BD wen-cai
同花顺问财全功能合并技能。集成行情数据查询、指数数据查询、财务数据查询、基本资料查询、事件数据查询、公司经营数据查询、机构研究与评级查询、研报搜索、公告搜索、新闻搜索、问财选A股、问财选板块共12个子功能。只需配置 IWENCAI_BASE_URL 和 IWENCAI_API_KEY 两个环境变量，无需任何外部库依赖（仅使用 Python 标准库）。当用户询问以下任意场景时必须使用此技能：股票价格/涨跌幅/成交量/行情数据、指数行情/点位/成分股、财务指标/营收/净利润/ROE/负债率/现金流/毛利率、公司基本信息/上市日期/行业/费率、业绩预告/增发配股/股权质押/限售解禁/机构调研/监管函/重大事件、主营业务/主要客户/供应商/参控股公司/重大合同、研报评级/目标价/机构评级/业绩预测/ESG/券商金股、研究报告搜索/投研决策、公告查询/年报/分红/回购/资产重组、财经新闻/行业动态/政策资讯、A股股票筛选/选股/板块筛选。
As a process D 46/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions
What is at stake
Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.
Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.
The instructions or scripts send data out: environment variables, keys, file contents, chat history. You may never notice, because the agent performs the upload as "part of the task".
If the upload is not needed for the task, remove it: catalogs flag such skills and delist them. If it is needed, name the destination explicitly, say what leaves the machine, and give the user a switch.
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- 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 · 1
✓ No critical or high findings
Medium and low: 1
-
medium Exfiltration
net-redirectable-api-keyscripts/cli.py:22Helper sends the API key to a host configured by an environment variable — the key can be redirected to another serverAPI key + configurable base URL from environment
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 46/100
- 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
- 60Tools and files. Uses tools (python) that frontmatter does not declare
- 100Steps. 9 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1182 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
- +1No license
- +2Single-language instructions
- +3Description length 420: enough signal without eating the budget
- +4Structure: 15 headings
- +3Step-by-step instructions: 9 items
- +4Has examples (4 code blocks)
- +3All 1 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 75.