Bm Md
miantiao-me/bm.md
使用 bm.md 写作、改写、排版或渲染 Markdown;生成 Mermaid 与 AntV Infographic,设置图片尺寸、高亮重点,以及执行 HTML/纯文本转换和 Markdown lint
美股个股次日走势预测 skill。当用户输入美股股票代码(如 AAPL、TSLA)或公司名称(如 "苹果"、"特斯拉"),并要求预测次日(T+1)走势、给出交易计划、评估盈亏概率时,触发本 skill。skill 会用 mcpMiniMaxwebsearch 搜证盘前 90…
The automated check flagged lines worth reading first. See the safety section below.
$ npx skills add digoal/blog --skill us-stock-prediction -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install digoal/blog us-stock-prediction --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/digoal/blog.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/us-stock-prediction .claude/skills/us-stock-prediction && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "us-stock-prediction" agent skill from https://github.com/digoal/blog/tree/master/skills/us-stock-prediction into .claude/skills/us-stock-prediction/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "us-stock-prediction", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/digoal/blog/tree/master/skills/us-stock-predictionType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add digoal/blog --skill us-stock-prediction -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install digoal/blog us-stock-prediction --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/digoal/blog.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/us-stock-prediction .agents/skills/us-stock-prediction && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "us-stock-prediction" agent skill from https://github.com/digoal/blog/tree/master/skills/us-stock-prediction into .agents/skills/us-stock-prediction/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "us-stock-prediction", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add digoal/blog --skill us-stock-prediction -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install digoal/blog us-stock-prediction --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/digoal/blog.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/us-stock-prediction .cursor/skills/us-stock-prediction && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "us-stock-prediction" agent skill from https://github.com/digoal/blog/tree/master/skills/us-stock-prediction into .cursor/skills/us-stock-prediction/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "us-stock-prediction", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/digoal/blog.git --path skills/us-stock-prediction--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add digoal/blog --skill us-stock-prediction -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install digoal/blog us-stock-prediction --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/digoal/blog.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/us-stock-prediction .gemini/skills/us-stock-prediction && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "us-stock-prediction" agent skill from https://github.com/digoal/blog/tree/master/skills/us-stock-prediction into .gemini/skills/us-stock-prediction/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "us-stock-prediction", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install digoal/blog us-stock-predictionInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add digoal/blog --skill us-stock-prediction -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/digoal/blog.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/us-stock-prediction .github/skills/us-stock-prediction && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "us-stock-prediction" agent skill from https://github.com/digoal/blog/tree/master/skills/us-stock-prediction into .github/skills/us-stock-prediction/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "us-stock-prediction", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add digoal/blog --skill us-stock-prediction -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install digoal/blog us-stock-prediction --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/digoal/blog.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/us-stock-prediction .opencode/skills/us-stock-prediction && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "us-stock-prediction" agent skill from https://github.com/digoal/blog/tree/master/skills/us-stock-prediction into .opencode/skills/us-stock-prediction/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "us-stock-prediction", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
us-stock-prediction美股个股次日走势预测 skill。当用户输入美股股票代码(如 AAPL、TSLA)或公司名称(如 "苹果"、"特斯拉"),并要求预测次日(T+1)走势、给出交易计划、评估盈亏概率时,触发本 skill。skill 会用 mcpMiniMaxwebsearch 搜证盘前 90…
Us Stock Prediction is an agent skill from digoal/blog. 美股个股次日走势预测 skill。当用户输入美股股票代码(如 AAPL、TSLA)或公司名称(如 "苹果"、"特斯拉"),并要求预测次日(T+1)走势、给出交易计划、评估盈亏概率时,触发本 skill。skill 会用 mcpMiniMaxwebsearch 搜证盘前 90 分钟的关键信息(财报、盘后走势、隔夜期货、期权异动、内部人交易、宏观日历等),按"五维分析框架"(基本面/技术面/资金面/情绪面/宏观面)交叉验证,产出可量化的方向预测(涨/跌/横盘 + 概率百分比 + 区间),并写入当前项目 markdown 目录下的 us-stock-{TICKER}-{YYYY-MM-DD}.md。输出必须有完整的逻辑推导链(每一维的打分过程与数据来源)、术语通俗解释、mermaid/ascii 流程图或可视化图表。适用场景:盘前/盘中想做次日的方向判断与交易计划;不适用:长线基本面估值(应改用 finance 领域其他 skill)、日内高频交易、加密货币/港股/A股。
Its SKILL.md is about 1.9k tokens, which your agent loads only when the skill is triggered. The skill folder holds 7 other files, including reference files (for example `agents/openai.yaml`, `references/five-dimension-framework.md` and `references/output-template.md`).
