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:

• Signal Board (10 indicators with latest value, direction, threshold status)
• Composite Risk Light (GREEN / YELLOW / ORANGE / RED)
• Actionable Notes (portfolio/risk posture suggestions)
• 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"
• "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[]