BC ashare-financial-report-analysis
A股上市公司财报结构化拆解系统。基于16节固定模板，对季报/年报进行一手数据提取、自算核验、跨季度可比的结构化分析。数据源优先级与racing-quant-ai一致（Tushare > akshare > baostock > 东方财富API）。触发词：财报拆解，财报分析，季报分析，年报分析，财报拆解模板，earnings report，财报解读。
A股上市公司财报结构化拆解系统。基于16节固定模板，对季报/年报进行一手数据提取、自算核验、跨季度可比的结构化分析。数据源优先级与racing-quant-ai一致（Tushare > akshare > baostock >…
As a process C 52/100 · Has gaps — 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 · 2
✓ No critical or high findings
Medium and low: 2
-
low Secrets in code
secret-high-entropy-tokenSKILL.md:45High-entropy token-like string (may be an id, hash or a credential) (documentation table row)| 前十大股东 | `mcp_…ers` | ts_code, period | 持股数/比例/变动 |
table -
low Secrets in code
secret-high-entropy-tokenSKILL.md:46High-entropy token-like string (may be an id, hash or a credential) (documentation table row)| 前十大流通股东 | `mcp_…ers` | ts_code, period | 流通股持仓 |
table
Files scanned: 6. 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 52/100
- 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
- 70Execution cost. Instruction body is 4151 tokens
- 100Tools and files. No external tools needed
- 100Steps. 72 steps
- 100Consistency. Name and required fields are in place
- 100Running it twice. No mutating operations
- low 16 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
- -42 reference files, but SKILL.md never points to them: the model will not open them
- +1No license
- +2Single-language instructions
- +3Description length 175: enough signal without eating the budget
- +4Structure: 37 headings
- +3Step-by-step instructions: 72 items
- +4Has examples (5 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 68.