name: ai-displacement-monitor description: Monitor early-warning signals of AI-driven white-collar labor displacement and macro-financial spillovers. Use when you need a practical indicator framework, thresholds, alert logic, and concise risk updates for employment, consumption, and credit stress.

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

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• 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" - "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[]

这是一个专业的AI替代风险监测工具，框架设计完整、指标体系清晰、逻辑严谨，质量良好。主要优点是风险分级明确、分析视角有深度；不足是示例内容较少、缺少通俗的使用说明，对普通用户来说上手门槛略高。适合有专业背景的用户使用。