It sits in Productivity & Automation, covering Web search, Diagrams and Markdown. It works with MiniMax, Model Context Protocol and Mermaid. The repository describes itself as: AI,Opensource,Database,Business,Finance,Minds. git clone --depth 1 https://github.com/digoal/blog. The licence is GPL-2.0.
4 steps, taken from the first numbered list in SKILL.md.
Read from SKILL.md and the folder at commit ad6fcb7. It shows what the files ask for, not the result of running them.
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
No scripts in the folder and no shell commands in SKILL.md (its code samples are markdown).
From the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Us Stock Prediction loads about 1.9k tokens when it runs, and up to ~11k if it reads all its reference files. Until then it costs about 117 tokens; SKILL.md has 280 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
The automated check found patterns that need a careful read before installing.
⟨U+200B⟩```mermaid⟨U+200B⟩```Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
The full file from digoal/blog at commit ad6fcb7, republished under its GPL-2.0 licence (© digoal). 280 words, ~1,933 tokens.
.claude/skills/us-stock-prediction/SKILL.md (or your agent's skills folder). This skill also uses 5 other files; get the full folder from GitHub.把"次日走势"这一低信噪比问题转成可重复、可量化、可复盘的决策流程:
[用户输入代码/名称]
│
▼
[步骤 1: 标的解析] — 确认 ticker、公司全名、所在交易所、所属板块
│
▼
[步骤 2: 信息搜证] — 用 mcp__MiniMax__web_search 拉取盘前 90 分钟关键数据
│
▼
[步骤 3: 五维打分] — 基本面 30% / 技术面 25% / 资金面 20% / 情绪面 15% / 宏观面 10%
│
▼
[步骤 4: 综合推演] — 五维加权 → 方向预测(看多/看空/中性) + 概率(%) + 价格区间
│
▼
[步骤 5: 交易计划] — 入场价、止损价、目标价、仓位、时间止损、退出条件
│
▼
[步骤 6: 写出 markdown] — 保存到 <项目>/markdown/us-stock-{TICKER}-{YYYY-MM-DD}.md
│
▼
[步骤 7: 输出摘要] — 控制台/聊天框给出核心结论 + 文件路径收到用户输入(如 "AAPL" 或 "苹果"),先确认以下信息(必要时就近 24h 内的搜索补充):
| 字段 | 说明 | 来源 |
|---|---|---|
| Ticker | 标准化代码(如 AAPL,BRK.B) | 用户输入 + 搜索 |
| 公司全名 | Legal Name | 搜索 "Apple Inc ticker" |
| 交易所 | NYSE / NASDAQ / 其他 | 搜索 |
| 板块 | GICS 一级行业(科技/金融/医疗等) | 搜索 |
| 关键宏观关联 | 是否权重股(影响 SPY/NQ)、是否中概、是否有特殊风险 | 推断 |
如果用户只给公司名(如"苹果"),用 mcp__MiniMax__web_search(query="苹果公司 股票代码 ticker") 之类查询,优先在结果里找 ticker。
