
用 chainladder-python 做精算损失准备金估算：从历史理赔三角到 IBNR 准备金、 尾部参数拟合。支持再保险 / 巨灾 / 一般责任险多产品线。
README (SKILL.md)
保险损失准备金 (insurance-loss-reserving)
用 chain ladder 方法从历史理赔三角估算 IBNR 准备金——再保险、巨灾、 一般责任险都能跑。
Pipeline
data_collection -> data_storage -> factor_computation -> target_selection -> trading_execution -> visualization
Top Use Cases (0 total)
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-INSURANCE-001: Implicit numeric format assumptions without validationAP-INSURANCE-002: Triangle axis construction with invalid temporal orderingAP-INSURANCE-003: Cumulative/incremental triangle representation misuse
All 15 anti-patterns: references/ANTI_PATTERNS.md
Evidence Quality Notice
[QUALITY NOTICE] This crystal was compiled from blueprint finance-bp-063. Evidence verify ratio = 56.5% and audit fail total = 15. 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-063 blueprint at 2026-04-22T13:00:20.366204+00:00.
See human_summary.md for non-technical overview.
Usage Guidance
This skill package is internally inconsistent: the label says 'insurance loss reserving' but most runtime instructions and reference files are for a ZVT quant-trading pipeline and include commands that install packages and manipulate ~/.zvt. Before installing or enabling this skill: 1) Ask the publisher to clarify the intended purpose and provide a corrected SKILL.md (actuarial vs trading). 2) Require an explicit install spec that lists exact packages and trusted sources (no ad-hoc pip at runtime). 3) Do NOT provide broker/API credentials (joinquant/qmt) or cloud secrets until the skill explicitly declares them and explains why they are needed. 4) If you must evaluate, run the skill in an isolated sandbox (container/VM) with no access to your real accounts or home directory. 5) Ask the author to declare required environment variables and show exactly what filesystem changes the skill will make (e.g., writes to ~/.zvt). 6) If the skill is intended to trade/execute orders, treat it with high caution — require full proof of broker integration, signing, and a safe test mode. Providing any credentials or allowing automatic installs before these clarifications would be risky.
Capability Analysis
Type: OpenClaw Skill
Name: insurance-loss-reserving
Version: 0.3.2
The skill bundle exhibits a profound functional discrepancy: while the metadata, title, and anti-patterns (references/ANTI_PATTERNS.md) describe an 'insurance loss reserving' actuarial tool, the core execution logic, semantic locks (references/LOCKS.md), and human summary (human_summary.md) are entirely focused on the ZVT quant trading framework for A-shares and crypto. This 'Frankenstein' architecture—mixing actuarial constraints with stock trading instructions—suggests a high-risk or poorly constructed bundle. Although no explicit evidence of data exfiltration or malicious payloads was found, the use of prescriptive prompt instructions to manage trading cycles and execute shell commands (via uv/pip) within a fundamentally mislabeled package warrants a suspicious classification.
Capability Tags
Capability Assessment
Purpose & Capability
Name and top description say 'chainladder-python' for actuarial loss reserving, but SKILL.md, human_summary.md and seed.yaml are dominated by ZVT quant-trading concepts (data recorders, MACD, trading_execution, entity_id formats) and semantic locks for trading. This mismatch suggests either a mislabeled/merged artifact or intentional misdirection: requested capabilities (trading, data recorders, pip-install of zvt, next-bar execution) do not align with a pure actuarial reserving tool.
Instruction Scope
Although there is no code, SKILL.md and seed.yaml instruct the agent to run runtime checks and commands (e.g., python import checks, pip install zvt, run zvt.recorders, read references/seed.yaml) and require reading many local reference files. The instructions reference system paths (ZVT_HOME, ~/.zvt) and tell the agent to run tooling that may install packages, create directories, and fetch external market data — which is beyond a simple 'compute loss reserves' skill and not justified by the declared metadata.
Install Mechanism
There is no formal install spec (instruction-only), but seed.yaml and SKILL.md explicitly instruct running installation commands (pip install zvt) and running recorders. That means runtime will attempt to install third-party packages with no vetted install recipe declared in the skill manifest. Instruction-driven installs are higher-risk because they run arbitrary package installs at runtime and are not surfaced in requires.install.
Credentials
The skill declares no required env vars or credentials but instructions reference and require ZVT_HOME and suggest using data providers that typically need credentials (joinquant, qmt). The skill also references writing to ~/.zvt and running recorders that may require network credentials — asking for or using such secrets is not declared in metadata, so environment/credential needs are under-specified and disproportionate to the manifest.
Persistence & Privilege
always:false (good) but the execution protocol in seed.yaml mandates re-reading seed.yaml and running precondition commands that can create directories, touch files (~/.zvt), and run pip installs. The skill would therefore modify host state at runtime (install packages, initialize data directories) despite lacking an explicit install step and without declaring elevated privileges. Combined with trading-related semantic locks (which imply external execution semantics), this broad host interaction increases risk.
How to Use
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install insurance-loss-reserving - After installation, invoke the skill by name or use
/insurance-loss-reserving - Provide required inputs per the skill's parameter spec and get structured output
Version History
v0.3.2
v0.3.2: inject bilingual metadata per naming spec. H1 now shows 保险损失准备金 + slug; tagline and description replaced with CTO-authored copy (fixes tagline pollution for non-ZVT skills).
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
Metadata
Frequently Asked Questions
What is Insurance Loss Reserving?
用 chainladder-python 做精算损失准备金估算：从历史理赔三角到 IBNR 准备金、 尾部参数拟合。支持再保险 / 巨灾 / 一般责任险多产品线。 It is an AI Agent Skill for Claude Code / OpenClaw, with 106 downloads so far.
How do I install Insurance Loss Reserving?
Run "/install insurance-loss-reserving" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is Insurance Loss Reserving free?
Yes, Insurance Loss Reserving is completely free, licensed under MIT-0. You can download, install and use it at no cost.
Which platforms does Insurance Loss Reserving support?
Insurance Loss Reserving is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).
Who created Insurance Loss Reserving?
It is built and maintained by Tang Weigang (@tangweigang-jpg); the current version is v0.3.2.
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