serenity-skill: a supply-chain research agent skill for identifying high-value investment targets in tech trends

What it solves

Serenity.skill helps investors navigate high-heat AI and tech trends (such as semiconductors, robotics, and CPO) by transforming vague market hype into a structured, evidence-based research process. It prevents investors from blindly following trends by forcing a systematic analysis of the supply chain to identify where the actual bottlenecks and high-value opportunities lie.

How it works

The project implements a research methodology based on the public investment research patterns of Serenity (@aleabitoreddit). It guides an AI agent to follow a specific workflow:

1. Deconstruct the Trend: Breaks down a hot topic into downstream demand, system integration, chips/devices, equipment, materials, packaging/testing, and infrastructure.
2. Identify Bottlenecks: Searches for segments with few suppliers, long validation cycles, difficult expansion, or strict customer certifications.
3. Map to Assets: Connects these bottlenecks to specific stocks or fund directions.
4. Verify Evidence: Cross-references findings with official announcements, financial reports, exchange filings, patents, and professional analysis.
5. Prioritize: Ranks candidates based on their proximity to the real bottleneck and the strength of the evidence.

Who it’s for

Investors who deal with high-volume information streams and want a systematic way to filter stocks and funds based on supply chain logic rather than social media hype.

Highlights

• Agent-Ready: Designed as a "Skill" that can be installed into various AI agent clients (like Claude Code, Codex, or Hermes Agent).
• Evidence-Based: Prioritizes hard evidence (filings, reports) over social media clues.
• Structured Output: Provides a prioritized research list with clear logic for why certain targets are ranked higher.
• Bottleneck Scoring: Includes a Python script to generate and run a "bottleneck scorecard" for company evaluation.