BD a-stock-individual-analysis
A股个股深度分析 — 五模块框架（基本面/趋势量价/紫苏叶逻辑/近期消息/综合总结）。基于a-stock-data工具包的真实数据，覆盖订单可见度、毛利率方向、现金流匹配度、产能扩张动因、PE/PEG/EPS、均线量价、主力资金、技术壁垒、行业卡位、供需研判、近期新闻研报、解禁预警等维度。适用于个股深度研判、估值分析、买卖点判断。涉及"分析个股""深度研判""估值分析""紫苏叶""serenity""个股报告"时激活。
A股个股深度分析 —…
As a process D 48/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions
AnalyzerSoftware developmentData and analyticsAI and agentstype and topics are labelled automatically from the skill text
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: 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 "origin"
Process rating: all ten parameters 48/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Progress reporting. Says nothing while it works
- 20When it triggers. No condition that starts the skill
- 30Running it twice. 2 mutating operations with no state check
- 50Failures and branches. 0 branches, has a failure section
- 60Tools and files. Uses tools (python) that frontmatter does not declare
- 100Steps. 38 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 3126 tokens
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 211: enough signal without eating the budget
- +4Structure: 28 headings
- +3Step-by-step instructions: 38 items
- +4Has examples (2 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 71.
External checks
ClawHub: clean
This skill is a disclosed A-share stock analysis helper that fetches public market data, with some data-quality and privacy caveats but no evidence of malicious behavior.
LLM: benign (high) · VirusTotal: · 17 Jun 2026