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spyfree

AI Displacement Monitor

by Li Xin · GitHub ↗ · v1.0.2
cross-platform ✓ Security Clean
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Install in OpenClaw
/install 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,...
README (SKILL.md)

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"
  • "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[]
Usage Guidance
This skill is a coherent, instruction-only framework (no installs or secrets). Before using: (1) confirm how your agent will source indicator data—if it will call external APIs or scrape sites, decide which API keys/credentials you will supply and to which component; (2) review and, if needed, adapt the thresholds.example.json to match your data definitions and coverage; (3) if you plan to automate alerts, test outputs manually first (check timestamps, frequency mismatches, and data gaps as the skill recommends); (4) avoid granting broad platform credentials unless necessary—prefer scoped API keys for specific data feeds; and (5) if the agent implementation scrapes login-protected sites (LinkedIn, job boards), be aware of terms-of-service and privacy implications.
Capability Analysis
Type: OpenClaw Skill Name: ai-displacement-monitor Version: 1.0.2 The skill bundle is benign. It defines an AI displacement monitor, providing instructions for an AI agent in `SKILL.md` on how to interpret data, apply rules, and format output. The agent is instructed to read `references/thresholds.example.json`, which is a local data file containing the specific indicator thresholds and rules. There is no evidence of data exfiltration, malicious execution, persistence mechanisms, or prompt injection attempts designed to make the agent perform unauthorized or harmful actions beyond its stated purpose.
Capability Assessment
✓ Purpose & Capability
Name/description match the included indicator definitions and composite rules. The skill is instruction-only and uses the bundled thresholds.example.json for indicator IDs, tiers, triggers and interpretation; it does not request unrelated binaries, credentials, or filesystem access.
ℹ Instruction Scope
SKILL.md is narrowly scoped to producing an indicator board, composite risk light, notes and gaps and explicitly references the bundled thresholds file. It does not instruct the agent to read unrelated system files or environment variables. One ambiguity: the instructions assume the agent will obtain values for the indicators from external data sources (job boards, JOLTS, LinkedIn, market data) but do not specify how to fetch them or which credentials to use—this is a benign omission but means data-acquisition behavior depends on the host agent/integration.
✓ Install Mechanism
No install spec and no code files—this is an instruction-only skill. Nothing is written to disk or fetched during installation.
ℹ Credentials
The skill requests no environment variables or credentials, which is proportional to an indicator/interpretation framework. However, practical use will typically require access to third-party data (APIs or web scraping). The skill itself does not request those credentials, so the user or host agent must supply them separately; verify any credentials you provide are only given to trusted integrations.
✓ Persistence & Privilege
always is false and the skill does not request persistent system-wide configuration or modify other skills. Autonomous invocation is allowed by default but that is standard and not by itself concerning here.
How to Use
  1. Make sure OpenClaw is installed (local or Docker)
  2. Run the install command in chat: /install ai-displacement-monitor
  3. After installation, invoke the skill by name or use /ai-displacement-monitor
  4. Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.2
Add Weak-Links (Jones) interpretation layer for bottleneck-aware macro risk reading
v1.0.1
Add Industrial-Revolution Lens and 4 balancing indicators for substitution vs re-absorption dynamics
v1.0.0
Initial release: 10-indicator framework, composite risk light rules, and monitoring thresholds
Metadata
Slug ai-displacement-monitor
Version 1.0.2
License —
All-time Installs 1
Active Installs 1
Total Versions 3
Frequently Asked Questions

What is AI Displacement Monitor?

Monitor early-warning signals of AI-driven white-collar labor displacement and macro-financial spillovers. Use when you need a practical indicator framework,... It is an AI Agent Skill for Claude Code / OpenClaw, with 573 downloads so far.

How do I install AI Displacement Monitor?

Run "/install ai-displacement-monitor" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.

Is AI Displacement Monitor free?

Yes, AI Displacement Monitor is completely free (open-source). You can download, install and use it at no cost.

Which platforms does AI Displacement Monitor support?

AI Displacement Monitor is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).

Who created AI Displacement Monitor?

It is built and maintained by Li Xin (@spyfree); the current version is v1.0.2.

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