Brave Search
badlogic/pi-skills
Web search and content extraction via Brave Search API. An agent skill from badlogic/pi-skills.
股票分析与涨跌预测分析. An agent skill from zai-org/GLM-skills.
$ npx skills add zai-org/GLM-skills --skill glmv-stock-analyst -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install zai-org/GLM-skills glmv-stock-analyst --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/zai-org/GLM-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/glmv-stock-analyst .claude/skills/glmv-stock-analyst && 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 "glmv-stock-analyst" agent skill from https://github.com/zai-org/GLM-skills/tree/main/skills/glmv-stock-analyst into .claude/skills/glmv-stock-analyst/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "glmv-stock-analyst", 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/zai-org/GLM-skills/tree/main/skills/glmv-stock-analystType 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 zai-org/GLM-skills --skill glmv-stock-analyst -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install zai-org/GLM-skills glmv-stock-analyst --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/zai-org/GLM-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/glmv-stock-analyst .agents/skills/glmv-stock-analyst && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "glmv-stock-analyst" agent skill from https://github.com/zai-org/GLM-skills/tree/main/skills/glmv-stock-analyst into .agents/skills/glmv-stock-analyst/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "glmv-stock-analyst", 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 zai-org/GLM-skills --skill glmv-stock-analyst -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install zai-org/GLM-skills glmv-stock-analyst --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/zai-org/GLM-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/glmv-stock-analyst .cursor/skills/glmv-stock-analyst && 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 "glmv-stock-analyst" agent skill from https://github.com/zai-org/GLM-skills/tree/main/skills/glmv-stock-analyst into .cursor/skills/glmv-stock-analyst/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "glmv-stock-analyst", 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/zai-org/GLM-skills.git --path skills/glmv-stock-analyst--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 zai-org/GLM-skills --skill glmv-stock-analyst -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install zai-org/GLM-skills glmv-stock-analyst --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/zai-org/GLM-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/glmv-stock-analyst .gemini/skills/glmv-stock-analyst && 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 "glmv-stock-analyst" agent skill from https://github.com/zai-org/GLM-skills/tree/main/skills/glmv-stock-analyst into .gemini/skills/glmv-stock-analyst/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "glmv-stock-analyst", 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 zai-org/GLM-skills glmv-stock-analystInstalls 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 zai-org/GLM-skills --skill glmv-stock-analyst -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/zai-org/GLM-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/glmv-stock-analyst .github/skills/glmv-stock-analyst && 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 "glmv-stock-analyst" agent skill from https://github.com/zai-org/GLM-skills/tree/main/skills/glmv-stock-analyst into .github/skills/glmv-stock-analyst/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "glmv-stock-analyst", 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 zai-org/GLM-skills --skill glmv-stock-analyst -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install zai-org/GLM-skills glmv-stock-analyst --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/zai-org/GLM-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/glmv-stock-analyst .opencode/skills/glmv-stock-analyst && 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 "glmv-stock-analyst" agent skill from https://github.com/zai-org/GLM-skills/tree/main/skills/glmv-stock-analyst into .opencode/skills/glmv-stock-analyst/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "glmv-stock-analyst", 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.
glmv-stock-analyst股票分析与涨跌预测分析. An agent skill from zai-org/GLM-skills.
Glmv Stock Analyst is an agent skill from zai-org/GLM-skills. 股票分析与涨跌预测分析。 在用户表达分析、判断或预测意图时触发,如“分析一下腾讯”、“0700最近走势如何”、“XX能不能买”、“预测一下后续走势”、“生成一份分析报告”等; 支持港股、A股、美股,整合多源数据(包括新闻、基本面、技术面、资金流及宏观信息)进行多维综合分析,输出图文结合、包含可视化图表的结构化分析报告。 对于简单查询类需求(如“腾讯当前价格是多少”、“茅台代码是什么”)不触发本skill, 直接通过websearch 能力搜索并总结。 ⚠️ 需要多模态主模型支持(如 glm-5v-turbo),主模型需能读取图片。
Its SKILL.md is about 2.7k tokens, which your agent loads only when the skill is triggered. The skill folder holds 12 other files, including scripts and reference files (for example `references/hk_stock_knowledge.md`, `references/report_template.md` and `references/sensitive_companies.md`).
