stock-analysis
- Repo stars 1,116
- Author updated Live
- Author repo skill
- Domain
- Other
- Compatible agents
-
- Claude Code
- Cursor
- Cline
- Codex
- Windsurf
- Gemini CLI
- +20
- Trust score
- 88 / 100 · community maintained
- Author / version / license
- @anbeime · no license declared
- Token usage
- Lean
- Setup complexity
- Plug-and-play
- External API key
- Not required
- Operating systems
- Unspecified (assume cross-platform)
- Runtime requirements
- No special requirements
- Permissions
-
- Read-only
- Write / modify
- Network behavior
- Local-only
- Install commands
- 26 variants
Profile is derived at build time from SKILL.md and install vectors. Subject to drift from author intent.
Heads up: 未限定 allowed-tools，默认拥有全部工具权限。
---
name: stock-analysis
description: requests>=2.28.0 用户：分析腾讯控股 00700.HK runs entirely locally. Works with Claude Code, Cursor, Cline…
category: other
runtime: no special runtime
---
# stock-analysis output preview
## PART A: Task fit
- Use case: requests>=2.28.0 用户：分析腾讯控股 00700.HK runs entirely locally. Works with Claude Code, Cursor, Cline and 23 more..
- Inputs: target material, constraints, expected output, and acceptance criteria.
- Evidence boundary: follow “任务目标 / 前置准备 / 操作步骤” and do not present inference as author intent.
## PART B: Execution result
- **01** The card summarizes the use case; runtime output centers on “requests>=2.28.0 用户：分析腾讯控股 00700.HK runs entirely locally. Works with Claude Code, Cursor, Cline and 23 more.”.
- **02** When the source has headings, the agent prioritizes “任务目标 / 前置准备 / 操作步骤” so the result follows the author’s structure.
- **03** Typical output includes task judgment, concrete steps, required commands or file edits, validation, and follow-up options.
- **04** Risk context follows the fingerprint: read files, write/modify files; mostly runs locally; usually needs no extra API key.
## Running Rules
- read files, write/modify files; mostly runs locally; usually needs no extra API key.
- Validate with a small sample before expanding scope.
- Return the result, validation criteria, and next iteration options. Choose the trigger
The source does not require a stable slash command. After installation, invoke the skill by name and describe the task.
Provide inputs and boundaries
Name target files or source material, expected output, forbidden changes, and whether network or shell access is allowed. Permission fingerprint: read files, write/modify files.
Validate with a small run
Start with a small task and check whether the result follows “任务目标 / 前置准备 / 操作步骤”. Inspect diffs, logs, previews, or tests before expanding scope.
Review before delivery
Confirm the final output includes a concrete result, evidence, and next action. If it stays generic, tighten inputs, boundaries, and acceptance criteria.
---
name: stock-analysis
description: requests>=2.28.0 用户：分析腾讯控股 00700.HK runs entirely locally. Works with Claude Code, Cursor, Cline…
category: other
source: anbeime/skill
---
# stock-analysis
## When to use
- requests>=2.28.0 用户：分析腾讯控股 00700.HK 任务目标 本 Skill 用于：对指定股票进行全面的技术分析，包括实时数据获取、技术指标计算、支撑位压力位分析、缺口识别分析 能力包含：实时行情获取、技术指标计算（…
- Use it when the task has clear inputs, repeatable steps, and validation criteria.
## What to provide
- Target material, scope, expected result, and forbidden changes.
- Whether network, commands, file writes, or external services are allowed.
## Execution rules
- Organize steps around “任务目标 / 前置准备 / 操作步骤” and keep inference separate from source facts.
- read files, write/modify files; mostly runs locally; usually needs no extra API key.
- Validate with a small sample before expanding the task.
## Output requirements
- Return the deliverable, key evidence, validation method, and next action.
