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。

Security Scan

The skill's description matches a Fama–French/ESG analysis pipeline, but its runtime instructions reference local Python libraries, filesystem paths, and DB preconditions that are not declared in the registry metadata, producing an incoherent footprint you should review before installing.

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.

What to consider before installing

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.

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

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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.