BD quant_trading-skills
获取股票/基金等金融标的的量化数据，包括行情数据、财务数据、资金流向数据和舆情数据。支持单只股票查询和批量数据拉取。当用户需要查询股票行情、财务指标、资金流向或相关舆情信息时使用此技能。
As a process D 41/100 · 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: 15. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
name-formatname should be kebab-case (lowercase letters, digits, hyphens) - warning
description-no-whendescription does not say WHEN to use the skill (no "use when") - note
frontmatter-keyunknown frontmatter key "parameters" - note
frontmatter-keyunknown frontmatter key "output_format" - note
frontmatter-keyunknown frontmatter key "trigger"
Process rating: all ten parameters 41/100
- 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_trading-skills) differs from the folder (quant-trading-skills)
- 60Tools and files. Uses tools (python) that frontmatter does not declare
- 100Steps. 63 steps
- 100Execution cost. Instruction body is 2440 tokens
- 100Running it twice. No mutating operations
- low 13 top-level sections: this looks like several domains in one skill
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)
- +3Description length 93: 120–800 characters recommended
- +3Output format is not stated: the model decides each time
- -32 of 3 scripts are never mentioned in SKILL.md
- +2Single-language instructions
- +4Structure: 38 headings
- +3Step-by-step instructions: 63 items
- +4Has examples (25 code blocks)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 59.
External checks
ClawHub: suspicious
This finance data skill is mostly coherent, but it needs Review because batch modes can write to caller-chosen local paths and one advertised sentiment feature returns mock news as if it were fetched data.
LLM: suspicious (high) · VirusTotal: · 29 May 2026