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Stock Entry Analyzer

v1.0.2

多指标股票入场分析工具。基于乖离率 (BIAS) 为核心，结合均线/MACD/RSI/成交量/资金流/估值等 7 大类指标，综合判断股票或基金是否适合买入。使用 stock-price-query、stock-market-pro、eastmoney-tools 获取数据，输出检查清单和综合评分。当用户询问"XX...

⭐ 0· 58·0 current·0 all-time
byLiuLi@liuli4
MIT-0
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LicenseMIT-0 · Free to use, modify, and redistribute. No attribution required.
Security Scan
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Benign
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Benign
high confidence
✓
Purpose & Capability
Name/description (multi-indicator entry analysis) align with the included files: SKILL.md, indicator references, scoring rules, and a single helper script (calc_bias.py). The skill declares it will use other stock-* skills for data — that is appropriate for this purpose.
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Instruction Scope
Runtime instructions are scoped to extracting a symbol, calling the listed stock-* data skills, running scripts/calc_bias.py, and assembling a report. The SKILL.md also mentions scheduled reports (10:00/14:00) — scheduling/autonomous invocation is allowed by the platform but worth noting. One minor oddity: the documented and implemented BIAS formula uses ln(close)/EMA — this is unusual mathematically (log divided by EMA) but is applied consistently in the docs and script; verify the formula is intentionally correct for the author's method.
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Install Mechanism
Instruction-only skill with no install spec and a single small script; nothing is downloaded or written during installation.
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Credentials
No environment variables, credentials, or config paths are requested. The skill relies on other stock-* skills for data, which may themselves require credentials — verify those dependencies separately.
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Persistence & Privilege
always:false (no forced inclusion). The skill can be invoked autonomously (platform default) and declares scheduled report times; if you do not want autonomous scheduled runs, ensure your agent's skill invocation policy or scheduler settings reflect that preference.
Assessment
This skill appears internally coherent for stock entry analysis. Before installing: 1) Confirm the dependent skills (stock-price-query, stock-market-pro, eastmoney-tools, etc.) are trusted and available, since this skill calls them for data. 2) Review the calc_bias.py logic (and the BIAS formula) to ensure it matches your intended math — the formula uses ln(close) divided by EMA which is unusual. 3) If you don't want automated scheduled reports, adjust agent/scheduler settings because the SKILL.md includes timed report triggers. 4) Treat outputs as informational only (the README includes a risk disclaimer) and never consider this a substitute for professional financial advice. 5) If you need stronger assurances, request the maintainer/source and confirm data endpoints used by the dependent stock-* skills.

Like a lobster shell, security has layers — review code before you run it.

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License

MIT-0
Free to use, modify, and redistribute. No attribution required.
Termshttps://spdx.org/licenses/MIT-0.html

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