Plugins

8 plugins

Memory, search and workflow skills from thedotmack/claude-mem.

19 skills · plugin

Builtin skills from Memento-Teams/Memento-Skills.

8 skills · plugin

Persistent memory system for Claude Code - seamlessly preserve context across sessions

5 skills · plugin

C/C++ Debugging

For C/C++ developers needing debugging tools, memory analysis, and GDB integration.

8 skills · plugin

Build RAG Pipeline with Pinecone

Build a production RAG pipeline and persistent agent memory using Pinecone as the vector database backend.

6 skills · plugin

Token-compression suite: compressed chat mode plus commit, review, help, stats, memory-compress and subagent-crew skills by Julius Brussee.

7 skills · plugin

Conversational derivation engine — generate agent-native memory architecture from natural conversation. 15 kernel primitives, 26 commands, 17 feature blocks, 3 presets.

10 skills · plugin

@pwdev-solucoes

Spec-driven development framework v2.3 — 8 real subagents (incl. advisor), per-task model routing, curated memory graph, opt-in parallel waves, external CLI delegation (Codex/OpenCode/Kimi/Gemini/Kiro), simplification pass, strict verify, audit hooks, 22 commands

2 skills · plugin

Results for “mem”

213 skills

intelli-verse-x

Mem0 CLI -- the command-line interface for mem0 memory operations. TRIGGER when: user mentions "mem0 cli", "mem0 command line", "@mem0/cli", "mem0-cli", "pip install mem0-cli", "npm install -g @mem0/cli", or is running mem0 commands in a terminal/shell (mem0 add, mem0 search, mem0 list, mem0 get, mem0 init, mem0 config, mem0 import). Also triggers when query includes CLI flags like --user-id, --output, --json, --agent, or describes bash/zsh/terminal/shell usage. DO NOT TRIGGER when: user asks about programmatic SDK integration in Python/TS code (use mem0 skill), or Vercel AI SDK provider (use mem0-vercel-ai-sdk skill).

Mem0 Automation

Automate Mem0 memory operations through Composio's Mem0 toolkit via Rube MCP, with dynamic tool discovery and connection management.

Orchestrate persistent agent memory across coding sessions, repos, and tools. Load when the user asks to remember, recall context, save project memory, create a handoff, manage global memory, update memory, compact memory, audit memory, forget memory, continue from prior sessions, or before commit/push/git operations that checkpoint project state.

Designs persistent memory architectures for AI agents, covering cross-session knowledge retention, entity tracking, temporal validity, graph/vector retrieval, and memory consolidation.

16.9k · bundle

Memory Reviewer

Review governed memory proposals, policy tags, tombstones, and effective memory.

Memberstack Automation

Automate Memberstack operations through Composio's Memberstack toolkit via Rube MCP, with tool discovery and connection management.

More results

Adds a persistent memory layer that stores and retrieves user preferences and context across conversations using semantic search.

Automate Mem operations through Composio's Mem toolkit via Rube MCP, with tool discovery and connection management.

Membervault Automation

Automate Membervault operations through Composio's Membervault toolkit via Rube MCP, with tool discovery and connection management.

Persistent cross-session memory for AI agents. Mem0 stores user preferences, past decisions, domain knowledge, and agent learnings across all sessions, all tools, and all users. Complements planning-with-files (task-level memory) with long-term agent intelligence (CRM + personal knowledge base layer). Use when asked to "remember this", "store preference", "mem0", "long-term memory", "user memory", "agent memory", or when building multi-session agents that need to recall past interactions.

Memberspot Automation

Automate Memberspot operations through Composio's toolkit via Rube MCP, with tool discovery and connection management.

Add persistent, intelligent memory to AI agents with Mem0 — add/search/update/delete memories per user/agent/session, supports vector + graph + key-value storage, integrates with LangChain, CrewAI, OpenAI Assistants, and any LLM.

johnalbertini14-glitch

Persistent memory CLI for LLM agents. Store facts, recall past knowledge, link related memories, manage lifecycle.

Jetson Inference Mem Tune

Recommends an inference runtime and memory-related launch flags for LLM/VLM workloads on NVIDIA Jetson devices, based on a live memory audit snapshot.

Estimates memory requirements for running Hugging Face models, including optional KV cache, using HTTP range requests without downloading weights.

Provides foundational infrastructure for the agent ecosystem, including entity memory management, model routing, and integration with the 1ai-skills hook system.

Nemo Rl Session Memory

Maintain durable session memory across agent disconnects by writing structured checkpoints to the repo's session directory, enabling context recovery.

Acontext Installer

Install and configure Acontext, a memory layer for AI agents that provides persistent sessions, file storage, and skill management.

Provides a persistent memory CLI for LLM agents, installed via npx.

You are an expert in Mem0, the memory infrastructure for AI applications. You help developers add persistent, personalized memory to LLM-powered apps and agents — storing user preferences, conversation history, facts, and context that persists across sessions, enabling AI that remembers users, learns from interactions, and provides increasingly personalized responses.

Agent Memory MCP

A hybrid memory system that provides persistent, searchable knowledge management for AI agents (Architecture, Patterns, Decisions).

Provides persistent, self-hosted semantic memory for AI agents via MCP, storing worklogs, decisions, and notes in PostgreSQL with pgvector for meaning-based retrieval across sessions.

Sets up persistent memory for AI agents on an OpenClaw gateway by installing a plugin that records observations and injects context into system prompts.

Estimates the memory required to load Safetensors or GGUF model weights for inference from the Hugging Face Hub, using HTTP Range requests without downloading weights.

Nemo Mbridge Perf Memory Tuning

Reduces peak GPU memory in Megatron Bridge training by applying expandable segments, parallelism resizing, activation recompute, and CPU offloading constraints.

Launch, monitor, stop, and debug NeMo-RL recipes on a Kubernetes cluster using the nrl-k8s CLI, supporting ephemeral and long-lived RayCluster modes.

Self-hosted semantic memory for AI agents via MCP. Save worklogs, decisions, and notes, then recall them across sessions by meaning, not keyword. Postgres + pgvector with auto-tagging.

Hugging Face CLI to estimate the required memory to load Safetensors or GGUF model weights for inference from the Hugging Face Hub

Arm Cortex Expert

Senior embedded software engineer specializing in firmware and driver development for ARM Cortex-M microcontrollers (Teensy, STM32, nRF52, SAMD). Decades of experience writing reliable, optimized, and maintainable embedded code with deep expertise in memory barriers, DMA/cache coherency, interrupt-driven I/O, and peripheral drivers.

Hugging Face CLI to estimate the required memory to load Safetensors or GGUF model weights for inference from the Hugging Face Hub

Hugging Face CLI to estimate the required memory to load Safetensors or GGUF model weights for inference from the Hugging Face Hub

Hugging Face CLI to estimate the required memory to load Safetensors or GGUF model weights for inference from the Hugging Face Hub

Configure and use Honcho memory with Hermes -- cross-session user modeling, multi-profile peer isolation, observation config, dialectic reasoning, session summaries, and context budget enforcement. Use when setting up Honcho, troubleshooting memory, managing profiles with Honcho peers, or tuning observation, recall, and dialectic settings.

Estimates GPU memory required to load Safetensors or GGUF model weights for inference from the Hugging Face Hub using HTTP Range requests, without downloading weights locally.

Continual Learning

Implements a continual learning loop for AI coding agents using hooks, two-tier memory (global and local), and automatic pattern detection to persist and apply learnings across sessions.

Coordinates multiple agents with spatial memory and swarm navigation for collective task execution.