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 ticker-pipelinegit 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/ticker-pipeline)<a href="https://agentmods.dev/skills/fize/mmtickerlab/ticker-pipeline"><img src="https://agentmods.dev/badge/skills/fize/mmtickerlab/ticker-pipeline/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/ticker-pipeline"><img src="https://agentmods.dev/badge/skills/fize/mmtickerlab/ticker-pipeline.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 | $0.00192 | $0.05232 |
| Opus 5 | $0.00096 | $0.02616 |
| Sonnet 5 | $0.00038 | $0.01046 |
| Haiku 4.5 | $0.00019 | $0.00523 |
Grade A, and why
ticker-pipeline 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 today.
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 — 309 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Ticker Pipeline — 多 Agent 并行投研与风控决策流水线
本技能作为端到端的多智能体协同投研操作系统。通过并发调度多个专业 Subagent,实现从底层事实采集、基本面估值核算、筹码与机构热度透视、全网消息舆情扫描,到跨维度综合评级,最后经由独立量化风控门禁行使一票否决权(VETO Authority),输出高置信度的决策总报。
零虚构与无数据坚决不输出研报铁律(Zero-Fabrication Data Gate)
- 真实数据绝对唯一性:
- 流水线中引用的所有基本面财务数据(最新 EPS、归母净利润、营收增速、净资产)、行情与技术指标(最新价、涨跌幅、均线、MACD、RSI、布林带)、筹码数据(获利盘比例、平均成本、集中度)、机构资金流(1/3/5日净流入、主力净买额、龙虎榜席位)以及宏观/外盘资讯,必须 100% 为确定性真实数据。
- 渐进式披露与多级数据获取路径:
- 第一优先:各专员遵照渐进式披露原则,查阅并调用本仓库
market技能(具体命令与参数规范直接参见market/SKILL.md)获取确定性数据; - 第二优先:若命令遇到网络波动、港美股特定财报字段未包含或问财受限,必须通过
search_web/read_url_content/tencent-news/agent-browser检索官方公告、交易所数据(上交所/深交所/港交所/SEC)或权威财经媒体(新华财经、彭博、路透、东方财富)。
- 第一优先:各专员遵照渐进式披露原则,查阅并调用本仓库
- 缺失即阻断(Fail-Fast):
- 若通过上述所有途径均无法获取到标的的核心真实财务(EPS/净利润)或行情数据,流水线必须立即无条件安全终止;
- 直接向用户输出《数据盲区安全阻断通知》,坚决严禁编造任何虚假财务数字、脑补技术指标或输出毫无数据支撑的虚构研报!
Subagent 架构与规范索引 (Agents Index)
流水线将各专业 Subagent 的系统 Prompt 与专项分析规范独立存放于 agents/ 目录下,便于查看、审查与独立演进:
| 专员代号 (Role) | 职能定位 | 数据源依赖 | 专属 Agent 规范文件 |
|---|---|---|---|
fundamental-valuation-agent |
真实财务 EPS、5 种多模型估值、技术面四维体检 | market 技能 (quote/financials/technical) |
agents/fundamental_valuation.md |
market-heat-agent |
筹码分布、获利盘比重、主力资金流向、龙虎榜席位 | market 技能 (chips/stock-flow/lhb) |
agents/market_heat.md |
intel-news-agent |
宏观政策、行业赛道、个股重大事件公告四维打标 | market 技能 (news/flows/overnight) + 搜索 |
agents/intel_news.md |
risk-guard-agent |
逻辑自洽交叉核验、大势乘数、4 大红线与一票否决 | market 技能 (snapshot/limits/technical) |
agents/risk_guard.md |
流水线整体架构
flowchart TD
Start(["输入: 标的代码 CODE + 日期 DATE"]) --> Gate{"数据可得性审查"}
subgraph Phase1["第一阶段: 多 Agent 并发深度调研 (invoke_subagent)"]
direction LR
AgentA["Subagent A: fundamental-valuation-agent<br/>(参见 agents/fundamental_valuation.md)"]
AgentB["Subagent B: market-heat-agent<br/>(参见 agents/market_heat.md)"]
AgentC["Subagent C: intel-news-agent<br/>(参见 agents/intel_news.md)"]
end
Gate -->|通过| Phase1
Gate -->|核心数据缺失| FailFast["安全阻断: 输出《数据盲区安全阻断通知》<br/>(绝不编造虚假研报)"]
subgraph Phase2["第二阶段: 逻辑汇总与决策评级合成"]
Synth["主调度器汇聚三方事实<br/>计算悲观/基准/乐观三档市值与买卖评级"]
end
Phase1 --> Synth
subgraph Phase3["第三阶段: 独立量化风控门禁 (invoke_subagent)"]
AgentD["Subagent D: risk-guard-agent<br/>(参见 agents/risk_guard.md)"]
end
Synth --> Phase3
Phase3 --> VetoCheck{"VETO 裁决"}
VetoCheck -->|VETO: PASSED| FinalReport["交付: 《标的全维度投研与风控决策总报》"]
VetoCheck -->|VETO: BLOCKED| VetoReport["交付: 《风控阻断安全警示》<br/>(驳回买入评级,强制观望/减仓)"]
market[("market 技能<br/>(底层确定性数据底座)")] -.->|渐进式披露调用| AgentA & AgentB & AgentC & AgentD
What ships with it
5 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.
- today Changed · +7 lines · +61 tokens per session 86a364df510d
- 4d ago First seen · 302 lines · 131 tokens per session scan A 7981ed81ea1a
ticker-pipeline is a skill published in the GitHub repository Fize/mmtickerlab (5 stars, last pushed today), licensed MIT. It adds 192 tokens to every session and 5,232 once invoked, about $0.0010 per session on Opus 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.
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