BD quant-tools
学术导向量化研究工具集。包含7大核心库（因子分析、组合优化、AI增强、因果验证、衍生品定价、回测引擎、情感分析）和5大投研工具（VeighNa交易框架、Qlib AI投研、WTP高性能框架、AkShare数据接口、JupyterHub研究环境）。适用于策略研发、因子挖掘、论文复现、资产配置、API服务化等投研任务。触发词：量化、quant、因子、组合优化、因子分析、回测、交易框架、数据接口、学术研究。
As a process D 49/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
For the model run — optional
- Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
- A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.
Guard findings · 0
✓ No critical or high findings
Files scanned: 3. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
description-no-whendescription does not say WHEN to use the skill (no "use when")
Process rating: all ten parameters 49/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Failures and branches. Linear process with no failure handling
- 0Progress reporting. Says nothing while it works
- 20When it triggers. No condition that starts the skill
- 40Consistency. Frontmatter name (quant-tools) differs from the folder (jiadong-quant-tools)
- 100Tools and files. No external tools needed
- 100Steps. 84 steps
- 100Execution cost. Instruction body is 979 tokens
- 100Running it twice. No mutating operations
Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.
Quality signals
- +5Description has no quoted example phrases that should trigger the skill
- +4Description does not say when NOT to use the skill (false activations)
- +3Output format is not stated: the model decides each time
- -266 emoji in the instructions: noise for the model
- +1No license
- +2Single-language instructions
- +3Description length 202: enough signal without eating the budget
- +4Structure: 32 headings
- +3Step-by-step instructions: 84 items
- +4Has examples (8 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 70.
External checks
ClawHub: clean
This appears to be a quantitative finance research skill with expected market-data and backtesting capabilities, but users should treat its trading-related workflows carefully.
LLM: benign (medium) · VirusTotal: · 29 May 2026