严格按以下优先级调用 mcp__MiniMax__web_search,每次查询都把 ticker + 关键词组合:
{ticker} earnings after-hours
{ticker} 8-K filing {最近一周}
{ticker} after-hours price reaction
ES NQ futures overnight
VIX index today{ticker} unusual options activity
{ticker} call put ratio
{ticker} institutional holdings 13F change
{ticker} insider trading Form 4
{ticker} analyst upgrade downgrade {最近一周}{板块} sector ETF performance today
{ticker} technical analysis support resistance
{宏观经济事件} CPI FOMC NFP date this week
US 10 year yield today
DXY dollar index today如果搜证失败(网络问题、结果太少),直接降级为"中低置信度"预测,并在 markdown 中明确标注"信息缺失"。
references/five-dimension-framework.md)每维给出 -2 ~ +2 的整数打分(代表强烈看空到强烈看多),并简述 1-3 条关键证据。
| 维度 | 权重 | 打分(举例) |
|---|---|---|
| 基本面催化 | 30% | 财报超预期 + 指引上修 → +2;无重大消息 → 0;业绩暴雷 + 指引下调 → -2 |
| 技术面结构 | 25% | 突破前高 + 量能配合 → +2;区间震荡 → 0;跌破关键支撑 → -2 |
| 资金面 | 20% | 大额回购 + 内部人增持 + OI 看涨 → +2;无明显异动 → 0;暗池出货 → -2 |
| 情绪面 | 15% | 分析师一致上调 + 散户关注度适中 → +1;极端一致预期(反向风险)→ 0 或反向 |
| 宏观面 | 10% | 期货涨 + VIX 跌 + 无大事件 → +1;FOMC 当天 → 0;宏观冲击 → -1~-2 |
加权求和 → 范围 -2.0 ~ +2.0。
如果五维里出现对冲信号(如基本面 +2 但宏观 -2),把"置信度"强制降一级,并在 markdown 里明确写"对冲点"。
输出三个数值(必须同时给):
方向: 看多 / 看空 / 中性
方向概率: 55% (区间 50%-60%)
价格区间: $185 - $192 (次日内最可能的运行区间)
期望波动幅度: ±2.3%(基于 ATR 估算或近期日均波幅)概率估算方法(不要瞎编):
必须明说:任何"≥ 70% 的方向概率"都是过度自信,需要在 markdown 顶部加免责声明。
只有置信度 ≥ "中" 才给完整交易计划;置信度 < "中" 只给"观望/小注试错"建议。
### 交易计划
- 入场触发价: $____ (理由:____)
- 止损价: $____ (理由:跌破 ____ 支撑 / 形态破坏)
- 目标价 TP1: $____ (赔率 1:1,平 50%)
- 目标价 TP2: $____ (赔率 2:1,再平 30%)
- 跟踪止损: 剩余 20% 用 1×ATR 跟踪
- 仓位大小: 账户净值的 ____% (确保止损触发时亏损 ≤ 1%)
- 最大持仓: ____ 个交易日(时间止损)
- 不做条件: 开盘 30 分钟内若 VWAP 之下,放弃仓位计算公式(必须用):
仓位(股数) = (账户净值 × 风险预算%) ÷ (入场价 − 止损价)<项目根目录>/markdown/us-stock-{TICKER}-{YYYY-MM-DD}.md
例:/Users/digoal/new/markdown/us-stock-AAPL-2026-06-08.md
# {TICKER} {公司名} 次日走势预测
> 预测日期:2026-06-08 | 目标交易日:2026-06-09
> 免责声明:本预测基于公开信息与概率推断,非投资建议。次日方向本身就是低信噪比问题,任何 ≥70% 的方向概率都应视为过度自信。
## 一、核心结论(速读)
用 3 行说清楚:方向、概率、关键价位。
例:
- 方向:看多(置信度 中)
- 次日方向概率:58%(区间 50%-66%)
- 关键价位:支撑 $185 / 压力 $195
## 二、五维分析(详细推导)
### 2.1 基本面催化(权重 30%,打分 +1)
证据 1:____(附数据来源)
证据 2:____
证据 3:____
### 2.2 技术面结构(权重 25%,打分 0)
证据 1:____
…
(每维重复)
## 五维加权得分
| 维度 | 打分 | 权重 | 加权 |
|---|---|---|---|
| 基本面 | +1 | 30% | +0.30 |