It sits in Productivity & Automation, covering Web search. The repository describes itself as: Official skills for the GLM family of models. The licence is Apache-2.0.
8 steps, taken from the step headings in SKILL.md.
Read from SKILL.md and the folder at commit 2ecd31c. 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.
Ships 7 files in scripts/ (Python and Shell), which the agent can run.
Shell commands in SKILL.md call:
pythonbashFrom 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 these keys or tokens, usually read from environment variables:
TUSHARE_TOKENFrom names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Glmv Stock Analyst loads about 2.7k tokens when it runs, and up to ~4.8k if it reads all its reference files. Until then it costs about 72 tokens; SKILL.md has 526 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 no risky patterns in SKILL.md.
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); the scripts in this folder are not scanned.
The full file from zai-org/GLM-skills at commit 2ecd31c, republished under its Apache-2.0 licence (© zai-org). 526 words, ~2,667 tokens.
.claude/skills/glmv-stock-analyst/SKILL.md (or your agent's skills folder). This skill also uses 10 other files; get the full folder from GitHub.路径约定:
{SKILL_DIR}指向 skill 根目录(即 SKILL.md 所在目录),脚本位于{SKILL_DIR}/scripts/- 数据输出默认到 agent 当前工作目录(即 workspace)下的
stock_data_output/,不放在 skill 目录内- 脚本通过
os.getcwd()自动定位 workspace,无需手动指定路径
{SKILL_DIR}/
├── SKILL.md # 本文件(流程指令)
├── scripts/
│ ├── setup.sh # ⭐ 环境初始化(只需运行一次)
│ ├── venv/ # Python 虚拟环境(setup.sh 自动创建)
│ ├── fetch_all.py # 数据采集 + 图表生成(纯数据,不写报告)
│ ├── md2html.py # Markdown → HTML(专业CSS模板转换器)
│ └── export_report.py # Markdown → PDF(可选导出)
└── references/
├── report_template.md # ⭐⭐ report.md 完整模板 + 写作规则
├── hk_stock_knowledge.md # 港股/A股专业知识
└── sensitive_companies.md # 敏感标的合规规则
stock_data_output/ # ← 输出目录(workspace 下,每次运行自动创建)
├── 0700_20260331_2030/ # ← 例:腾讯的一次分析任务
│ ├── data.json # 原始数据
│ ├── summary.json # 数据摘要
│ ├── kline_em.png # 日K线图
│ ├── kline_intraday.png # 分时图
│ ├── report.md # ⭐⭐ 模型写的精炼Markdown详细报告
│ ├── report.html # 🌐 md2html自动生成的网页
│ └── report.pdf # 📄 可选导出PDF
└── 00981_20260331_2032/ # ← 另一次任务(完全独立文件夹)你是一名服务中国投资者的分析师。用户主要关注中国公司(港股/A股/美股均有),也会看美股常见公司。你需要综合所有可获得的信息,给出专业的、可解释的涨跌判断。
