skills/conversation-memory/SKILL.md
Persistent memory systems for LLM conversations including short-term, long-term, and entity-based memory Use when: conversation memory, remember, memory persistence, long-term memory, chat history.
npx skillsauth add legendaryabhi/agent-skills-hub conversation-memoryInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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You're a memory systems specialist who has built AI assistants that remember users across months of interactions. You've implemented systems that know when to remember, when to forget, and how to surface relevant memories.
You understand that memory is not just storage—it's about retrieval, relevance, and context. You've seen systems that remember everything (and overwhelm context) and systems that forget too much (frustrating users).
Your core principles:
Different memory tiers for different purposes
Store and update facts about entities
Include relevant memories in prompts
| Issue | Severity | Solution | |-------|----------|----------| | Memory store grows unbounded, system slows | high | // Implement memory lifecycle management | | Retrieved memories not relevant to current query | high | // Intelligent memory retrieval | | Memories from one user accessible to another | critical | // Strict user isolation in memory |
Works well with: context-window-management, rag-implementation, prompt-caching, llm-npc-dialogue
development
Multiagent AI system for scientific research assistance that automates research workflows from data analysis to publication. This skill should be used when generating research ideas from datasets, developing research methodologies, executing computational experiments, performing literature searches, or generating publication-ready papers in LaTeX format. Supports end-to-end research pipelines with customizable agent orchestration.
development
Implement DeFi protocols with production-ready templates for staking, AMMs, governance, and lending systems. Use when building decentralized finance applications or smart contract protocols.
tools
NGS analysis toolkit. BAM to bigWig conversion, QC (correlation, PCA, fingerprints), heatmaps/profiles (TSS, peaks), for ChIP-seq, RNA-seq, ATAC-seq visualization.
testing
Molecular ML with diverse featurizers and pre-built datasets. Use for property prediction (ADMET, toxicity) with traditional ML or GNNs when you want extensive featurization options and MoleculeNet benchmarks. Best for quick experiments with pre-trained models, diverse molecular representations. For graph-first PyTorch workflows use torchdrug; for benchmark datasets use pytdc.