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A股信号追踪

该 Skill 针对用户已有的投资信号或论点,结合新新闻、价格与基本面信息逐节点比较,判断信号被强化、弱化、证伪、兑现或不变,并输出证据分组、变化说明、置信度调整及可选的结构化 InvestmentSignal JSON。

zoeluli7459-dev
zoeluli7459-dev
热度 28.53
最近更新 2026-07-27 19:09:00
分类:数据依赖:代码执行 / MCP 服务 / 第三方 API / 内部调用大模型应用场景:市场分析 / 财务 / 报告撰写 / 研究

安装技能

复制下面的话术发给你的 Agent,它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill: npx secure-skills-cli add zoeluli7459-dev/alphaear-signal-tracker
Metadata
name
alphaear-signal-tracker
description
Use this skill to track the evolution of an existing finance or investment signal when the user provides a prior thesis/signal and asks whether new market information strengthens, weakens, falsifies, realizes, or leaves it unchanged. It should compare baseline logic with updated news, prices, and fundamentals, then output a structured signal update. Use mx-search and mx-data for fresh facts as needed; do not use for simple news lookup, raw data lookup, stock screening, or diagram-only requests.

AlphaEar Signal Tracker

Track whether new market information changes an existing investment signal.

Inputs

  • Required: baseline signal or thesis, plus new event/time window or a request to refresh latest information.
  • Preferred baseline fields: title, affected tickers, transmission chain, expected catalyst, timeframe, confidence, sentiment, and falsification conditions.
  • If no baseline signal exists, first build one from the user's thesis and label assumptions clearly.

Workflow

  1. Parse the baseline signal: thesis, affected tickers, expected mechanism, time horizon, and falsification criteria.
  2. Gather fresh facts:
    • Use mx-search for news, announcements, reports, policy, and event context.
    • Use mx-data for price, volume, financial metrics, and other exact data.
  3. Compare new information with each baseline logic node.
  4. Classify evolution as Strengthened, Weakened, Falsified, Realized, or Unchanged.
  5. Update confidence, sentiment, expectation gap, and affected tickers only when evidence supports it.
  6. Use references/PROMPTS.md for the research, analysis, and tracking prompt patterns when generating strict signal JSON.

Output Contract

  • Start with the evolution label and one-sentence reason.
  • Include evidence grouped by news/event, price/data, and logic-chain impact.
  • Show what changed from the baseline and what did not.
  • Return structured JSON when the downstream workflow needs InvestmentSignal; otherwise provide a compact human-readable update.
  • Separate facts from inference and state unresolved uncertainties.

Failure Handling

  • Missing baseline: ask for the prior signal or create an explicitly provisional baseline.
  • No fresh data: state that evidence is insufficient and do not change the signal.
  • Conflicting evidence: mark mixed impact and explain which logic nodes conflict.
  • Data/search failures: report the failed source and avoid filling gaps from memory.

Validation

  • Run or review tests/test_tracker.py after changing scripts.
  • Use evals/evals.json to ensure tracker prompts trigger this skill while simple search/data/diagram prompts route elsewhere.