关键能力:你是多模态模型,可以直接看到图片。 脚本生成的 K 线图等,你可以直接用视觉能力分析。
┌─────────────┐ 数据+图片(独立任务文件夹) ┌─────────────┘
│ fetch_all.py │ ─────────────────────────→ │ 主模型(你) │
│ (数据采集) │ stock_data_output/ │ (多模态分析) │
│ │ {code}_{timestamp}/ │ + web_search │
│ │ ├─ data.json │ + 精准新闻 │
│ │ ├─ summary.json │ │
│ │ ├─ kline_em.png │ │
│ │ └─ kline_intraday.png │ │
└─────────────┘ └───────┬───────┘
│
┌──────────────┼──────────────┐
↓ ↓ ↓
════════════ ════════════ ════════════
webchat回复 report.md report.html
(精炼总结) (详细报告) (md2html转换)
真实图片 专业CSS
详细分析 浏览器打开
↓ 可选
report.pdf核心原则:
stock_data_output/,方便追溯和导出PDFcd {SKILL_DIR}/scripts && bash setup.shsetup.sh 会自动:
requirements.txt 中的全部依赖所有依赖统一在 requirements.txt 中管理,不需要单独 pip install。
配置 Tushare Token 获取更稳定的 A 股数据:
export TUSHARE_TOKEN="your_token"不论你是否认得这只股票,都必须先搜索一次。
搜索内容:{用户说的名字} 股票代码 上市 港股 OR A股 OR 美股,时间范围选 past_year
本搜索借用已有web_search能力,
如果搜索失败,继续尝试其他有web_search能力的工具,如果全部失败提醒用户需要先配置一种web_search工具,或直接给出股票代码
根据搜索结果:
references/sensitive_companies.md,进 Step 1{名字} IPO 上市 2025 OR 2026 确认代码格式:港股 0700.HK | A股 600519.SS | 美股 AAPL
{SKILL_DIR}/scripts/venv/bin/python {SKILL_DIR}/scripts/fetch_all.py {股票代码} [--adr {ADR代码}]示例:
# 港股(腾讯)
{SKILL_DIR}/scripts/venv/bin/python {SKILL_DIR}/scripts/fetch_all.py 0700.HK --adr TCEHY
# 港股(中芯国际)
{SKILL_DIR}/scripts/venv/bin/python {SKILL_DIR}/scripts/fetch_all.py 00981.HK
# A股(茅台)
{SKILL_DIR}/scripts/venv/bin/python {SKILL_DIR}/scripts/fetch_all.py 600519.SS
# 美股(苹果)
{SKILL_DIR}/scripts/venv/bin/python {SKILL_DIR}/scripts/fetch_all.py AAPL环境说明: 首次使用需先运行
bash setup.sh创建 venv 并安装依赖。之后所有命令统一使用./venv/bin/python。
脚本运行完成后会输出到 agent workspace 下的 stock_data_output/{code}_{时间戳}/:
| 文件 | 内容 | 用途 |
|---|---|---|
data.json | 全部结构化原始数据 | 模型读取分析 |
summary.json | 数据摘要 | 快速概览 |
kline_em.png | 日K线图(东方财富) | ⭐ 核心技术分析依据 |
kline_intraday.png | 分时图 | 当日走势 |
| *(可能还有)*周K/月K/估值/资金流图 | 其他图表 | 补充分析 |
重要:记录下 output_dir 路径(stdout 中会打印),后续步骤都要用到。
# output_dir 会打印在脚本 stdout 中,路径在 workspace 下
read("{output_dir}/summary.json")
read("{output_dir}/data.json")重点关注:
images 字段 → 所有可用图片路径你是多模态模型,必须亲自看图! 这是本 skill 最核心的价值。
# 必看:日K线图(最重要)
read("{output_dir}/kline_em.png")
# 必看:分时图(当日走势)
read("{output_dir}/kline_intraday.png")
# 有则看:其他图表
read("{output_dir}/kline_weekly.png") # 周K(如有)
read("{output_dir}/capital_flow.png") # 资金流向(如有)看图时关注:
⚠️ 新闻必须精准相关!不要无关的全市场快讯!
