pieces-ltm

Persist skill chain outcomes to Long-Term Memory with structured metadata.

3|Updated Jun 12, 2026

Search Tags:#retrieval #memory #persistence #ltm #skill chain

Ready to run

npx skills add https://github.com/weebcoder101/dreamcode --skill pieces-ltm-weebcoder101

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: pieces-ltm
Source: https://github.com/weebcoder101/dreamcode/tree/main/.dreamcode/skills/pieces-ltm
Command: npx skills add https://github.com/weebcoder101/dreamcode --skill pieces-ltm-weebcoder101

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires @dreamcode/PiecesLTM, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill ensures that the outcomes of skill chains are automatically persisted to Long-Term Memory (LTM), enhancing memory retention and retrieval for future reference.

Core Features & Use Cases

Memory Persistence: Persists the results of skill chains to LTM, ensuring that the outcomes are not lost.
Memory Retrieval: Provides improved retrieval patterns for future context queries.
Use Case: After performing a complex analysis of a codebase, use this Skill to persist the results to LTM for future reference and retrieval.

Quick Start

After completing a skill chain, automatically persist the results to LTM by calling the 'persist_chain_result' function.

Frequently Asked Questions about pieces-ltm

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

FAQPage Schema

How do I persist skill chain outcomes to long-term memory?▼

You can persist skill chain outcomes to long-term memory by using a Skill that automates storage via the 'persist_chain_result' function. This ensures non-trivial results are stored with structured metadata and auto-classification for future retrieval.

How does automated memory persistence improve context retrieval in skill chains?▼

Automated memory persistence improves context retrieval by storing non-trivial skill chain outcomes in Long-Term Memory with structured metadata. This ensures previous results are not lost and provides enhanced patterns for future context queries.

Do I need Pieces MCP tools to store skill chain results in LTM?▼

Yes, you need Pieces MCP tools for LTM storage and querying. This Skill requires the @dreamcode/PiecesLTM dependency to automate the persistence of skill chain outcomes and ensure effective memory retrieval.

Can I automatically classify memory types when saving skill chain results?▼

Yes, you can automatically classify memory types when saving skill chain results. This Skill features auto-classification of memory types alongside structured metadata when persisting outcomes to Long-Term Memory.

What's the best way to save complex codebase analysis results for future reference?▼

The best way to save complex codebase analysis results is to use this Skill to persist outcomes to Long-Term Memory. After completing your analysis, call the 'persist_chain_result' function to store the data for future retrieval.

Why are my skill chain results lost after execution completes?▼

Skill chain results are lost after execution if they are not explicitly persisted to Long-Term Memory. You can prevent this by using the 'persist_chain_result' function to automatically store non-trivial outcomes with structured metadata.