Data Dependencies and Execution Prerequisites
It is recommended to use Wind wind-mcp-skill to obtain the underlying data. Before execution, first check whether there is already wind-mcp-skilllocally; if not, prompt the user to install:
# GitHub
npx skills add Wind-Information-Co-Ltd/wind-skills --skill wind-mcp-skill -g -y
# Gitee 镜像（国内）
npx skills add https://gitee.com/wind_info/wind-skills.git --skill wind-mcp-skill -g -y
Skill Positioning
This skill is used to judge the participation breadth and upward quality behind index performance, answering "whether an index rise is truly strong, or whether a few heavyweight stocks are forcibly pulling it up; whether an index fall is a broad weakening, or a localized structural adjustment." The output focus is not to restate changes in index levels, but to assess market health, identify clustering and divergence, and help users calibrate risk exposure.
Execution Process
Step 1: Confirm the Observation Target and Period
First clarify what this breadth judgment revolves around:
- Which index or which type of market is being observed
- Whether to look at intraday, the past week, the past month, or a longer period
- Whether to explain the current upward quality or judge the scope of damage from a decline
- Whether the user is more concerned about short-term participation or medium-term trend robustness
If the user does not specify, use the past week and the past month as the two scales for cross-judgment by default.
Step 2: Establish a Breadth Observation Framework
Market breadth should not only look at the number of advancers and decliners, but should be judged simultaneously from multiple angles:
- Distribution of the number of rising and falling instruments
- Changes in the number of new highs and new lows
- Degree of participation across different market capitalizations, styles, and industries
- Whether leading stocks, heavyweight stocks, and ordinary constituent stocks are synchronized
- Whether index performance is consistent with the money-making effect
The goal of this stage is to confirm "whether index performance has support from the broader market."
Step 3: Identify Healthy Diffusion, Localized Clustering, or Weak Rebound
Combining breadth data and structural characteristics, classify the market into roughly one of the following types:
- Healthy diffusion: Most industries and individual stocks improve simultaneously, and the index rise has a foundation
- Structural clustering: A few heavyweight or leading stocks drive the index, while breadth does not follow
- Weak repair rebound: A brief repair after a decline, but with limited participation scope
- Broad weakening: Breadth deteriorates before or simultaneously with the index pullback
When classifying, look at both the cross-sectional distribution and temporal persistence, avoiding judging only by a single day's state.
Step 4: Explain the Reasons Behind Breadth Changes
Further explain why breadth is currently in this state, focusing on:
- Whether the market is broadly diffusing under improved risk appetite, or clustering for risk aversion
- Whether styles such as growth, value, cyclical, and defensive are taking turns to absorb
- Whether there are structural disturbances brought by macro events, policy signals, or earnings windows
- Whether breadth deterioration stems from release of crowded high-level positions, chip switching, or liquidity problems
Step 5: Judge the Implications for Index Trends and Positions
Breadth analysis must ultimately return to an actionable level, focusing on answering:
- Whether the current index rise has continuity
- Even if the index appears stable, whether structural drawdown still needs to be guarded against
- If breadth improves, which directions should receive increased attention
- If breadth deteriorates, where risk should be controlled first
Step 6: Form a Final Market Breadth Checkup Draft
The final output should help users quickly judge whether the overall market is "strong, stable, and has a profit-making effect." The main text should retain only breadth conclusions, structural diagnosis, driver explanations, and risk warnings.
Output Structure
{Market/Index Name} Market Breadth Checkup ({YYYY-MM-DD})
I. 30-Second Conclusion
- Index Status: {Strong Uptrend / Choppy / Weakening}
- Breadth Status: {Healthy / Average / Weak}
- Market Character: {Broad Diffusion / Localized Clustering / Weak Repair / Broad Weakening}
- Current Prompt: {Can Actively Track / Maintain Screening / Treat Index Appearance with Caution}
II. Breadth Observation Panel
| Dimension | Current Performance | Conclusion |
|---|---|---|
| Advance/Decline Distribution | {Content} | {Judgment} |
| New Highs/New Lows | {Content} | {Judgment} |
| Industry Participation | {Content} | {Judgment} |
| Synchronization Between Weighted and Ordinary Stocks | {Content} | {Judgment} |
| Profit-Making Effect | {Content} | {Judgment} |
III. Market Structure Diagnosis
- Whether Clustering Exists: {Content}
- Whether Diffusion Exists: {Content}
- Most Obvious Point of Divergence: {Content}
IV. Driver Explanation
- Main Driver: {Content}
- Auxiliary Factors: {Content}
- Potential Disturbances: {Content}
V. Implications for Trend and Position
- Judgment on Trend Continuity: {Content}
- Current Main Risks: {Content}
- Operational Focus Points: {Content}
VI. Follow-Up Observation Checklist
- {Observation Point 1}
- {Observation Point 2}
- {Observation Point 3}
Quality Requirements
- Breadth analysis must reflect the relationship between "index performance" and "individual stock participation"; it cannot address only one side.
- It must clearly judge whether the market belongs to healthy diffusion, localized clustering, or weak repair, to improve readability.
- Weak breadth does not necessarily equal bearishness, but it must explain where the risk comes from.
- If different breadth indicators give opposite signals, the points of conflict should be clearly stated.
- The output should help users identify the true health of the overall market, rather than remaining at the surface of the index.