🧠Memory and Context Management Skills
Browse the best Memory and Context Management skills for Claude Code, Codex, Cursor and other AI agents.
1,211 skills · Page 1 of 51
github-url-intercept
★ 4BLOCKING INTERCEPT: When ANY github.com URL appears in user input, STOP and use this skill. Never fetch GitHub HTML pages directly - they are 5-10MB and will exhaust your context window. This skill routes URLs to efficient API calls (1-50KB). Triggers on: pull/, issues/, blob/, tree/, commit/, compare/, discussions/.
using-loaded-knowledge
MANDATORY protocol enforcing knowledge check before EVERY response - prevents explaining systems without reading docs, claiming without verification, and ignoring auto-loaded context
pinecone
★ 15.4kManaged vector database for production AI applications. Fully managed, auto-scaling, with hybrid search (dense + sparse), metadata filtering, and namespaces. Low latency (<100ms p95). Use for production RAG, recommendation systems, or semantic search at scale. Best for serverless, managed infrastructure.
sglang
★ 62Fast structured generation and serving for LLMs with RadixAttention prefix caching. Use for JSON/regex outputs, constrained decoding, agentic workflows with tool calls, or when you need 5× faster inference than vLLM with prefix sharing. Powers 300,000+ GPUs at xAI, AMD, NVIDIA, and LinkedIn.
session-memory
★ 19DO NOT invoke directly - loaded automatically by ALL agents via frontmatter in cc10x-router workflows.MANDATORY skill loaded at start and updated at end of EVERY workflow. Persists context, learnings, decisions, and progress to .claude/cc10x/ folder. Without this, context is lost on compaction.Agents are invoked through cc10x-router workflows (BUILD/REVIEW/DEBUG/PLAN) - do not invoke agents or this skill directly.
soul
★ 5Core identity and continuity system. Use to grow wisdom, record failures, observe identity, and access accumulated knowledge.
shodh-memory
★ 5Persistent memory system for AI agents. Use this skill to remember context across conversations, recall relevant information, and build long-term knowledge. Activate when you need to store decisions, learnings, errors, or context that should persist beyond the current session.
github-elements-tracking
★ 3This skill should be used when the user asks to "track work across sessions", "create an epic", "manage issue waves", "post a checkpoint", "claim an issue", "recover from compaction", "coordinate multiple agents", "update memory bank", "store large documents", or mentions GitHub Issues as persistent memory, multi-session work, context survival, agent collaboration, SERENA MCP memory, or project-level context. Provides complete protocols for using GitHub Issues as permanent memory that survives context exhaustion, with integrated SERENA MCP memory bank for project-level context and large document storage.
memory-integration
★ 1Use to maintain context across sessions - integrates episodic-memory for conversation recall and mcp__memory knowledge graph for persistent facts
memory-bank
★ 1Persistent project documentation system that maintains context across sessions. Creates structured Memory Bank files to preserve project knowledge, decisions, and progress.
project-indexing
★ 1Generates SHANNON_INDEX for 94% token reduction (58K → 3K tokens). Compresses large codebasesinto structured summaries with Quick Stats, Tech Stack, Core Modules, Dependencies, Recent Changes,and Key Patterns. Enables fast agent onboarding, efficient multi-agent coordination, and instantcontext switching. Use when: starting project analysis, onboarding new agents, coordinating waves,switching between codebases, or when context window efficiency is critical.
using-loaded-knowledge
MANDATORY protocol enforcing knowledge check before EVERY response - prevents explaining systems without reading docs, claiming without verification, and ignoring auto-loaded context
managing-claude-memory
Expert guidance for creating, updating, and maintaining CLAUDE.md memory files following best practices for hierarchical memory, structure, content organization, and team collaboration
openai-responses
This skill provides comprehensive knowledge for working with OpenAI's Responses API, the unified stateful API for building agentic applications. It should be used when building AI agents that preserve reasoning across turns, integrating MCP servers for external tools, using built-in tools (Code Interpreter, File Search, Web Search, Image Generation), managing stateful conversations, implementing background processing, or migrating from Chat Completions API.Use when building agentic workflows, conversational AI with memory, tools-based applications, RAG systems, data analysis agents, or any application requiring OpenAI's reasoning models with persistent state. Covers both Node.js SDK and Cloudflare Workers implementations.Keywords: responses api, openai responses, stateful openai, openai mcp, code interpreter openai, file search openai, web search openai, image generation openai, reasoning preservation, agentic workflows, conversation state, background mode, chat completions migration, gpt-5, polymorphic o
sequential-thinking
Performs dynamic, reflective problem-solving through iterative thought chains. Use for complex planning requiring revision, branching, backtracking, or hypothesis verification. Ideal for multi-step analysis where context maintenance is required or the full scope isn't initially clear.
rag-implementation
★ 24.2kBuild Retrieval-Augmented Generation (RAG) systems for LLM applications with vector databases and semantic search. Use when implementing knowledge-grounded AI, building document Q&A systems, or integrating LLMs with external knowledge bases.
agent-memory
★ 21.0kUse this skill when the user asks to save, remember, recall, or organize memories. Triggers on: 'remember this', 'save this', 'note this', 'what did we discuss about...', 'check your notes', 'clean up memories'. Also use proactively when discovering valuable findings worth preserving.
faiss
★ 15.4kFacebook's library for efficient similarity search and clustering of dense vectors. Supports billions of vectors, GPU acceleration, and various index types (Flat, IVF, HNSW). Use for fast k-NN search, large-scale vector retrieval, or when you need pure similarity search without metadata. Best for high-performance applications.
sglang
★ 15.4kFast structured generation and serving for LLMs with RadixAttention prefix caching. Use for JSON/regex outputs, constrained decoding, agentic workflows with tool calls, or when you need 5× faster inference than vLLM with prefix sharing. Powers 300,000+ GPUs at xAI, AMD, NVIDIA, and LinkedIn.
memory
★ 124Save and recall important information from previous sessions. Use when you want to remember technical insights, decisions, project context, or discoveries. Simply say 'remember:' or 'save to memory:' and I'll store it persistently and automatically reference it in future conversations.
pinecone
★ 62Managed vector database for production AI applications. Fully managed, auto-scaling, with hybrid search (dense + sparse), metadata filtering, and namespaces. Low latency (<100ms p95). Use for production RAG, recommendation systems, or semantic search at scale. Best for serverless, managed infrastructure.
faiss
★ 62Facebook's library for efficient similarity search and clustering of dense vectors. Supports billions of vectors, GPU acceleration, and various index types (Flat, IVF, HNSW). Use for fast k-NN search, large-scale vector retrieval, or when you need pure similarity search without metadata. Best for high-performance applications.
sentence-transformers
★ 62Framework for state-of-the-art sentence, text, and image embeddings. Provides 5000+ pre-trained models for semantic similarity, clustering, and retrieval. Supports multilingual, domain-specific, and multimodal models. Use for generating embeddings for RAG, semantic search, or similarity tasks. Best for production embedding generation.
recall
★ 42Retrieve context from past sessions before implementing. Use for learned patterns, past solutions, anti-patterns, session history, or when the user mentions "recall", "memory", "history", "cass", "cm", or "what do we know about".