By haohanyang92Updated 2026-05-11a-stock-news-sentiment@clawhub_haohanyang92/a-stock-news-sentiment
FinanceIndustry Research#News#消息
According to AlphaMoat, as of 2026-09, A股消息面分级解读 has 2,164 downloads and 1 stars — ranked #4,313 of 5,078 AI skills overall, and #372 of 465 in Professional.
| Platform | Installs | Downloads | Stars | Comments |
|---|---|---|---|---|
| ClawHub ↗ | 40 | 1.5K | - | - |
| SkillHub ↗ | 386 | 2.2K | 1 | — |
Platforms count differently and update at different times.
It groups policy changes, industry events and routine announcements, and suggests checking subsidy significance and sector fund flows. These rules are an interpretive framework, not evidence that news is genuine or that a trade will be profitable.
Profile published 2026-10-27
Generated from the skill’s documentation as examples; not results of actual runs.
The counting method changed in 2026-09: skills with the same name are now counted separately by author, and the full catalog is covered. Dashed lines show months before the change; they are not compared with later months, and no growth is computed across the change.
| Month | Downloads | MoM | Stars |
|---|---|---|---|
| 2026-09 | 2.2K | — | 1 |
| 2026-08 | 1.7K | +9.4% | 1 |
| 2026-07 | 1.6K | +19.5% | 1 |
| 2026-06 | 1.3K | +13.3% | 1 |
| 2026-05 | 1.2K | — | 1 |
| 2026-03 | 701 | — | - |
The author’s original file, shown as plain text without edits or review by AlphaMoat. Links are not clickable; check commands before running them. Fetched 2026-10-23.
--- name: news-sentiment-analysis description: 区分"真利好"和"假利好"，避免追高被套。用于A股消息面分析。 --- # 消息面分级解读 Skill ## 核心能力 区分"真利好"和"假利好"，避免追高被套。
## 利好分级规则 ### 一级利好（可重仓追涨） - 国家级政策落地（如降息、行业扶持） - 龙头股直接受益 - **操作建议**：可重仓追涨 ### 二级利好（轻仓参与） - 行业事件（如产业链涨价、技术突破） - 板块联动性强 - **操作建议**：轻仓参与 ### 无效利好（观望为主） - 公司常规公告（如股东增持、战略合作） - 无资金承接 - **操作建议**：观望为主 ## 实战规则 ### 补贴类利好判断 看到"某公司获政府补贴"公告时： 1. 判断补贴金额占净利润的比例 2. 比例 < 5% 为无效利好 3. 再看板块资金流向 ### 追高避坑 - 利好发布后已涨停2次以上，谨慎追 - 利好发布但无资金承接，及时止盈 ## 触发场景 - "这个利好是真的吗" - "能追吗" - "这个公告是利好还是利空"
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Data month: 2026-09 · Downloads, stars and installs are aggregated monthly from public skill registries (ClawHub, SkillHub). See methodology.