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Climate Esg Investing

Name: Climate Esg Investing
Author: tangweigang-jpg

by Tang Weigang · GitHub ↗ · v0.3.3 · MIT-0

cross-platform ⚠ suspicious

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Install in OpenClaw

/install climate-esg-investing

Description

使用Fama-French因子模型进行气候ESG投资分析，支持月度股价数据下载、因子相关性计算、OLS回归诊断及显著性筛选，帮助用户构建因子组合和风险评估。

README (SKILL.md)

ESG 气候投资 (climate-esg-investing)

使用Fama-French因子模型进行气候ESG投资分析，支持月度股价数据下载、因子相关性计算、OLS回归诊断及显著性筛选，帮助用户构建因子组合和风险评估。

Pipeline

data_collection -> data_storage -> factor_computation -> target_selection -> trading_execution -> visualization

Top Use Cases (9 total)

Sector Stock Count and Significant Factor Regression Analyzer (`UC-101`)

Identifies how many stocks from an index fall into each sector and screens for stocks with statistically significant factor regression results based o Triggers: sector composition, significant regression, p-value screening

Factor Correlation Calculator (`UC-102`)

Computes correlations between different factors over time to understand factor relationship dynamics and potential multicollinearity issues Triggers: factor correlation, correlation matrix, factor relationships

OLS Regression with Diagnostic Statistics (`UC-103`)

Performs ordinary least squares regression on factor data with comprehensive diagnostic tests including Durbin-Watson, Jarque-Bera, and Breusch-Pagan Triggers: OLS regression, diagnostic tests, statistical tests

For all 9 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 (14 total)

AP-MACRO-DATA-001: SEC EDGAR Rate Limit Violation
AP-MACRO-DATA-002: Temporal Knowledge Graph Look-Ahead Bias
AP-MACRO-DATA-003: Technical Indicator Look-Ahead Bias via Missing Shift

All 14 anti-patterns: references/ANTI_PATTERNS.md

Evidence Quality Notice

[QUALITY NOTICE] This crystal was compiled from blueprint finance-bp-105. Evidence verify ratio = 3.3% and audit fail total = 20. 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-105 blueprint at 2026-04-22T13:00:49.775031+00:00. See human_summary.md for non-technical overview.

Usage Guidance

This skill appears to be a legitimate Fama‑French ESG analysis pipeline, but it omits declaring the runtime dependencies and credentials it actually expects. Before installing or running it: 1) Confirm you trust the source (homepage/source unknown). 2) Expect to need Python 3.12+, the 'zvt' ecosystem, and a writable ZVT_HOME (~/.zvt) — the skill's preconditions will attempt to import zvt and touch that directory. 3) Plan for database access (Postgres) and provider API credentials (joinquant/qmt) if you will use those data sources; do not put those secrets into an environment the skill hasn't declared. 4) Run in an isolated environment or sandbox first (container/VM) so the skill's Python checks and potential DB initializations cannot affect your primary system. 5) If you want to proceed, ask the author to (a) publish an install spec and explicit required env vars/credentials, (b) remove or document any filesystem writes, and (c) clarify whether the agent will run arbitrary python commands locally or only provide code snippets for the user to run.

Capability Analysis

Type: OpenClaw Skill Name: climate-esg-investing Version: 0.3.3 The skill bundle provides a structured framework for climate-related ESG investment analysis using Fama-French factor models and the ZVT quantitative library. The extensive instructions in SKILL.md and references/seed.yaml (including 'Semantic Locks' and 'Fatal Constraints') are designed to enforce econometric rigor, ensure regulatory compliance (e.g., preventing misleading performance claims), and avoid common financial modeling errors like look-ahead bias. No evidence of data exfiltration, malicious execution, or harmful prompt injection was found; the complex instruction set functions as a robust set of guardrails for the AI agent's analytical behavior.

Capability Tags

cryptocan-make-purchases

Capability Assessment

ℹ Purpose & Capability

The name/description (Fama‑French ESG analysis, data fetch, regression/backtest) aligns with the SKILL.md content and many use-cases. However, SKILL.md requires Python 3.12+, ZVT library, ZVT_HOME, and database access (Postgres) in its preconditions and components, but the registry metadata lists no required binaries, env vars, or credentials — an inconsistency indicating undeclared dependencies.

⚠ Instruction Scope

Runtime instructions (and seed.yaml) instruct the agent to re-read seed.yaml, run Python precondition checks that import zvt and touch ZVT_HOME, initialize databases, and execute pipeline steps. Those steps access local filesystem (touching ~/.zvt or ZVT_HOME), import local/third-party Python packages, and may attempt DB operations. While relevant to a backtest pipeline, these actions are not explicitly declared and grant the agent broad discretion to run Python commands and interact with local files/DBs.

✓ Install Mechanism

This is instruction-only (no install spec, no downloads, no code files to execute). That lowers install-time risk. Note seed.yaml contains an 'install_trigger' execution protocol, but no concrete install_recipes are present in the package/registry — another mismatch to be aware of.

⚠ Credentials

The skill references environment state (ZVT_HOME), Python packages (zvt, possibly psycopg2), and external data providers (eastmoney, joinquant, yfinance) but declares no required env vars, credentials, or primary credential. Database credentials and provider API keys (joinquant, qmt) are expected by the pipeline but are not declared — this under-declaration makes it unclear what secrets the skill will need or attempt to access.

✓ Persistence & Privilege

always is false and the skill does not request permission to persistently modify other skills or global agent settings. It does instruct write access checks (touching ZVT_HOME) but that is scoped to its own data directories per the preconditions; no evidence it tries to change other skills' configs.

How to Use

Make sure OpenClaw is installed (local or Docker)
Run the install command in chat: /install climate-esg-investing
After installation, invoke the skill by name or use /climate-esg-investing
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 ESG 气候投资; 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

Slug climate-esg-investing

Version 0.3.3

License MIT-0

All-time Installs 0

Active Installs 0

Total Versions 4

Frequently Asked Questions

What is Climate Esg Investing?

使用Fama-French因子模型进行气候ESG投资分析，支持月度股价数据下载、因子相关性计算、OLS回归诊断及显著性筛选，帮助用户构建因子组合和风险评估。 It is an AI Agent Skill for Claude Code / OpenClaw, with 70 downloads so far.

How do I install Climate Esg Investing?

Run "/install climate-esg-investing" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.

Is Climate Esg Investing free?

Yes, Climate Esg Investing is completely free, licensed under MIT-0. You can download, install and use it at no cost.

Which platforms does Climate Esg Investing support?

Climate Esg Investing is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).

Who created Climate Esg Investing?

It is built and maintained by Tang Weigang (@tangweigang-jpg); the current version is v0.3.3.

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