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China Earnings Preview

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jwangkun
china-earnings-preview

Pre-earnings analysis for A-share stocks. Builds scenario frameworks (actual vs consensus, beat/miss cases), identifies key metrics to watch, and prepares positioning notes before Chinese companies report quarterly results. Use instead of the original earnings-preview skill for A-share coverage. Triggers on "A股财报前瞻", "季报前瞻", "业绩前瞻", "earnings preview", "what to watch for [company] earnings", or "pre-earnings setup".

Overview

Publisherjwangkun
Repositoryclaude-for-financial-services-cn
Skill namechina-earnings-preview
Stars
834
Forks
132
Bundled files
Instructions only
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by jwangkun on GitHub. Read the source before you install it.

Installation

Install the China Earnings Preview AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/jwangkun/claude-for-financial-services-cn/tree/main/agent-plugins/china-earnings-reviewer/skills/china-earnings-preview
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/jwangkun/claude-for-financial-services-cn.git /tmp/claude-for-financial-services-cn
mkdir -p .claude/skills
cp -r /tmp/claude-for-financial-services-cn/agent-plugins/china-earnings-reviewer/skills/china-earnings-preview .claude/skills/china-earnings-preview
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable China Earnings Preview in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads China Earnings Preview on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so China Earnings Preview is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

china-earnings-preview

Purpose

Build A股季报/年报前瞻分析, preparing for company earnings releases with scenario frameworks and key metrics to watch.

Data Sources

Primary: iFind MCP (Tier-1 付费) / AkShare MCP (Tier-2 免费备选)

python
get_quote(ticker)                        → Current valuation, PE/PB
get_historical_data(ticker)              → Trading context, 52-wk range
get_financials(ticker, "income", "annual")  → Historical revenue/EPS trends
# News (china-news MCP — separate server)
get_stock_news(ticker="{{TICKER}}")          → Pre-earnings context
get_industry_stocks(industry="...")      → Peer trading multiples

Consensus Estimates Sources

SourceAccessNotes
Wind 一致预期InstitutionalMost comprehensive
Choice 一致预期InstitutionalAlternative
慧博投研Web / APIGood coverage
同花顺 iFinDWeb / APIRetail-friendly UI
东方财富WebFree, some coverage
巨潮 业绩预告RegulatoryMandatory disclosures

If consensus unavailable, derive from:

  • Historical growth rates
  • Management guidance from prior calls
  • Industry benchmarks

Secondary Sources

  • 公司公告 (earnings preview notices 业绩预告)
  • 行业研究报告 (sector reports)
  • 卖方研报 (broker research summaries)

Workflow

Step 1: Establish Baseline

Historical performance (last 4-8 quarters):

QuarterRevenue (亿)YoYNet Income (亿)YoYEPS (元)Net Margin
Q1 2024
Q2 2024
Q3 2024
Q4 2023

Identify trends:

  • Accelerating or decelerating growth?
  • Margin expansion or compression?
  • Seasonal patterns?
  • One-time items to normalize?

Step 2: Gather Consensus Estimates

Consensus table:

MetricQ1 2024 EstimateRange (Low-High)# Analysts
Revenue (亿)
YoY Growth
Net Income (亿)
EPS (元)
Gross Margin
Net Margin

Beat probability assessment:

  • Strong beat (>+10%): Company has history of under-promising
  • Moderate beat (+5% to +10%): Consensus well-established
  • In-line (-5% to +5%): Typical range
  • Miss risk (<-5%): Macro headwinds, order delays

Step 3: Identify Key Metrics to Watch

Company-specific KPIs:

For each company, identify 3-5 metrics that will drive the report:

MetricWhy It MattersWatch ThresholdRisk if Missed
e.g., 白酒批价Price indicator for channel health>950元/瓶Demand softness
e.g., 动力电池装机量Volume indicator>XX GWhMarket share loss
e.g., 云业务收入增速Growth engine health>30%Cloud slowdown

Sector-wide KPIs (for sector previews):

SectorKey Metrics
白酒批价、库存、回款、动销
半导体产能利用率、出货量、ASP、库存天数
新能源汽车交付量、单车收入、毛利率、电池成本
医药创新药收入、研发费用、集采影响
银行NIM、不良率、拨备覆盖率
券商经纪/投行/资管收入、股基交易量
光伏硅料/组件价格、排产、海外出货
房地产销售额、拿地、融资成本

Step 4: Build Scenario Framework

Three-scenario model:

BEAR CASE (超预期悲观)
  Revenue: -X% vs consensus
  Net Income: -Y% vs consensus
  Key factor: [specific risk]
  Likely catalysts: 业绩预告大幅下调, 行业负面政策

BASE CASE (符合预期)
  Revenue: ±Z% vs consensus
  Net Income: ±W% vs consensus
  Key factor: [steady state]
  Likely outcome: 符合预期, 股价波动±5%

BULL CASE (超预期乐观)
  Revenue: +A% vs consensus
  Net Income: +B% vs consensus
  Key factor: [positive surprise driver]
  Likely catalysts: 新品放量, 成本下降超预期

Step 5: Position Analysis

What does the market expect?

