a-share-screener

a-share-screener is a skill for Claude Code from nicepkg/ai-workflow. It costs 79 tokens per session (2,655 once invoked), scanned A, original, MIT.

A stock screener for shares listed on mainland China's Shanghai and Shenzhen exchanges. It filters companies using financial measures, price-chart indicators, money-flow patterns, or combinations of these criteria.

In plain words
What is it for?
Use it to find stocks by measures such as valuation, profitability, revenue growth, debt, dividends, technical signals, or capital flows.
Why use it?
It narrows a large stock market to companies that match defined conditions, so you do not have to inspect every stock manually. Preset approaches cover value, growth, momentum, and dividend searches.

Skill for Claude Code

Written for Claude Code: installed under .claude/.

Good fit Use it to find stocks by measures such as valuation, profitability, revenue growth, debt, dividends, technical signals, or capital flows.

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Install with agentmods
npx agentmods add skills/nicepkg/ai-workflow/a-share-screener
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add nicepkg/ai-workflow --skill a-share-screener
Clone the repo
git clone --depth 1 https://github.com/nicepkg/ai-workflow

Made for: Claude Code.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for a-share-screener

README.md
[![agentmods](https://agentmods.dev/badge/skills/nicepkg/ai-workflow/a-share-screener.svg)](https://agentmods.dev/skills/nicepkg/ai-workflow/a-share-screener)
Your own site
<a href="https://agentmods.dev/skills/nicepkg/ai-workflow/a-share-screener"><img src="https://agentmods.dev/badge/skills/nicepkg/ai-workflow/a-share-screener.svg" alt="Measured on agentmods" height="20"></a>
Per session 79 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,655 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00079 $0.02655
Opus 5 $0.00039 $0.01327
Sonnet 5 $0.00016 $0.00531
Haiku 4.5 $0.00008 $0.00265

Measured 4d ago against content hash 50ff5c546b06, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-07, from the pricing page.

Security

Grade A, and why

a-share-screener scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

workflows/stock-trader-workflow/.claude/skills/a-share-screener/SKILL.md · 316 lines

How it starts

The opening of the file, as written. The whole thing — 316 lines — stays where its author put it; the contents beside it link to each section on GitHub.

A股选股器 (A-Share Stock Screener)

Overview

Screen A-share stocks using various criteria including fundamental metrics, technical indicators, capital flow patterns, and combined strategies. Support preset screening strategies and custom filter combinations.

Screening Categories

1. Fundamental Screening (基本面选股)

Value Metrics (价值指标):

Metric Chinese Typical Criteria
P/E Ratio 市盈率 < 15 (value), < 行业均值
P/B Ratio 市净率 < 1.5, < 行业均值
P/S Ratio 市销率 < 2
PEG Ratio PEG < 1 (被低估)
Dividend Yield 股息率 > 3%
EV/EBITDA < 10

Quality Metrics (质量指标):

Metric Chinese Typical Criteria
ROE 净资产收益率 > 15%
ROA 总资产收益率 > 5%
Gross Margin 毛利率 > 30%
Net Margin 净利率 > 10%
Operating Cash Flow 经营现金流 > 净利润

Growth Metrics (成长指标):

Metric Chinese Typical Criteria
Revenue Growth 营收增速 > 20% YoY
Profit Growth 利润增速 > 20% YoY
EPS Growth 每股收益增速 > 15%
3Y CAGR 三年复合增长率 > 15%

Financial Health (财务健康):

Metric Chinese Typical Criteria
Debt Ratio 资产负债率 < 60%
Current Ratio 流动比率 > 1.5
Quick Ratio 速动比率 > 1
Interest Coverage 利息保障倍数 > 3
Cash/Debt 货币资金/有息负债 > 1

2. Technical Screening (技术面选股)

Trend Filters (趋势筛选):

Criteria Chinese Description
Above MA200 站上年线 价格 > 200日均线
Above MA60 站上季线 价格 > 60日均线
Golden Cross 金叉 短期均线上穿长期均线
Death Cross 死叉 短期均线下穿长期均线
New High 创新高 52周/历史新高
Breakout 突破 突破关键阻力位

Momentum Filters (动量筛选):

Criteria Chinese Description
RSI RSI指标 RSI > 50 (上涨趋势)
MACD MACD MACD > 0 或金叉
Volume Surge 放量 成交量 > 5日均量 x 2
Price Range 振幅 近期振幅筛选

Pattern Filters (形态筛选):

  • 连续涨停
  • 涨停后缩量整理
  • 底部放量
  • W底形态
  • 头肩底形态

Read the full file on GitHub · 316 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 4d ago First seen · 316 lines · 79 tokens per session scan A 50ff5c546b06

Subscribe to this mod's changes

a-share-screener is a skill published in the GitHub repository nicepkg/ai-workflow (282 stars, last pushed 7mo ago), licensed MIT. It adds 79 tokens to every session and 2,655 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.

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