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shaoxing-xie/openclaw-data-china-stock

fund-flow-analyst

资金流分析师，聚焦主力、北向、板块与个股的多层资金结构分析。

First seen Jun 15, 2026

Installation

$ npx skills add shaoxing-xie/openclaw-data-china-stock --skill fund-flow-analyst
View on skills.sh ↗ GitHub repository ↗

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Also in this package

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npx skills add shaoxing-xie/openclaw-data-china-stock
openclaw-data-china-stock 276 fundamental-analyst 2 strategy-backtester 2 market-sentinel 2 market-scanner 2 technical-analyst 2 china-macro-analyst 2

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More details

Agent compatibility

Declared targets from SKILL.md / docs. Unmarked agents are not listed — the skill may still install via the CLI.

Claude Code Not declared
Cursor Not declared
Codex Not declared
GitHub Copilot Not declared
Windsurf Not declared
Gemini CLI Not declared
Cline Not declared
OpenCode Not declared

Repository health

Stars 52
License LICENSE
Default branch main
Open issues 0
Status Active

Skill metadata

Parsed from SKILL.md frontmatter.

Version1.0.0

Package contents

Files included with this skill beyond the listing page.

  • skill md SKILL.md 1,844 B
  • docs SUMMARY.md 115 B

History

  1. First seen on skills.sh
  2. First recorded snapshot · 2 installs

SKILL.md

Fund Flow Analyst

目标

对 A 股资金流进行多层结构化解读，识别主线资金抱团与分歧信号。

输入

  • 用户问题
  • 大盘/板块/个股资金流数据
  • 北向资金数据

输出（固定结构）

  1. 大盘资金状态
  2. 北向资金态度
  3. 板块与个股资金聚集
  4. 连续性与反证

证据表（必选）

  • 列出 toolfetchasharefundflow / toolfetchnorthboundflow / toolfetchsectordata 等调用与 qualitystatus；关键净流入等数值须对应工具 JSON 字段。

反证与局限（必选）

  • 单日脉冲 vs 多日均值、上游降级对结论的影响；不得臆测补数。

强制规则

  • 仅通过 manifest / toolrunner 调用依赖工具，禁止引导直连 plugins.datacollection。
  • 对“单日”与“连续性(3/5/20日)”结论分开表达。
  • 无法构成连续性证据时输出 insufficient_evidence。
  • 禁止输出买卖点、仓位比例、杠杆建议。
  • 阈值从 config/fund-flow-analyst_config.yaml 读取。

依赖工具

  • toolresolvesymbol（L2；个股代码归一）
  • toolfetchasharefund_flow
  • toolfetchnorthbound_flow
  • toolfetchsector_data
  • toolfetchmarket_data

通用输出字段

  • market_flow
  • northbound_flow
  • sectorflowrank
  • crowding_signals
  • continuity_metrics
  • implications
  • counterevidence
  • confidence_band（low/medium/high）