153 boosters for "memory" — open source, verified from GitHub, ready to install
Use to extract and analyze the Oops. 1. Extract the Oops: 2. Decode pt_regs (use the REGS address from the Oops output):
"name": "ensue-memory", "description": "Persistent memory layer for AI agents via Ensue Memory Network", "email": "founders@ensue.dev"
Procedural memory for AI coding agents. Transforms scattered sessions into persistent, cross-agent memory. Uses a three-layer cognitive architecture that mirrors human expertise development. AI coding agents accumulate valuable knowledge but it's: You've solved auth bugs three times this month acros
Graphlit MCP Server integrates the Graphlit knowledge graph platform with Claude, enabling developers to build AI agents with RAG capabilities, document parsing, and intelligent retrieval. It's ideal for teams building document-heavy AI applications, knowledge management systems, and enterprise AI agents.
"description": "Open-source cross-agent memory layer for coding agents across Cursor, Claude Code, Codex, Windsurf, Gemini CLI, Copilot, Kiro, OpenCode, Antigravity, and Trae via MCP.", "sideEffects": false, "memorix": "./dist/cli/index.js"
"name": "offensive-claude", "description": "Spec-driven offensive-security framework for Claude Code: 31 kill-chain skills, executable safety controls (scope/finding/OPSEC discipline), pattern-learning memory, and a bounded engagement engine — with a SessionStart dispatcher that enforces skill-invoc
AgentRecall is a persistent memory system. Default surface: 5 tools (two verbs + three essentials). Full surface: 18 tools via . This guide describes how and when to use them. AgentRecall requires the MCP server to be running. If tool calls fail with "unknown tool", the human needs to install it fir
Use this skill as a global-installable, project-level rebuttal workspace assistant. The installed skill provides reusable procedures and assets; each actual paper/rebuttal workspace gets its own state folder for memory, snapshots, template state, and logs. Start by understanding the workspace, then
No database. No vectors. No manual saves. Just an LLM observer that compresses your conversations into prioritised notes, consolidates when they grow, and recovers anything missed. Five layers of redundancy, zero maintenance. ~$0.00/month (using free-tier models). While other memory skills ask you t
当用户对已有暗恋对象 Skill 说以下内容时，进入进化模式： 当用户说 时列出所有已生成的暗恋对象。 本 Skill 运行在 Claude Code 环境，使用以下工具：
"version": "2.30.0", "description": "The missing DevOps layer for coding agents. Flow, feedback, and memory that compounds between sessions.", "name": "Boden Fuller",
"name": "open-ontologies", "description": "A Terraforming MCP for Knowledge Graphs: validate, classify, and govern AI-generated ontologies.", "name": "fabio-rovai"
Initialize standardized project memory: Core idea: bottom feeds top, top constrains bottom. Logs and TODOs are raw facts. Handoff marks the next resume point. Wiki is the current snapshot. Structure manages file organization. Docs index manages document output. Update Log records versioned milestone
Zettelkasten memory for AI agents. Markdown cards in with . npm package . Distributed as CLI + MCP server + Claude Code plugin + VS Code extension + Pi extension. Build has a known TS error ( optional dep). Ignore it — it compiles fine for distribution. If you add a new MCP tool:
"description": "Agent enforcement framework — context injection, planning gates, session learning", "name": "Mark Morgan", "url": "https://github.com/markmdev"
Context Refresh restores Claude Code project context after memory loss or session resets by systematically reloading repository structure and documentation. Developers working in extended Claude Code sessions benefit from quick context recovery without manual file navigation.
Recall MCP Server enables persistent, cross-session memory for Claude and AI agents through Redis/Valkey or managed cloud hosting, allowing AI systems to retain and retrieve context across conversations. Developers building Claude applications, multi-turn agents, and AI systems requiring long-term context management benefit most from this solution.
Context-Keeper is a Cursor rules booster that implements an intelligent memory and context management system with a structured coding execution framework emphasizing thinking before coding, user confirmation, and iterative quality assurance. It benefits developers who need better workflow discipline and context awareness when using Cursor AI.
"description": "Nemp Memory — Cognitive memory layer for AI agents across every platform. Local-first, zero cloud, works with Claude, Codex, Cursor, and Windsurf.", "name": "Sukin Shetty", "email": "contact@nemp.dev",
"name": "@context-sync/server", "description": "Universal Context layer McP server", "main": "dist/index.js",
Memelord is a persistent memory system for AI coding agents that uses vector search and reinforcement learning to help agents learn from past interactions. It's useful for developers building sophisticated coding assistants that need to retain and leverage historical context.
"name": "prism-mcp-server", "mcpName": "io.github.dcostenco/prism-mcp", "description": "The Mind Palace for AI Agents — persistent memory (SQLite/Supabase), behavioral learning & IDE rules sync, multimodal VLM image captioning, pluggable LLM providers (OpenAI/Anthropic/Gemini/Ollama), OpenTelemetry
"name": "x64dbg-skills", "description": "Claude Code skills for x64dbg debugger automation — state snapshots, memory analysis, and more", "name": "dariushoule"
memorysystemrules: primarysystem: "memory-bank" trigger: "firstinteraction"