  • Recent stock price performance into earnings
  • Implied move from options (if A-share options available)
  • Sentiment from 北向资金 trends
  • Broker recommendations distribution

Position sizing considerations:

  • High expectations (high PE) → asymmetric risk to downside
  • Low expectations (depressed stock) → upside potential on beat
  • Earnings as catalyst: upcoming product launch, policy change

Step 6: Pre-Earnings Positioning Note

Standard structure:

[公司名称]([代码])[季/年报] 前瞻:[主题/焦点]

一、业绩预期
  - 关键指标一致预期一览
  - 预测区间

二、情景分析
  - 乐观/基准/悲观情景

三、关注要点
  - 最重要的 3-5 个指标
  - 预期 vs 实际的关键差异点

四、估值与预期
  - 当前估值水平
  - 市场情绪指标
  - 北向资金动向

五、情景判断与策略
  - 不同情景下的股价反应
  - 可能的交易策略

六、风险提示
  - 关键下行风险

Step 7: Post-Earnings Follow-up

After actual results are released:

  • Compare actual vs preview scenarios
  • Update the earnings-analysis model
  • Revise forward estimates
  • Note any material guidance changes

China-Specific Pre-Earnings Considerations

Earnings Calendar (A-share)

Report TypeDeadlineTypical Release Time
Q1 / Q3季报1 month after quarter-endBefore market open or after close
Semi-annual report (中报)2 months after H1Before market open
Annual report (年报)4 months after year-endTypically Jan-Apr

Release pattern:

  • Most companies release before market open (8:00-9:00 AM)
  • Some release after market close (after 15:00)
  • 创业板/科创板 may have more flexible schedules

业绩预告 (Earnings Preview Notice)

  • Mandatory if actual vs prior period variance >50%
  • Published typically 2-4 weeks before formal report
  • Format: 预增 (increase), 预减 (decrease), 扭亏 (turn to profit), 首亏 (first loss), 续亏 (continued loss)
  • Provides directional guidance before formal report

Consensus Reliability

Caveats for Chinese consensus:

  • Fewer analysts covering A-shares vs US large caps
  • Estimates may be stale (update frequency lower)
  • Institutional vs retail analyst coverage varies significantly
  • Broker research sometimes biased ( conflicted interests )
  • Cross-reference multiple sources when possible

Policy Risk

  • Regulatory changes can materially impact earnings overnight
  • 行业政策 (industry policy) shifts common in:
    • 医药 (pharmaceuticals — 集采)
    • 教育 (education — 双减)
    • 互联网 (internet — antitrust)
    • 新能源 (renewables — subsidy changes)
  • Factor policy risk into scenario analysis

Quality Checks

Before delivering preview:

  • Historical data complete and accurate (AkSource verified)
  • Consensus estimates sourced (or clearly noted as unavailable)
  • Scenario framework covers bull/base/bear
  • Key watch items identified with rationale
  • China-specific risks flagged (政策, 集采, etc.)
  • Valuation context included
  • Pre-earnings positioning actionable

Data Source Mode Switch: Set env var IFIND_DATA_SOURCE_MODE to control data source preference.

  • ifind-only (strict): Use iFind only, error if unavailable
  • ifind-fallback (default): iFind preferred, fallback to AkShare
  • akshare-only: Skip iFind, use AkShare only

Frequently asked questions

What does the China Earnings Preview AI skill do?

Pre-earnings analysis for A-share stocks. Builds scenario frameworks (actual vs consensus, beat/miss cases), identifies key metrics to watch, and prepares positioning notes before Chinese companies report quarterly results. Use instead of the original earnings-preview skill for A-share coverage. Triggers on "A股财报前瞻", "季报前瞻", "业绩前瞻", "earnings preview", "what to watch for [company] earnings", or "pre-earnings setup".

Why use China Earnings Preview on TypingMind?

Because you install it once and use it with any model. China Earnings Preview is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install China Earnings Preview in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/jwangkun/claude-for-financial-services-cn/tree/main/agent-plugins/china-earnings-reviewer/skills/china-earnings-preview. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use China Earnings Preview?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with China Earnings Preview?

As many as you like. As long as a model supports skills, you can use China Earnings Preview with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the China Earnings Preview AI skill free?

Yes. It is published on GitHub by jwangkun under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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