| 技术面 | 0 | 25% | 0 |
| 资金面 | +2 | 20% | +0.40 |
| 情绪面 | +1 | 15% | +0.15 |
| 宏观面 | +1 | 10% | +0.10 |
| **合计** | | | **+0.95** |
→ 弱看多(置信度"中"),基础概率 55% + 调整 +3% = **58%**
## 三、价格区间与可视化
用 mermaid 画一根"价格刻度尺",标记入场/止损/目标位:
```mermaid
graph LR
A[止损 $182] -->|6% 风险| B[入场 $185]
B -->|5% 赔率| C[目标 TP1 $190]
C -->|3% 赔率| D[目标 TP2 $195]
style A fill:#ff6b6b,color:#fff
style B fill:#4ecdc4,color:#fff
style C fill:#95e1d3,color:#333
style D fill:#95e1d3,color:#333或者用 ascii:
$182 ----|---- $185 ----|---- $190 ----|---- $195 ----|---- $200
止损 入场 TP1(50%平) TP2(30%平) 跟踪止盈
↑ 风险6% ↑ 赔率5% ↑ 赔率10% ↑ 赔率17%(填步骤 5 模板)
列出本次预测的 3-5 个最大风险点(用通俗语言 + 术语解释)。 例:"如果开盘 CPI 数据高于预期 0.3 个百分点,会触发系统性抛售,这个预测要立即废弃。"
列出本文用到的所有专业术语(财报超预期、VWAP、Put/Call、ATR、暗池、IV Rank…),每个用 1-2 句通俗话解释,默认读者是投资小白。
每一条数据后面用 [n] 标注来源(链接或机构名)。
### 写作风格硬性要求
1. **每个专业术语第一次出现时,立即用括号给出口语解释**
- 正确: "财报超预期(EPS 实际比分析师平均预期高)"
- 错误: "公司 Q2 EPS beats consensus"
2. **每个数字必须标注单位、时点、来源**
- 正确: "盘后股价 $208(2026-06-07 18:00 ET,Bloomberg)"
- 错误: "盘后涨到 208"
3. **必须用图示**(mermaid 或 ascii),至少 1 个;最好 2-3 个(五维雷达、概率饼图、价格刻度尺)
4. **逻辑链必须能逆向追溯**:每一条结论都能找到对应证据 + 来源
---
## 步骤 7:输出摘要
markdown 写完后,在聊天框给用户一个**不超过 10 行的摘要**:
✅ 已生成预测报告:markdown/us-stock-AAPL-2026-06-08.md
📊 核心结论:
⚠️ 重要风险:
💡 建议仓位:账户净值的 0.8%(基于止损触发最大亏损 1% 计算)
---
## 风险铁律(执行时必须严格遵守)
1. **永远不要给出"必涨/必跌"的判断**。即便五维全正向,也要给概率区间,留反手空间。
2. **任何"≥ 70% 的方向概率"自动 clamp 到 65%**。美股个股次日方向是低信噪比事件,过度自信是破产的最快路径。
3. **信息缺失必须明确标注**,并把置信度降级,而不是补全数据。
4. **不预测的具体情形**:
- 长线估值(应改用 fundamental analysis skill)
- 日内高频点位(预测频率与时窗不匹配)
- 财报前 24h 内的方向博弈(信息已被 Price-in,IV Crush 风险)
5. **不要把"看起来很对"的逻辑直接当成"会发生的现实"**。每一步都要写明"如果这个证据错了,推理链怎么塌"。
---
## 反模式(出现这些情况要重做)
| 反模式 | 为什么错 | 正确做法 |
|---|---|---|
| 只看技术面就给方向 | 技术面滞后,被 price-in 的概率高 | 必须五维交叉验证 |
| 把"利多"翻译成"必涨" | 利多已被市场消化或被宏观对冲 | 给概率,不给定论 |
| 没有数据来源 | 等同于瞎猜 | 每条数据附 [n] 引用 |
| 概率写 80% / 90% | 过度自信 | 强制 clamp 到 ≤ 65% |
| 跳过步骤 2 直接打分 | 拍脑袋 | 必须先搜证,再打分 |
| 给出"目标 50%" 的赔率 | 赔率 1:1 不值得入场 | 只参与 ≥ 2:1 赔率 |
| 把日内波动当趋势 | 噪声被当成信号 | 区分 1 日 / 5 日 / 20 日的尺度 |
---
## 资源引用
- **五维分析详细规则**:见 `references/five-dimension-framework.md`(打分细则、典型证据、常见陷阱)
- **术语通俗解释词典**:见 `references/terminology.md`(小白友好的术语表,每写一个术语前先查)
- **输出模板完整版**:见 `references/output-template.md`(可直接复制的 markdown 骨架)
