ticker-pipeline

ticker-pipeline is a skill for Claude Code, Codex from Fize/mmtickerlab. It costs 192 tokens per session (5,232 once invoked), scanned A, original, MIT.

A multi-agent stock research and risk workflow that gathers financial, market, ownership, institutional-flow, news, and broader economic information for a ticker and date. It combines those findings into a research report only when required data can be verified.

In plain words
What is it for?
Use it to examine company fundamentals, earnings and valuation, technical indicators, trading and institutional activity, news sentiment, and risk controls for a specified security.
Why use it?
It reduces the need to coordinate separate research tasks and blocks a report when key financial or market data is unavailable instead of filling gaps with invented figures.

Skill for Claude CodeCodex

Which agent this was written for is unclear — built for openclaw. Also seen: mentions subagents; built for openclaw.

Good fit Use it to examine company fundamentals, earnings and valuation, technical indicators, trading and institutional activity, news sentiment, and risk controls for a specified security.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/fize/mmtickerlab/ticker-pipeline
Install

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.

Any agent
npx skills add Fize/mmtickerlab --skill ticker-pipeline
Clone the repo
git clone --depth 1 https://github.com/Fize/mmtickerlab

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for ticker-pipeline

README.md
[![agentmods](https://agentmods.dev/badge/skills/fize/mmtickerlab/ticker-pipeline/github.svg)](https://agentmods.dev/skills/fize/mmtickerlab/ticker-pipeline)
Your own site
<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.

agentmods 80×15 button for ticker-pipeline

Your own site · 80×15
<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>
Per session 192 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 5,232 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured today against content hash 86a364df510d, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

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.

ticker-pipeline/SKILL.md · 309 lines

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)

  1. 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
  2. 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)。
  3. 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

Read the full file on GitHub · 309 lines

Files

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.

Changes

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.

  1. today Changed · +7 lines · +61 tokens per session 86a364df510d
  2. 4d ago First seen · 302 lines · 131 tokens per session scan A 7981ed81ea1a

Subscribe to this mod's changes

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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