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.
npx skills add huangrichao2020/pretty-skills --skill daily-market-reviewgit clone --depth 1 https://github.com/huangrichao2020/pretty-skillsWrote 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.
[](https://agentmods.dev/skills/huangrichao2020/pretty-skills/daily-market-review)<a href="https://agentmods.dev/skills/huangrichao2020/pretty-skills/daily-market-review"><img src="https://agentmods.dev/badge/skills/huangrichao2020/pretty-skills/daily-market-review/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/huangrichao2020/pretty-skills/daily-market-review"><img src="https://agentmods.dev/badge/skills/huangrichao2020/pretty-skills/daily-market-review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00116 | $0.02331 |
| Opus 5 | $0.00058 | $0.01166 |
| Sonnet 5 | $0.00023 | $0.00466 |
| Haiku 4.5 | $0.00012 | $0.00233 |
Grade A, and why
daily-market-review scanned grade A with 1 finding 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 12d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -s "https://qt.gtimg.cn/q=sh000001,sz399001,sz399006,sh000688,sh000300" | iconv -f GBK -t UTF-8 How it starts
The opening of the file, as written. The whole thing — 229 lines — stays where its author put it; the contents beside it link to each section on GitHub.
每日复盘
核心理念
每日复盘不是罗列数据，而是用四层融合框架给市场做一次"日度体检"：
L0 人性层（最高否决权）：住相信号链亮了几灯？执念在哪个阶段？
L1 情绪层：情绪周期在冰点/复苏/高潮/退潮的哪个位置？
L2 产业层：今天的热点有真实因果逻辑吗？还是题材轮动？
L3 证据层：支撑方向的证据是 Tier A（趋势确认）还是 Tier C（情绪修复）？
否决权层级：L0 > L1 > L3 > L2。
使用流程
1. 获取大盘指数行情
用腾讯行情 API 批量获取五大指数：
curl -s "https://qt.gtimg.cn/q=sh000001,sz399001,sz399006,sh000688,sh000300" | iconv -f GBK -t UTF-8
解析字段：名称(代码) | 现价 | 昨收 | 开盘 | 最高 | 最低 | 涨跌%
关注：
- 指数涨跌是否一致（沪强创弱 = 高低切换信号）
- 冲高回落幅度（开盘vs收盘差距 = 情绪持续性）
- 成交额变化（放量还是缩量）
2. 搜索市场关键数据
并行搜索以下信息（用 WebSearch）：
搜索1: "{日期} A股 涨停家数 连板高度 炸板率 成交额 情绪"
搜索2: "{日期} A股 主力资金 板块资金流 龙头股 热门板块"
搜索3: "A股 {下周/下月} 机会 热点板块 策略"
需要提取的核心指标：
| 指标 | 数据源 | 诊断用途 |
|---|---|---|
| 涨停家数 | 搜索/新闻 | L1情绪层温度 |
| 跌停家数 | 搜索/新闻 | 恐慌程度 |
| 封板率 | 搜索/新闻 | 情绪持续性（>70%强，<60%弱） |
| 炸板数 | 搜索/新闻 | 午后是否大面积炸板 |
| 成交额 | 搜索/新闻 | 量能配合 |
| 主力净流出 | 搜索/新闻 | L0资金转向信号 |
| 连板高度 | 搜索/新闻 | 龙头持续性 |
| 上涨家数 | 搜索/新闻 | 普涨还是分化 |
3. 搜索板块与催化剂
搜索4: "{日期} A股 {领涨板块} 涨停 龙头 催化剂"
对领涨的2-3个板块，进一步搜索：
- 具体催化剂是什么？（政策/事件/业绩）
- 龙头股是谁？有没有连板效应？
- 板块内部有没有分歧？
4. 四层融合诊断
L0 人性层 — 住相信号链（五维检测）
逐条判断是否亮灯：
| 信号 | 判断方法 |
|---|---|
| 龙头乏力 | 连板高度是否下降？龙头是否切换频繁？ |
| 跟风先跑 | 板块涨跌比 < 0.5？后排股是否先跌？ |
| 扩散停止 | 热点是否集中在1-2个方向？新方向不再出现？ |
| 情绪背离 | 指数涨但涨停少？或指数跌但涨停多？ |
| 资金转向 | 主力净流出 > 100亿？午后集中抛售？ |
住相信号 ≥ 3 = 减仓信号生效。
同时判断当前执念阶段：
确认（底部放量）→ 发酵（逻辑扩散）→ 分歧（跟风掉队）
→ 高潮（加速放量）→ 衰退（断板滞涨）→ 退潮（连续下跌）
L1 情绪层 — STW 情绪周期
| 阶段 | 涨停特征 | 市场温度 |
|---|---|---|
| 冰点 | < 20只，炸板率高 | 不宜割肉，等修复 |
| 复苏 | 30-60只，封板率回升 | 可以观察参与 |
| 高潮 | > 80只，连板加速 | 是减仓窗口 |
| 退潮 | 断板潮，昨日涨停今日大面 | 反弹即抛 |
L2 产业层 — 方向真实性
对每个领涨板块做三层因果检验：
第一层：有政策催化吗？
第二层：有业绩兑现吗？
第三层：有供需拐点吗？
三层齐全 = 真主线，1-2层 = 有逻辑待验证，0层 = 纯题材轮动。
L3 证据层 — 证据硬度
| 等级 | 含义 |
|---|---|
| Tier A | 趋势确认（突破关键位+放量+资金流入） |
| Tier B | 有逻辑待验证（催化明确但未兑现） |
| Tier C | 情绪修复（仅跟大盘涨，无独立证据） |
| Tier D | 负面证据（利空/风险） |
5. 分析下期机会
对下一交易周期（下周/下月）的机会做方向性判断：
| 确信度 | 含义 | 操作建议 |
|---|---|---|
| A | 催化剂明确 + 产业逻辑硬 + 本周刚启动 | 逢回调关注前排 |
| B | 有逻辑但需要验证 | 观察为主 |
| C（观望） | 高低切换中/缺乏催化 | 等资金回流信号 |
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 12d ago First seen · 229 lines · 116 tokens per session scan A b0db9a82446e
daily-market-review is a skill published in the GitHub repository huangrichao2020/pretty-skills (54 stars, last pushed today), licensed MIT. It adds 116 tokens to every session and 2,331 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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