- **搜证查询模板库**:见 `references/search-queries.md`(针对不同情境的关键词组合)
> 编写预测时,如果用到某个参考文件,先 Read 它再继续 — 不要凭印象生成内容。
---
## 自检清单(交付前 30 秒过一遍)
- [ ] 标的解析完整(ticker、交易所、板块、宏观关联)
- [ ] 至少 3 条 P0 搜证查询已执行
- [ ] 五维打分有具体证据 + 数据来源
- [ ] 加权得分算式清晰、可验证
- [ ] 方向概率 ≤ 65%,并有区间
- [ ] 交易计划包含入场/止损/目标/仓位/时间止损
- [ ] 至少 1 个 mermaid 或 ascii 图
- [ ] 至少 5 个专业术语有通俗解释
- [ ] 数据来源在文末以 [n] 编号列出
- [ ] 文件命名符合 `us-stock-{TICKER}-{YYYY-MM-DD}.md` 规范
- [ ] 文件保存到 `<项目根目录>/markdown/` 下
**未通过任何一项 = 重新写,不能交付。**© digoal, GPL-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. 2 hidden characters (zero-width or bidirectional) removed. Raw file
SKILL.md and 5 other files (references) in skills/us-stock-prediction of digoal/blog.
Open the folder on GitHubat commit ad6fcb7
Us Stock Prediction next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Us Stock Prediction this skilldigoal/blog | 8.6k | — | ~1.9k | Automated safety check: Warn | GPL-2.0 | |
| Bm Mdmiantiao-me/bm.md | 617 | — | ~2.1k | Automated safety check: Pass | LGPL-3.0 | |
| Feishu Doc Creatorop7418/CodePilot | 6.5k | 1 repos | ~3.1k | Automated safety check: Pass | Custom licence | |
| Markdown Syntax Guideantdigital-ai/agentic-ui | 224 | — | ~3.1k | Automated safety check: Pass | MIT | |
| Chatbot Mvp Distillationpdsuwwz/chatgpt-vue3-light-mvp | 579 | — | ~722 | Automated safety check: Pass | MIT | |
| Markdown Report WritingNeuroAIHub/BrainPilot | 1.1k | — | ~2.6k | Automated safety check: Warn | AGPL-3.0 |
miantiao-me/bm.md
使用 bm.md 写作、改写、排版或渲染 Markdown;生成 Mermaid 与 AntV Infographic,设置图片尺寸、高亮重点,以及执行 HTML/纯文本转换和 Markdown lint
op7418/CodePilot
Creates a new Feishu cloud document from Lark-flavored Markdown through the create-doc MCP tool, in a folder, wiki node or knowledge space.
antdigital-ai/agentic-ui
指导用户使用 @ant-design/agentic-ui 的 Markdown Editor / Renderer 扩展语法。图表场景优先使用内置 chart(HTML 注释 chartType + 表格),只有当内置 chartType 都不能表达诉求时才回退 Mermaid。Triggers on keywords like 表格, 视频, 图表, 卡片, 提示块, 流程图, 语法…
pdsuwwz/chatgpt-vue3-light-mvp
Distill the chatgpt-vue3-light-mvp project into reusable architecture for building similar ChatGPT-style web products in other repositories.
