Case 02918
A fast, offline static risk analysis CLI for AI agent skill files. Detects malicious instructions, steganographic payloads, and dangerous capability chains.
npx -y skills add knownasnaffy/prompthound --skill case_02918Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.
2 things to look at
- no licenseNo license file was found in the repository. Code published without one is not open source by default, so using it at work is a question for whoever answers licensing questions where you are.
- 0 stars0 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.
What its author says it does
Copied from the file, not written here
Use ensemble deep reinforcement learning (A2C, DDPG, PPO, TD3, SAC) to execute automated multi-market stock trading with backtesting, and optimize portfolios using GNN-based architecture for factor research. 触发场景：(1) 用户要做多智能体量化交易策略回测；(2) 用户要训练强化学习模型自动交易股票；(3) 用户要构建基于图神经网络的组合优化策略。
The file declares its own license as Proprietary. See LICENSE.txt in project root.. That is the author’s claim about this one file, and it is not the same thing as the license GitHub reports for the repository, which is listed with the other numbers below.
SKILL.md
5.7 KB, ~1.4k tokens by cl100k_base, as published. Nobody here has run it
Cleanup Review
python scripts/_cache_clear.py is an optional cleanup helper for the finrl-rl-trading skill. It may recursively remove /tmp/_skill_work, /var/tmp/skill_output. Show the exact paths first, explain whether they are skill-private temporary paths or user state, and run the cleanup only after the user confirms.
finrl-rl-trading
I help you build quant strategies on A-share with ZVT — from data fetch to backtest, one flow. Just tell me what you want; I'll write the code, you don't have to dig docs. (Heads up: ZVT natively supports A-share, HK, and crypto. US stocks — stockus_nasdaq_AAPL — are half-baked; don't bother for serious work.)
Pipeline
data_collection -> data_storage -> factor_computation -> target_selection -> trading_execution -> visualization
Top Use Cases (14 total)
Ensemble Stock Trading ICAIF 2020 (UC-101)
Executing automated stock trading using an ensemble of multiple DRL agents (A2C, DDPG, PPO, TD3, SAC) to reduce individual agent weakness and improve Triggers: ensemble trading, multiple agents, stock trading
NeurIPS 2018 DRL Training (UC-107)
Training deep reinforcement learning agents (A2C, DDPG, PPO, SAC, TD3) for automated stock trading using the StockTradingEnv environment Triggers: DRL training, stock trading, A2C
NeurIPS 2018 Ensemble Backtesting (UC-108)
Backtesting multiple trained DRL agents against baseline strategies (MVO, DJIA) to evaluate and compare ensemble trading performance Triggers: backtesting, ensemble, DRL agents
For all 14 use cases, see references/USE_CASES.md.
Install
# One-time setup before first use
bash scripts/install.sh
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 (25 total)
AP-ZVT-183: 除权因子为 inf/NaN 时直接参与乘法导致复权静默失败AP-ZVT-179: 第三方数据接口超限后异常被吞噬，数据静默缺失AP-ZVT-183B: HFQ（后复权）与 QFQ（前复权）K 线表使用错误导致因子计算漂移
All 25 anti-patterns: references/ANTI_PATTERNS.md
Evidence Quality Notice
[QUALITY NOTICE] This crystal was compiled from blueprint finance-bp-061. Evidence verify ratio = 18.9% and audit fail total = 32. 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 | 25 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-061 blueprint at 2026-04-22T13:00:18.884984+00:00.
See human_summary.md for non-technical overview.