BD stock-analysis-expert
专业A股九维分析体系 - 政策面+基本面+技术面+资金面全面分析，支持ESG评级、北向资金追踪、期权波动率分析 适用市场：A股（沪深两市） 版本：V3.0 九维分析体系： - 第0层：信息质量审计 - 第1层：宏观环境（水温判断） - 第2层：政策面分析 - 第3层：行业逻辑（赛道选择） - 第4层：公司基本面（4M+模型） - 第5层：竞争格局分析 - 第6层：技术面（辅助层） - 第7层：资金面（催化剂） - 第8层：行为金融层 核心功能： - 多维度财务指标分析 - 技术指标组合信号 - 资金流向追踪 - 风险评估模型 - 估值对比分析 - 实时买卖信号 输出：专业股票分析报告（含投资建议）
专业A股九维分析体系 - 政策面+基本面+技术面+资金面全面分析，支持ESG评级、北向资金追踪、期权波动率分析 适用市场：A股（沪深两市） 版本：V3.0 九维分析体系： - 第0层：信息质量审计 - 第1层：宏观环境（水温判断） - 第2层：政策面分析 - 第3层：行业逻辑（赛道选择） -…
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.
- 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: 2. 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") - note
frontmatter-keyunknown frontmatter key "categories" - note
frontmatter-keyunknown frontmatter key "homepage" - note
frontmatter-keyunknown frontmatter key "repository"
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 (stock-analysis-expert) differs from the folder (stock-analysis-skill-2)
- 100Tools and files. No external tools needed
- 100Steps. 26 steps
- 100Execution cost. Instruction body is 153 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
- +2Single-language instructions
- +3Description length 306: enough signal without eating the budget
- +4Structure: 7 headings
- +3Step-by-step instructions: 26 items
- +4Has examples (1 code blocks)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 70.