compound

Analyze losing trades to classify failures and generate knowledge-base entries.

Updated Apr 14, 2026
One-click install
npx skills add https://github.com/raosuraj23/alpaca-bot --skill compound-raosuraj23
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: compound
Source: https://github.com/raosuraj23/alpaca-bot/tree/main/.claude/skills/compound
Command: npx skills add https://github.com/raosuraj23/alpaca-bot --skill compound-raosuraj23

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

Compound learning — post-mortem analysis of losing trades writes lessons to knowledge base, feeds back into scan and research.

Core Features & Use Cases

  • Post-mortem analysis for every loss to extract root causes and generate knowledge-base entries.
  • Integrates with Reflection Engine and nightly consolidation to update failure_log.jsonl and metrics_log.jsonl.
  • Supports deterministic classification and structured KB entries for long-term calibration.

Quick Start

Analyze each losing trade to generate a KB entry and refine strategy parameters.

Frequently Asked Questions about compound

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

FAQPage Schema
How do I automate post-mortem analysis for losing trades?

Automating post-mortem analysis for losing trades classifies failures deterministically and generates structured knowledge-base entries to refine your trading strategy. It extracts root causes from each loss to update failure logs and risk guardrails.

What is deterministic failure classification in trading analysis?

Deterministic failure classification in trading analysis systematically categorizes losing trades to extract actionable lessons. It formats these insights into knowledge-base entries using Haiku-based formatting for long-term strategy calibration.

How do I generate knowledge-base entries from trading losses?

To generate knowledge-base entries from trading losses, the system applies Haiku-based KB formatting to extract and structure root causes. This creates actionable lessons for nightly consolidation and strategy adjustment.

Can I integrate post-mortem analysis with an automated trading backend?

Yes, you can integrate post-mortem analysis with automated trading backends, live trader desks, and simulated environments. It connects with backend knowledge logs, updates metrics_log.jsonl, and triggers risk guardrail adjustments.

What's the best way to consolidate trading analysis logs nightly?

The best way to consolidate trading analysis logs nightly uses a Reflection Engine to aggregate daily post-mortem entries. It updates failure_log.jsonl and generates summaries to prompt strategy parameter adjustments.

How does post-mortem analysis feed into risk management guardrails?

Post-mortem analysis feeds into risk management by extracting root causes from losses and writing them to a knowledge base. These entries trigger prompts to adjust strategies and update risk guardrails for future trades.