- Mark missing information as unknown; do not invent commands, platforms, or dependencies. Evidence boundary and execution chain
The author source anchors workflow facts; repository files anchor sources and commands; Fluxly only adds fit, limitations, and quality judgment.
skill "stock-analysis" {
input -> user goal + target files + boundaries + acceptance criteria
context -> 任务目标 / 前置准备 / 操作步骤
rules -> SKILL.md triggers / order / output contract
runtime -> no special runtime | read files, write/modify files | mostly runs locally
guardrails -> usually needs no extra API key + small-sample validation + diff/log review
output -> copyable result + checklist + next iteration
}股票个股分析
任务目标
- 本 Skill 用于：对指定股票进行全面的技术分析，包括实时数据获取、技术指标计算、支撑位压力位分析、缺口识别分析
- 能力包含：实时行情获取、技术指标计算（均线、MACD、RSI）、支撑位压力位识别、缺口识别（向上/向下缺口及支撑压力作用）、趋势判断、未来走势预测
- 触发条件：用户提供股票代码并要求分析走势、预测未来、获取操作建议
前置准备
- 依赖说明：
requests>=2.28.0 numpy>=1.24.0 pandas>=2.0.0
操作步骤
标准流程
获取股票代码并验证
- 用户提供股票代码，如：000001（平安银行）、sh600000（浦发银行）、000001.SZ（深交所格式）
- 参考股票代码格式文档，确保代码格式正确
获取实时行情数据
- 调用
scripts/fetch_stock_data.py获取实时行情和历史K线数据 - 参数：
--stock_code: 股票代码--days: 获取历史数据天数（默认30天）
- 返回包含：当前价格、涨跌幅、成交量、历史K线数据
- 调用
计算技术指标和支撑位
- 调用
scripts/analyze_stock.py进行技术分析 - 参数：
--data_file: 上一步获取的数据文件路径
- 计算结果：
- MA5/MA10/MA20/MA60 均线
- MACD 指标
- RSI 指标
- 支撑位和压力位
- 缺口分析（向上缺口和向下缺口）
- 成交量分析
- 趋势判断
- 调用
分析当前走势
- 基于技术指标进行多维度分析：
- 均线排列（多头排列/空头排列/缠绕）
- MACD金叉死叉状态
- RSI超买超卖状态
- 成交量配合情况
- K线形态分析
- 缺口分析：
- 向上缺口：通常构成支撑位（回调时缺口上沿可能成为支撑）
- 向下缺口：通常构成压力位（反弹时缺口下沿可能成为压力）
- 缺口大小和位置对走势的影响
- 基于技术指标进行多维度分析：
预测未来3天走势
- 综合技术指标和趋势分析，对未来3天走势进行判断
- 考虑因素：趋势方向、支撑压力位、缺口支撑压力、成交量变化、市场情绪
- 给出概率评估：上涨/下跌/横盘的概率和强度
生成操作建议
- 根据分析结果和预测，给出明确的操作建议：
- 买入/持有/卖出/观望
- 建议的买入/卖出价格区间
- 止损位和止盈位设置
- 缺口相关的操作提示（如：向上缺口未回补前可作为支撑参考）
- 风险提示和注意事项
- 根据分析结果和预测，给出明确的操作建议：
资源索引
- 获取数据：见 scripts/fetch_stock_data.py（用途：获取股票实时行情和历史K线）
- 技术分析：见 scripts/analyze_stock.py（用途：计算技术指标和支撑位压力位）
- 代码格式：见 references/stock_code_format.md（用途：股票代码格式参考）
注意事项
- 股票市场存在风险，所有分析仅供参考，不构成投资建议
- 技术分析基于历史数据，不能保证未来表现
- 建议结合基本面分析和市场环境进行综合判断
- 实时数据可能存在延迟，请以实际交易数据为准
- 缺口分析要点：
- 向上缺口（跳空高开）：通常在回调时可能构成支撑，关注缺口是否回补
- 向下缺口（跳空低开）：通常在反弹时可能构成压力，关注缺口是否回补
- 缺口越大，其支撑或压力作用通常越强
- 成交量配合的缺口更具参考意义
- 近期缺口的参考价值高于远期缺口
- 必须在所有建议中包含风险提示
使用示例
示例1：A股股票分析
用户：分析000001平安银行
执行：
1. 调用 fetch_stock_data.py --stock_code 000001 --days 30
2. 调用 analyze_stock.py --data_file stock_data_000001.json
3. 基于分析结果生成走势预测和操作建议示例2：港股股票分析
用户：分析腾讯控股 00700.HK
执行：
1. 调用 fetch_stock_data.py --stock_code 00700.HK --days 30
2. 调用 analyze_stock.py --data_file stock_data_00700.HK.json
3. 生成分析报告和操作建议示例3：美股股票分析
用户：分析AAPL苹果公司
执行：
1. 调用 fetch_stock_data.py --stock_code AAPL --days 30
2. 调用 analyze_stock.py --data_file stock_data_AAPL.json
3. 提供全面的技术分析报告
Decide Fit First
Design Intent
How To Use It
Boundaries And Review