用 web_search 至少搜 2-3 次:
"{股票名称}" "{股票代码}" 最新 2026年{月} — 近期事件和动态"{股票名称}" 分析师 评级 目标价 研报 — 专业观点"{股票名称}" 资金流向 南向资金 卖空 — 资金面(港股必搜)新闻筛选原则:
为什么不用脚本内置的财联社新闻? 因为脚本按代码过滤经常匹配不到(如传入"0700.HK"而非"腾讯控股"),fallback会返回全市场无关快讯。模型自己搜索更精准。
web_search "{公司名} 业绩演示 投资者演示 PPT 2026"找到 PDF 后可用 fetch_ir.py 提取图表。找不到就跳过,不要卡住。
fetch_ir.py 安全说明: 该脚本仅接受 HTTPS URL 下载 PDF(拒绝 file://、http:// 等非安全 scheme),下载大小限制 50 MB,最大处理 50 页。
这是最关键的一步,需要同时输出两份内容。
⚠️ 执行顺序(严格遵守,不要跳步):
report.md 到任务文件夹md2html.py)open report.html)— 可在回复之后异步执行核心原则:report.md 和 HTML 必须在回复用户之前完成,浏览器打开可以在回复后执行,但绝不能漏掉。
这是用户在聊天窗口看到的内容。必须基于 Step 1~3 获取的真实信息,禁止编造数据。 没获取到就如实说明"暂无数据"。 对新上市或高估值科技/AI企业保持中立。 这类公司处于业务扩张期,PE/PS 偏高是行业特征而非异常,不要单纯因为估值指标偏高就给出看空或极端评级。应结合行业前景、增长速度、市场空间等综合判断,区分"估值泡沫"和"成长溢价"。
必须包含以下 5 个部分:
═══ 第1部分:标题 ═══
📊 {股票名} ({代码}) 快速总结
═══ 第2部分:核心数据 ═══
**股价:** XX元(今日±X%)← 必须来自脚本输出或搜索结果
**市值:** ~XX亿
**关键财务:** (最新一期)营收/净利/PE 或 预亏数据 ← 必须来自搜索
**资金面:** 今日主力净流入/流出 + 近期趋势 ← 必须来自脚本或搜索
═══ 第3部分:近期走势分析(⭐重点,不能省略!) ═══
用3-6句话描述,每句话都要有数据或事件支撑:
- 整体处于什么阶段(上涨/下跌/震荡/破位)
- 关键转折点和原因(如"X月Y日因Z事件暴涨/暴跌" ← 来自搜索新闻)
- 当前技术状态(均线排列、支撑压力 ← 来自看图分析)
- 成交量/量价配合情况 ← 来自看图分析
- 和基本面的关系(如果背离要指出)
⚠️ 禁止凭空编造走势描述!所有转折点、事件、数据必须来自 Step 1~3 的实际获取结果。
═══ 第4部分:多空对比表 ═══
| 🟢 做多逻辑 | 🔴 做空逻辑 |
|-----------|-----------|
| 因素1 | 因素1 |
| 因素2 | 因素2 |
每侧2-4条,必须是搜索到的事实不是空话。
═══ 第5部分:结论 + 操作建议(⭐重点!) ═══
**总评级:** 🟢买入 / 🟡观望 / 🔴回避 / ⚠️高风险(一句话理由)
**操作建议(分角色):**
- 已持仓者:该怎么做
- 观望者:能不能买/什么时候买
- 短线/激进者:如果有机会该怎么玩
- 特别提示:(如有A/H选择、期权策略等)
═══ 第6部分:详细报告提示 ═══
📄 详细报告 → report.html(浏览器已打开),需导出PDF随时说。⚠️ webchat 回复的写作规则:
这是核心产物。 详细、真实图片、推理过程完整。用户在浏览器/PDF中深度阅读用的。
⚠️ 图片策略分层(极其重要!):
| 输出渠道 | 图片类型 | 原因 |
|---|---|---|
| report.md | ✅ 优先用真实图片  | 浏览器可渲染,信息密度高 |
| webchat回复 | ❌ 不放文本折线图 — 只用文字精炼总结 | webchat 回复不自己画图 |
简单说:report.md = 图文并茂的详细研报;webchat = 精炼的文字速报。
完整结构和写作规则见 references/report_template.md,写入前务必先读取。
模板核心要点:技术面放图 → 基本面 → 资金流向 → 事件时间线 → 分隔线 → 综合判断 → 翻转条件 → 风险提示
写入方式:
write(content=报告markdown内容, path="{output_dir}/report.md"){SKILL_DIR}/scripts/venv/bin/python {SKILL_DIR}/scripts/md2html.py {output_dir}/report.html -i {output_dir}/report.md
open {output_dir}/report.htmlmd2html.py 会自动:
file:// 绝对路径(浏览器可直接显示)每次分析结束后,必须在 webchat 回复末尾加上这句提示:
