AD astock-analyst
A股实盘全流程量化分析助理。让AI像专业交易员一样盯盘、分析、复盘，真金白银的决策有数据撑腰。 解决的问题：不知道能不能买→五关论证完整逻辑链；不知道何时止损→跌破即出铁律； 担心被庄家割→OBV+不对称比识别出货；每天盯盘累→cron四段式全自动播报； 复盘不长进→结构化模板强制自我校验，错误永久沉淀。 武器库： 数据：push2实时行情、push2his历史K线、资金流向超大单、千股千评控盘度，curl直取无需登录。 指标：OBV、涨跌量不对称比、MACD、RSI、布林带、均线MA5/10/20/60、期望值公式、KDJ/CCI/WR，Python内联计算无需安装库。 形态：61种K线形态（射击之星/乌云盖顶/吞噬/晨暮星）、11种选股策略（停机坪/回踩年线/突破平台/海龟法则）、筹码分布解读。 决策：荐股五关论证、庄家行为识别手册、止损止盈条件单建议、仓位管理规则。 自动化：盘前/开盘/盘中/收盘cron完整JSON，可直接注册到OpenClaw。 容错：网络报错/JSON失败/价格单位陷阱/K线字段顺序陷阱，全部有处理模板。 触发词：分析股票、荐股、买不买、止损、复盘、盯盘、持仓更新、资金流向、技术指标、庄家、期望值、A股。 凭证说明（均为可选）： - 东方财富Cookie：仅用于提升API稳定性，短效（数天），勿硬编码，用完即弃 - datasaver Bearer Token：个人浏览器插件token，不要粘贴到共享环境 - 无以上凭证时，所有核心功能（行情/K线/指标/复盘）仍可正常使用
As a process D 43/100 · 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: 9. 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 43/100
- 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
- 30Running it twice. 2 mutating operations with no state check
- 60Tools and files. Uses tools (web, python) that frontmatter does not declare
- 100Steps. 52 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 662 tokens
- low 11 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 665: enough signal without eating the budget
- +4Structure: 22 headings
- +3Step-by-step instructions: 52 items
- +4Has examples (1 code blocks)
- +4Reference files are cited in the instructions (7 of 7)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 76.