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trainer-memory-core

Automate investment thesis lifecycle management from registration to postmortem analysis.

1|Updated Feb 18, 2026
Search Tags:#data analysis#portfolio management#thesis management#investment tracking#postmortem analysis
One-click install
npx skills add https://github.com/darkounus90/BOTTX3 --skill trainer-memory-core
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: trainer-memory-core
Source: https://github.com/darkounus90/BOTTX3/tree/main/.agents/skills/trader-memory-core
Command: npx skills add https://github.com/darkounus90/BOTTX3 --skill trainer-memory-core

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pyyaml, jsonschema, datetime, pathlib, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill manages investment theses throughout their lifecycle, automating tasks and streamlining workflows from idea registration to postmortem analysis.

Core Features & Use Cases

  • Thesis Registration: Register and track theses with relevant data points such as ticker, type, and entry/exit details.
  • Automated Tracking: Monitor theses across different lifecycle stages with built-in transitions and state management.
  • Data Analysis: Attach position-sizing outputs and linked reports for in-depth analysis.
  • Postmortem Analysis: Generate structured postmortems with P&L and MAE/MFE analysis after position closure.
  • Use Case: Use this Skill to manage a portfolio of theses. As new ideas arise, register them with relevant details. Monitor their progress, make updates as needed, and generate detailed postmortems upon closing positions.

Quick Start

Register a new thesis with the 'register_thesis' command followed by the thesis details.

Frequently Asked Questions about trainer-memory-core

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I track investment theses from idea generation to postmortem analysis?▼

Investment thesis tracking is automated through a lifecycle management framework that registers ideas with ticker and entry data, monitors state transitions, and generates structured postmortems with P&L and MAE/MFE analysis upon position closure.

What is the best way to manage a portfolio of investment theses across different lifecycle states?▼

Portfolio thesis management is handled by registering each idea with relevant data points like entry and exit details, then using built-in state transitions to monitor progress across various investment types and lifecycle stages.

Do I need Python and specific libraries to automate investment thesis tracking?▼

Python is required to run this investment thesis automation framework, specifically utilizing pyyaml, jsonschema, datetime, and pathlib dependencies to manage data validation and lifecycle state processing.

Can I attach position-sizing outputs and linked reports for in-depth investment analysis?▼

Position-sizing outputs and linked reports can be attached to registered investment theses, providing structured data points for in-depth analysis throughout the lifecycle management process.

How do I generate structured postmortems with P&L and MAE/MFE analysis after closing a position?▼

Structured postmortems with P&L and MAE/MFE analysis are generated automatically by the investment tracking framework after a position is closed, summarizing performance and maximum adverse or favorable excursions.

What investment types does this thesis tracking framework support?▼

The investment thesis tracking framework supports various investment types, allowing users to register and monitor theses with flexible data points while maintaining consistent lifecycle state management across the portfolio.

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