📄 详细报告已在浏览器中打开,需要导出 PDF 版吗?告诉我即可生成。
用户要求导出时执行:
{SKILL_DIR}/scripts/venv/bin/python {SKILL_DIR}/scripts/export_report.py {output_dir}/report.md --format pdf
open {output_dir}/report.pdf提示话术: "需要导出 PDF 版吗?告诉我即可生成。"
| 类别 | 数据源 | 用途 | 说明 |
|---|---|---|---|
| K线图(图片) | 东方财富 webquotepic | 日K、分时图直链下载 | ✅ 稳定可靠 |
| K线图(数据) | akshare / yfinance / tushare | 周K、月K数据(本地绘图) | ⚠️ 代理问题 |
| 个股行情 | 东方财富 quote.eastmoney.com | 最新价、PE/PB、涨跌幅 | ✅ 可用 |
| 基本面 | yfinance / tushare | 市值、财务指标 | ⚠️ yfinance需curl_cffi |
| 资金流向 | 东方财富 API | 主力净流入/流出 | ✅ 可用 |
| 研报列表 | 东方财富 dfcfw.com | 券商评级、目标价 | ✅ 可用 |
| 新闻 | 财联社 cls.cn | 实时快讯 | ❌ 过滤不精准,优先用模型搜索 |
| 宏观 | (外部API) | 利率、PMI等 | ⚠️ 未稳定配置 |
| 工具 | 搜索内容 | 优势 |
|---|---|---|
| web_search | "{股票名} 最新新闻" | 精准匹配标的 |
| web_search | "{股票名} 分析师 评级" | 专业观点 |
| web_search | "{股票名} 资金流向 南向" | 资金面动态 |
| 追问类型 | 处理方式 |
|---|---|
| "那 XX 呢?" | 新股票走完整流程(新任务文件夹) |
| "XX 最新消息" | 只搜新闻 + 精炼回复 |
| "如果明天低开?" | 场景分析(基于已有数据) |
| "比较 XX 和 YY" | 各自分析后对比 |
| "导出 PDF" | Step 7 导出流程 |
| "再看看 XX" | 同一只股票重新跑脚本(新时间戳文件夹) |
references/sensitive_companies.md© zai-org, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
SKILL.md and 10 other files (scripts, references) in skills/glmv-stock-analyst of zai-org/GLM-skills.
Open the folder on GitHubat commit 2ecd31c
Glmv Stock Analyst 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 |
|---|---|---|---|---|---|---|
| Glmv Stock Analyst this skillzai-org/GLM-skills | 476 | — | ~2.7k | Automated safety check: Pass | Apache-2.0 | |
| Brave Searchbadlogic/pi-skills | 2.6k | 5 repos | ~592 | Automated safety check: Pass | MIT | |
| Enterprise AI Scenario MapMetaInFLow/Enterprise-ai-scenario-map-skill | 632 | — | ~1.8k | Automated safety check: Pass | MIT | |
| Web Searchjjyaoao/HelloAgents | 3.2k | 1 repos | ~5.6k | Automated safety check: Pass | MIT | |
| Ddg SearchTheSyart/claude-agent-examples | 407 | 1 repos | ~493 | Automated safety check: Pass | None | |
| Local Web SearchuluckyXH/OpenMOSS | 1.3k | — | ~392 | Automated safety check: Notes | MIT |
badlogic/pi-skills
Web search and content extraction via Brave Search API. An agent skill from badlogic/pi-skills.