NeuroAIHub/BrainPilot
Guide AI agents to write beautifully formatted, well-illustrated Markdown reports with proper structure, diagrams, and compatibility across GitHub and Obsidian.
pdsuwwz/chatgpt-vue3-light-mvp
将 chatgpt-vue3-light-mvp 项目蒸馏为可迁移到其他项目的中文架构指南。适用于设计或实现类似 ChatGPT 的 Web 对话产品,包括 SSE/fetch 流式响应、模型适配器契约、打字机渲染、Markdown/代码/KaTeX/Mermaid 渲染、推理过程展示,以及从本 Vue 3 MVP 迁移到其他项目的方案规划。
digoal/blog
三层审查模型,逐段逐句验证文章真伪、证据链与逻辑结构。Use when the user asks to fact-check, verify, audit, or evaluate the credibility of an article, essay, report, opinion piece, social-media post, or any written claim —…
digoal/blog
Find latent bugs in a local PostgreSQL source tree (RELxxSTABLE branch or HEAD) the way a core hacker does: build a heavily-poisoned debug instance (cassert + cache-discard + -O0/-ggdb3 + core…
digoal/blog
Portable digital employee distilled from digoal's personal blog for PostgreSQL, PolarDB, DuckDB, AI+database, vector/RAG, database operations, source-code reading, technical content creation…
digoal/blog
从论文 PDF 文件或论文 PDF URL 生成通俗易懂、图文并茂、带批判性评估的中文 Markdown 解读,并保存到当前项目的 markdown 目录。Use when the user asks to interpret,精读,解读,summarize,explain,analyze, or write an article from an academic paper PDF…
digoal/blog
Analyze a product from documentation, websites, PDFs, articles, release notes, pricing pages, app listings, reviews, filings, or related links; save separate intermediate analyses from seven roles…
digoal/blog
Turn a blog post, article, notes, or any source material into a set of vertical poster images — one cover plus several coherent content slides that explain the core points.
Works with
美股个股次日走势预测 skill。当用户输入美股股票代码(如 AAPL、TSLA)或公司名称(如 "苹果"、"特斯拉"),并要求预测次日(T+1)走势、给出交易计划、评估盈亏概率时,触发本 skill。skill 会用 mcpMiniMaxwebsearch 搜证盘前 90…. Us Stock Prediction is an agent skill from digoal/blog.
Us Stock Prediction fits situations like: tasks that involve Web search; tasks that involve Diagrams; tasks that involve Markdown.
Run `npx skills add digoal/blog --skill us-stock-prediction -a claude-code`. Or copy the skill folder (skills/us-stock-prediction in digoal/blog) into .claude/skills/us-stock-prediction in your project. Claude Code loads it when a task matches its description.
Run `npx skills add digoal/blog --skill us-stock-prediction -a codex`. Or copy the skill folder (skills/us-stock-prediction in digoal/blog) into .agents/skills/us-stock-prediction in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add digoal/blog --skill us-stock-prediction -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/us-stock-prediction, .gemini/skills/us-stock-prediction, .github/skills/us-stock-prediction and .opencode/skills/us-stock-prediction in your project.
SKILL.md names no scripts, command-line tools or credentials: Us Stock Prediction is instructions for the agent only.
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Our automated static check of SKILL.md flagged 2 warning(s): contains zero-width characters. Read the flagged lines before installing; the check is not a guarantee either way.
Us Stock Prediction is published under the GPL-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 1.9k tokens (SKILL.md is roughly 7.7k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 9.4k tokens, read only when the agent opens those files.
Skills that share tags, products or a category with Us Stock Prediction: Bm Md (miantiao-me/bm.md, 617 stars), Feishu Doc Creator (op7418/CodePilot, 6.5k stars), Markdown Syntax Guide (antdigital-ai/agentic-ui, 224 stars) and Chatbot Mvp Distillation (pdsuwwz/chatgpt-vue3-light-mvp, 579 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
digoal (a GitHub user) maintains it in digoal/blog, which has 8,588 GitHub stars. The repository holds 98 skills in this directory. The repository was last updated on October 9, 2026.
Source: digoal/blog on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.