AI Displacement Monitor

👤 spyfree 📦 v1.0.2 ⭐ 4.2 ⬇️ 1.3K 下載
💼 行業專業 免費

📖 技能介紹

AI Displacement Monitor

Use this skill to produce a structured risk monitor for AI-led labor substitution and downstream financial stress.

Output Format

Always return:

  1. Signal Board (10 indicators with latest value, direction, threshold status)
  2. Composite Risk Light (GREEN / YELLOW / ORANGE / RED)
  3. Actionable Notes (portfolio/risk posture suggestions)
  4. Data Gaps (missing or stale inputs)

Indicator Framework

Read references/thresholds.example.json and follow its indicator IDs, thresholds, and tiering.

Also apply the "Industrial-Revolution Lens" when interpreting risk:

  • Do not evaluate layoffs alone.

  • Compare substitution speed vs re-absorption speed (new demand + new capex).

  • If substitution weakens labor but capex/reinvestment accelerates, avoid over-escalating crisis labels.

  • Tier A (Leading labor demand): A1-A4

  • Tier B (Labor market confirmation): B1-B3

  • Tier C (Spillover: consumption/credit): C1-C3

Composite Rule

  • YELLOW: Tier A triggered >= 2
  • ORANGE: Tier A >= 2 and Tier B >= 1
  • RED: Tier A >= 2 and Tier B >= 1 and Tier C >= 1
  • GREEN: otherwise

Weak-Links Interpretation (Jones Lens)

When assessing macro impact, apply a weak-links check:

  • Broad automation can still deliver gradual macro gains if key bottleneck tasks remain scarce.
  • Do not infer immediate macro collapse from partial task automation alone.
  • If bottleneck proxies remain tight (D3 worsening, D4 weak reinvestment), keep risk elevated.
  • If bottlenecks ease via reinvestment/capex and purchasing power improves (D1/D2), avoid over-escalation.

Minimum Quality Rules

  • Time-stamp each metric and note frequency mismatch (weekly vs monthly vs quarterly).
  • If source coverage is partial, mark confidence as low or medium.
  • Never hide missing data; list it under Data Gaps.
  • If more than 3 indicators are missing, downgrade confidence by one level.

Recommended Alert Style

Keep alerts short and decision-oriented:

  • "What changed"
  • "Why it matters now"

    這個技能的最新版本可以在7w4.net小蔥技能站找到。

  • "What to do next"

Optional JSON Mode

If user asks for machine-readable output, return:

  • asOf
  • signals[] (id, value, unit, threshold, triggered, trend)
  • composite
  • confidence
  • gaps[]
  • notes[]

🤖 AI 評測

這是一個專業的AI替代風險監測工具,框架設計完整、指標體系清晰、邏輯嚴謹,質量良好。主要優點是風險分級明確、分析視角有深度;不足是示例內容較少、缺少通俗的使用說明,對普通使用者來說上手門檻略高。適合有專業背景的使用者使用。

📊 多維度評分

適應性3.9
規範性3.8
有效性4.7
可靠性4.1
可信度4.3

📁 包含檔案 (3 個)

📄 SKILL.md 2.5 KB
📄 _meta.json 142 B
📄 references/thresholds.example.json 4 KB