BF mx_financial_assistant
基于东方财富权威金融数据库，打造覆盖"数据-资讯-知识-分析-决策"全链条的智能问答服务。通过自然语言交互， 为用户提供从基础金融知识科普到专业投资研究支持的全方位解决方案，实现"一问即达"的高效金融信息获取体验。 涵盖七大核心能力：全市场金融数据实时查询（A港美股、基金、债券）、全网财经资讯智能检索（公告、研报、新闻、政策）、 全球宏观经济数据追踪（国民经济、货币财政、产业就业）、多维度智能选股选基（技术面、基本面、消息面）、 专业金融百科知识问答、深度金融市场分析（趋势、资金、情绪）、以及实时热点事件解读（政策、公告、行业变革）。 适配多元化应用场景，满足机构投资者在投资研究、资产配置、风险管理、策略回测等方面的专业需求， 同时支持个人用户的理财决策、知识学习、行情监控与投资组合管理。 当用户提出自然语言问题并希望获得智能回答时使用此技能，包括但不限于： "帮我查一下"、"请问"、"分析一下"、"选股"、"推荐股票"、"XX怎么样"、"XX是什么"、 "解释一下"、"总结一下"、"最新政策"、"宏观数据"、"市场行情"、"基金筛选"等问答类请求。 支持标准模式和深度思考模式（deepThink）。 此技能不适用于需要生成完整报告的场景（如行业跟踪报告、首次覆盖报告），这些场景应使用对应的报告生成技能。
As a process F 31/100 · Will not run — References files that are not bundled: {jumpUrl}
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- The text references files that are not there: add them or drop the references.
- 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
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") - warning
missing-refreference to a missing file: {jumpUrl}
Process rating: all ten parameters 31/100
- 0Tools and files. 1 referenced file(s) missing: {jumpUrl}
- 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 (mx_financial_assistant) differs from the folder (mx-mx-financial-assistant)
- 100Steps. 21 steps
- 100Execution cost. Instruction body is 1130 tokens
- 100Running it twice. No mutating operations
- low 10 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)
- +3Output format is not stated: the model decides each time
- +1No license
- +2Single-language instructions
- +3Description length 561: enough signal without eating the budget
- +4Structure: 23 headings
- +3Step-by-step instructions: 21 items
- +4Has examples (4 code blocks)
- +3All 1 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 64.