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P2p Lending Data
by
Tang Weigang
· GitHub ↗
· v0.3.3
· MIT-0
109
Downloads
0
Stars
0
Active Installs
3
Versions
Install in OpenClaw
/install p2p-lending-data
Description
验证 Frappe Lending 贷款模块核心流程，包括贷款申请创建、放款计划生成、还款处理及结清退款的自动化测试能力。
Usage Guidance
This skill is internally inconsistent: its name/description say it's for Frappe Lending tests, but many instructions, preconditions and the human summary talk about ZVT quant backtests, data recorders and trading semantic locks. Before installing or using it, ask the publisher: 1) Which domain is this for (lending tests or ZVT trading/backtest)? 2) Provide a clear, minimal list of required binaries/env vars (Python version, zvt, ZVT_HOME, any recorder credentials) and an explicit install recipe if the skill needs to install packages. 3) Confirm whether the skill will run Python commands/recorders on your host and whether it will write to ~/.zvt or other directories. 4) If you only want lending test automation, request a trimmed SKILL.md that removes ZVT/backtest triggers and semantic locks. Because of the ambiguity, avoid granting broad runtime privileges or running it unattended until the author clarifies these inconsistencies.
Capability Analysis
Type: OpenClaw Skill
Name: p2p-lending-data
Version: 0.3.3
The bundle exhibits a critical functional discrepancy: while titled and described as a 'P2P Lending' and 'Frappe' testing tool, the underlying execution logic, pipeline, and 'Doraemon' persona are entirely focused on quantitative trading using the 'zvt' library for A-shares. This mismatch between the stated purpose in SKILL.md and the operational logic in seed.yaml creates a high risk of the AI agent performing unintended financial modeling or trading actions. The bundle enforces strict 'Semantic Locks' (SL-01 to SL-12) and 'Fatal Constraints' (finance-C-*) that steer agent behavior toward trading, which, combined with the functional confusion, makes the bundle deceptive and unreliable.
Capability Tags
Capability Assessment
Purpose & Capability
The skill name/description claim the goal is validating Frappe Lending loan flows, but large portions of SKILL.md and human_summary refer to ZVT quant backtesting, markets, data recorders (eastmoney/joinquant/akshare), trading pipelines and semantic locks for trading. Metadata also advertises compatibility with Doramagic host and Python 3.12+ with 'uv' package manager. The registry requirements list no binaries/env/configs, which contradicts the SKILL.md preconditions that require Python packages (zvt) and data directories. These cross-domain and metadata contradictions are disproportionate and unclear.
Instruction Scope
The SKILL.md runtime instructions require re-reading seed.yaml, consulting many local reference files, and enforcing semantic locks (trading rules). Preconditions reference running python commands to check for zvt and data directories and instruct the agent to run recorders and installers. That expands the agent's runtime actions beyond simple lending-test descriptions — it instructs the agent to execute environment checks, run recorders, and follow trading/backtest execution triggers. The instructions are prescriptive and mix unrelated file-read and execution steps (lending tests vs market backtests).
Install Mechanism
There is no install spec (instruction-only), which reduces risk of arbitrary code downloads. However the seed.yaml/execution_protocol claims install_trigger steps (resources.host_adapter.install_recipes[]) that are not present in the registry install metadata, creating an inconsistency: the skill expects installation actions but provides no install recipe.
Credentials
The skill declares no required env vars, binaries, or config paths in the registry, yet SKILL.md and seed.yaml expect Python (3.12+), a 'uv' package manager, and the zvt Python package and ZVT_HOME data directories. That mismatch means the skill will attempt actions (import zvt, check ZVT_HOME, run recorders) without declaring the required environment; it's disproportionate and confusing but not explicitly requesting secrets or external credentials.
Persistence & Privilege
always is false and there is no install script included, so the skill does not request permanent automatic inclusion or declared privileged persistence. Autonomous invocation is allowed (default) but not combined with 'always:true' or broad credential requests.
How to Use
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install p2p-lending-data - After installation, invoke the skill by name or use
/p2p-lending-data - 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 P2P 贷款测试; 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
Metadata
Frequently Asked Questions
What is P2p Lending Data?
验证 Frappe Lending 贷款模块核心流程，包括贷款申请创建、放款计划生成、还款处理及结清退款的自动化测试能力。 It is an AI Agent Skill for Claude Code / OpenClaw, with 109 downloads so far.
How do I install P2p Lending Data?
Run "/install p2p-lending-data" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is P2p Lending Data free?
Yes, P2p Lending Data is completely free, licensed under MIT-0. You can download, install and use it at no cost.
Which platforms does P2p Lending Data support?
P2p Lending Data is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).
Who created P2p Lending Data?
It is built and maintained by Tang Weigang (@tangweigang-jpg); the current version is v0.3.3.
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