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 Fize/mmtickerlab --skill market-intelgit clone --depth 1 https://github.com/Fize/mmtickerlabWrote 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/fize/mmtickerlab/market-intel)<a href="https://agentmods.dev/skills/fize/mmtickerlab/market-intel"><img src="https://agentmods.dev/badge/skills/fize/mmtickerlab/market-intel/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/fize/mmtickerlab/market-intel"><img src="https://agentmods.dev/badge/skills/fize/mmtickerlab/market-intel.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00141 | $0.01488 |
| Opus 5.5 | $0.00056 | $0.00595 |
| Sonnet 5.5 | $0.00028 | $0.00298 |
| Haiku 4.5 | $0.00014 | $0.00149 |
Grade A, and why
market-intel scanned grade A with 0 findings 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 1mo 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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 91 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Market Intel — 市场情报与资讯扫描
本技能作为投研流水线的第一步（消息面与情报扫描）。核心原则：只陈述客观事实，不夹带主观推测或交易建议。
独立运行与补充数据核验
本技能可以单独运行，不要求先生成其他 Skill 的报告。需要补充数据时可使用权威网页或交易所来源，但每条补充数据必须记录来源、发布时间、行情时点、统计范围、单位和核验结果；来源冲突或口径不清时停止使用。输出只包含事实和证据，不复述用户任务或工具调用。
零虚构与数据真实性铁律（Zero-Fabrication Data Gate）
- 真实数据唯一原则：情报快报中的所有要闻、指数数值、汇率及资金流向金额必须 100% 真实。
- 多级真实数据获取路径：
- 第一优先：调用
market技能（参见market/SKILL.md）获取确定性市场情报与资讯数据； - 第二优先：若命令报错或暂时不可用，必须使用
search_web/read_url_content/tencent-news/agent-browser检索权威新闻与市场数据。
- 第一优先：调用
- 缺失即阻断（Fail-Fast）：若通过上述所有途径均无法获取到所需的关键真实数据，必须立即终止报告生成，直接告知用户：“由于无法获取 [具体缺失数据]，市场情报快报已安全阻断，拒绝提供未经证实的伪信息”。绝对禁止凭空编造、脑补或填充任何虚假新闻与数值！
真实数据采集（调用 market 技能）
在生成情报快报前，根据报告场景遵循渐进式披露原则，直接查阅 market/SKILL.md 调用对应能力：
| 业务场景 | market 对应指令与能力 |
采集核心事实 |
|---|---|---|
| 隔夜外盘与汇率 | overnight 命令 |
美股三大指数涨跌、富时 A50 实时截面与离岸人民币汇率 |
| 全市场涨跌停与梯队 | limits 命令 |
涨停池、炸板率、跌停池、连板梯队高度及晋级率 |
| 行业/概念资金流向 | flows 命令 |
行业与概念 1/3/5 日主力资金净流入/流出排行榜与主线异动 |
| 盘中大单异动 | big-deals 命令 |
针对特定时点的市场大单与超大单成交分布结构 |
| 标的针对性新闻 | news 命令 |
标的最新官方公告与权威财经媒体新闻原文 |
对于宏观政策通告与突发大事件，可结合外部 tencent-news 或 agent-browser 进行补充抓取。
情报归类与打标
获取到的每一条情报需进行四维打标：
- 资产类别：A 股 / 港股 / 美股 / ETF / 期货 / 大宗商品 / 宏观
- 区域范围：中国 / 美国 / 欧洲 / 亚太 / 全球
- 影响方向：正面（Positive） / 负面（Negative） / 中性（Neutral）
- 重要程度：
- 🔴 高（High）：直接影响大盘开盘或板块整体走势（如央行重磅政策、美股大跌、重大行业催化）；
- 🟡 中（Medium）：影响特定行业或个股（如财报发布、巨额大单、公司公告）；
- 🔵 低（Low）：常规行业背景资讯。
输出规范：市场情报快报（Market Intel Briefing）
根据采集的数据，输出以下格式的 Markdown 快报：
# [市场情报快报] YYYY-MM-DD
## 1. 🔴 重点要闻与宏观截面
| 时间/时点 | 资产/区域 | 核心事实摘要 | 数据源 | 影响方向 |
|---|---|---|---|---|
| 09:15 | 宏观/中国 | [核心事实描述，注明证据引用] | tencent-news | 中性 |
## 2. 隔夜外盘与跨市场截面
- **美股三大指数**：道琼斯 [E01], 标普500 [E02], 纳斯达克 [E03]
- **富时中国 A50**：[A50 时点最新价/涨跌幅]
- **外汇市场**：USD/CNY [汇率最新值]
## 3. 🟡 资金流向与市场结构
- **主力资金净流入 Top 5**：[行业/概念名称 (金额)]
- **主力资金净流出 Top 5**：[行业/概念名称 (金额)]
- **涨跌停与连板梯队**：涨停 [N] 家，炸板率 [N]%，最高连板高度 [N] 板
## 4. 🔵 关注动态与个股异动
- [个股大单成交/重大公告/新闻摘要]
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.
- 1mo ago Changed · +3 lines 3fc92d2a5e1f
- 1mo ago Changed · +5 lines · +80 tokens per session e83a0a476cac
- 1mo ago First seen · 83 lines · 61 tokens per session scan A 00449ac0bf06
market-intel is a skill published in the GitHub repository Fize/mmtickerlab (5 stars, last pushed 25d ago), licensed MIT. It adds 141 tokens to every session and 1,488 once invoked, about $0.0006 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-05.
Other skills, from other repositories
tushare
A Python interface for Tushare, a financial data service that provides market and company information for stocks, funds, futures, and digital assets. It returns queried data as pandas tables.
social-media-intelligence
Social media intelligence: financial signal extraction from Twitter/X, Telegram, Discord, and Reddit for sentiment-driven trading strategies.
credit-analysis
A guide to analysing bonds and other fixed-income investments, including issuer credit quality, interest payments, default risk, credit spreads, and convertible bonds. It also covers Chinese fixed-income markets and local-government financing bonds.
etf-analysis
A framework for comparing exchange-traded funds (ETFs), which are funds bought and sold on a stock exchange and usually track an index, industry, asset, or strategy. It covers fees, how closely an ETF follows its target, trading activity, and portfolio use.
geopolitical-risk
Geopolitical risk analysis: quantify crisis signals, identify precursors, and build event-driven strategies for war, sanctions, and supply disruption scenarios.
vibe-trading
Professional finance research toolkit — backtesting (11 engines + benchmark comparison panel), factor analysis, Alpha Zoo (478 pre-built alphas across qlib158/alpha101/gtja191/academic/fundamental), options pricing, 90 finance skills, 30 multi-agent swarm teams, Trade Journal analyzer, and Shadow Account (extract →…