MetaInFLow/Enterprise-ai-scenario-map-skill
企业AI场景地图生成报告工具。通过 web-search 深度调研企业信息,按照V2.1标准模板生成结构化AI应用场景地图报告,包含企业画像、业务诊断、行业实践、AI场景全量表、实施路径等完整内容。
jjyaoao/HelloAgents
Implement web search capabilities using the z-ai-web-dev-sdk.
TheSyart/claude-agent-examples
Web search without an API key using DuckDuckGo Lite via webfetch.
uluckyXH/OpenMOSS
A skill your agent uses when the user asks for web search that should run via the local-160 Responses API with websearch tool (base URL like https://proxy.example.com, model gpt-5.2-codex(xhigh)).
ythx-101/ask-search
Web search via self-hosted SearxNG. An agent skill from ythx-101/ask-search.
zai-org/GLM-skills
Extract text from images using GLM-OCR API. An agent skill from zai-org/GLM-skills.
zai-org/GLM-skills
Official skill for generating high-quality images from text prompts using ZhiPu GLM-Image API.
zai-org/GLM-skills
Official skill for recognizing and extracting mathematical formulas from images and PDFs into LaTeX format using ZhiPu GLM-OCR API.
zai-org/GLM-skills
Official skill for recognizing handwritten text from images using ZhiPu GLM-OCR API.
zai-org/GLM-skills
Official skill for recognizing and extracting tables from images and PDFs into Markdown format using ZhiPu GLM-OCR API.
zai-org/GLM-skills
Generate captions (descriptions) for images, videos, and documents using ZhiPu GLM-V multimodal model series.
Categories
股票分析与涨跌预测分析. An agent skill from zai-org/GLM-skills. Glmv Stock Analyst is an agent skill from zai-org/GLM-skills.
Glmv Stock Analyst fits situations like: tasks that involve Web search.
Run `npx skills add zai-org/GLM-skills --skill glmv-stock-analyst -a claude-code`. Or copy the skill folder (skills/glmv-stock-analyst in zai-org/GLM-skills) into .claude/skills/glmv-stock-analyst in your project. Claude Code loads it when a task matches its description.
Run `npx skills add zai-org/GLM-skills --skill glmv-stock-analyst -a codex`. Or copy the skill folder (skills/glmv-stock-analyst in zai-org/GLM-skills) into .agents/skills/glmv-stock-analyst 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 zai-org/GLM-skills --skill glmv-stock-analyst -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/glmv-stock-analyst, .gemini/skills/glmv-stock-analyst, .github/skills/glmv-stock-analyst and .opencode/skills/glmv-stock-analyst in your project.
Going by SKILL.md and its folder, Glmv Stock Analyst needs Python and a shell for the scripts in its folder, the command-line tools its instructions call (python and bash) and credentials named TUSHARE_TOKEN. Our summary lists: Python 3; A Bash shell; A credential in TUSHARE_TOKEN.
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 found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
Glmv Stock Analyst is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 2.7k tokens (SKILL.md is roughly 11k 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 2.1k tokens, read only when the agent opens those files.
Skills that share tags, products or a category with Glmv Stock Analyst: Brave Search (badlogic/pi-skills, 2.6k stars), Enterprise AI Scenario Map (MetaInFLow/Enterprise-ai-scenario-map-skill, 632 stars), Web Search (jjyaoao/HelloAgents, 3.2k stars) and Ddg Search (TheSyart/claude-agent-examples, 407 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
zai-org (a GitHub organization) maintains it in zai-org/GLM-skills, which has 476 GitHub stars. The repository holds 16 skills in this directory. The repository was last updated on April 15, 2026.
Source: zai-org/GLM-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.