About this skill

Problem addressed

daily review targets common issues in A-share post-market reviews: conclusions without verifiable metrics, silent fallbacks when data is missing, inconsistent index, sector, and individual-stock definitions, and reports that are hard to audit. It constrains the task to structured analysis rather than prediction, focusing on market structure, strength, sentiment, and trend retrospectives while tying each judgment to explicit validation metrics.

How it works and limits

The skill reads quotes, K-line data, intraday trends, market breadth, limit-up/limit-down pools, and limit-up attribution from prioritized sources such as Tencent Finance, East Money, mootdx, and stockstats, and marks degradation instead of silently replacing data. It produces Markdown/HTML reviews, a weekly HTML report, and a machine-checkable JSON snapshot. The fixed output covers yesterday calibration, core conclusion, index status, sentiment pool, sector strength, stock samples, trend retrospectives, and tomorrow watchlist. Required conventions include using an A-share average price or equal-weight all-A indicator, integer 3-day close paths, stock names, continuous N-day limit-up counts, and other auditable fields.
Use with care: it depends on public data-source stability. If more than two key data modules are missing, it outputs a “data fetch exception” version instead of a full conclusion. It is suited for post-market structure checks and review traceability, not forecasting or investment advice.

Screenshots

Use Cases

After close, assemble indices, turnover, limit-up pools, and ladder counts into an auditable review.
Turn sector leaders, strong stock samples, and 5-8 day trend paths into structured Markdown reports.
When market-data APIs fail, identify which layer is missing and mark degradation explicitly.
Calibrate yesterday's watchpoints into today's validation metrics and produce a 3-5 item watchlist.

Best For

A-share short-term researchers who need post-market conclusions tied to metrics and watchlists.
Data engineers maintaining market APIs who need to trace Tencent, East Money, and mootdx failures.
Sentiment trackers focused on sector leaders who need limit-up, blow-off, and ladder changes.
Traders keeping strategy records who need JSON snapshots and stock or trend retrospectives.