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Shamrock2245
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self-improving-agent

Extract experiences into reusable semantic patterns for Shamrock Trading Bot's memory system.

1|1|Updated Mar 20, 2026
Search Tags:#automation#codebase#self-improvement#agent#memory#self-correction#pattern-extraction
One-click install
npx skills add https://github.com/Shamrock2245/shamrock-trading-bot --skill self-improving-agent-shamrock2245
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: self-improving-agent
Source: https://github.com/Shamrock2245/shamrock-trading-bot/tree/main/.agent/skills/self-improving-agent
Command: npx skills add https://github.com/Shamrock2245/shamrock-trading-bot --skill self-improving-agent-shamrock2245

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

The self-improving-agent captures experiences and automated signals from the Shamrock Trading Bot codebase to continuously extract lessons, correct recurring errors, and evolve operational patterns so regressions and repeated mistakes are prevented over time.

Core Features & Use Cases

  • Multi-Memory Architecture: Maintains semantic memory (memory/semantic-patterns.json), episodic memory (memory/episodic/), and working memory (memory/working/) to separate abstract patterns, past episodes, and current session context.
  • Automated Experience Extraction & Pattern Abstraction: Hooks capture tool outputs, errors, and session summaries, convert recurring incidents into reusable patterns with confidence scores, and record evolution/correction markers for traceability.
  • Self-Correction & Guardrails: Detects when applied guidance causes failures, records last_error.json, applies correction markers, and updates semantic patterns to prevent repeat failures.
  • Integration Points: Session hooks (hooks/pre-tool.sh, hooks/post-bash.sh, hooks/session-end.sh) support automatic activation after deployments, bug fixes, scoring regressions, or on user commands like self-improve or learn from this.
  • Use Case: After a scoring regression or deployment, run the session-end hook to extract the episode, update semantic patterns, and produce evolution markers so the bot avoids repeating the same failure.

Quick Start

Ask the self-improving-agent to self-improve by summarizing recent experiences, extracting patterns, and updating semantic memory.

Frequently Asked Questions about self-improving-agent

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

FAQPage Schema
How do I automate self-improvement for my trading bot codebase after deployment?▼

To automate self-improvement for a trading bot codebase, use session hooks like post-tool and session-end scripts to capture episodic logs, extract reusable patterns, and update semantic memory files to prevent recurring errors.

How does an agent use semantic and episodic memory to prevent repeated code failures?▼

An agent uses semantic and episodic memory to prevent repeated code failures by separating abstract patterns from past incidents, recording errors in last_error.json, and applying correction markers to update semantic rules.

Can I extract reusable patterns from bugfix logs using a self-correcting agent?▼

Yes, you can extract reusable patterns from bugfix logs by running session-end hooks that consolidate episodic memories into semantic patterns with confidence scores, ensuring automated experience extraction and traceability.

Does the self-improving agent require specific file structures for working memory persistence?▼

The self-improving agent requires file-based persistence structures including memory/semantic-patterns.json for abstract rules, memory/episodic/ for past incidents, and memory/working/ for current session context.

What is the best way to track pattern confidence and correction markers in an automated agent?▼

The best way to track pattern confidence and correction markers in an automated agent is through a multi-memory architecture that records evolution markers, updates semantic-patterns.json, and applies self-correction markers when applied guidance causes failures.

When should I not use a self-improving agent for codebase memory management?▼

You should not use a self-improving agent for codebase memory management when your workflow lacks automated session hooks, post-deployment triggers, or episodic logs needed to consolidate experiences into semantic rules and corrections.

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