Results for “memmap”

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

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.

Integrates Mem0 Platform SDK for persistent memory in AI applications. Use when building agents or chatbots that need to remember user preferences, past interactions, or personalized context across sessions. Covers Python and TypeScript SDKs, plus LangChain, CrewAI, OpenAI Agents, LlamaIndex, AutoGen, and LangGraph integrations.

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

Machine learning on genomic interval data (BED files) with the geniml Python package — region embeddings (Region2Vec), joint region+metadata embeddings (BEDspace/StarSpace), single-cell ATAC-seq embeddings (scEmbed), consensus peak sets / universes (build-universe), tokenization, BEDshift randomization, and BBClient/BEDbase caching. Use when training or using region/cell embeddings, clustering scATAC-seq, building a tokenization universe from BED collections, or any ML/feature-learning task over genomic regions. NOT for plain interval arithmetic (overlap/intersect/merge counts) — that is gtars, not geniml. Part of the AlterLab Academic Skills suite.

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.

Indexes document metadata in SQLite and manages ephemeral key-value state in Redis for cross-session context.

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.

Generate a persistent .nexus-map/ knowledge base that lets any AI session instantly understand a codebase's architecture, systems, dependencies, and change hotspots. Use when starting work on an unfamiliar repository, onboarding with AI-assisted context, preparing for a major refactoring initiative, or enabling reliable cold-start AI sessions across a team. Produces INDEX.md, systems.md, concept_model.json, git_forensics.md and more. Requires shell execution and Python 3.10+. For ad-hoc file queries or instant impact analysis during active development, use nexus-query instead.

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.

Persists context across sessions by compiling sources into a local SQLite store and markdown tree, then retrieves relevant memory via embedding search.

Search meme templates, suggest formats, and generate local or hosted image memes.

This skill should be used when working with genomic interval data (BED files) for machine learning tasks. Use for training region embeddings (Region2Vec, BEDspace), single-cell ATAC-seq analysis (scEmbed), building consensus peaks (universes), or any ML-based analysis of genomic regions. Applies to BED file collections, scATAC-seq data, chromatin accessibility datasets, and region-based genomic feature learning.

This skill should be used when working with genomic interval data (BED files) for machine learning tasks. Use for training region embeddings (Region2Vec, BEDspace), single-cell ATAC-seq analysis (scEmbed), building consensus peaks (universes), or any ML-based analysis of genomic regions. Applies to BED file collections, scATAC-seq data, chromatin accessibility datasets, and region-based genomic feature learning.

Perform nonlinear dimensionality reduction, 2D/3D embeddings, clustering preprocessing, supervised or semi-supervised UMAP, DensMAP, AlignedUMAP, and Parametric UMAP workflows using the umap-learn library.

UMAP dimensionality reduction. Fast nonlinear manifold learning for 2D/3D visualization, clustering preprocessing (HDBSCAN), supervised/parametric UMAP, for high-dimensional data.

Reduce high-dimensional data with UMAP for visualization, clustering preprocessing, and supervised or semi-supervised learning, including parameter tuning guidance.

Compress bloated project or global memory while preserving decisions, rationale, revisit triggers, provenance, and active user preferences. Load when memory exceeds budget, global memory is too large, session logs are repetitive, or before appending to an over-budget memory file.

Trains machine learning models on genomic interval data from BED files, including region embeddings, single-cell ATAC-seq analysis, and consensus peak building.

Simulates man-in-the-middle attacks using Ettercap, mitmproxy, and Bettercap in authorized environments to intercept, analyze, and modify network traffic for testing encryption enforcement, certificate validation, and detection capabilities.

Searches a local-first memory index and returns the top matching entries with paths and headers.

Enhances AI agent memory by injecting large external context, auto-summarizing conversations with tone and intent preservation, compressing multi-turn histories, and verifying facts.

FREE — God-tier long-context memory for AI agents. Injects 500K-1M clean tokens, auto-summarizes with tone/intent preservation, compresses 14-turn history into 800 tokens.

This skill should be used when the user asks to "swap tokens", "trade ETH for USDC", "exchange tokens on Uniswap", "buy tokens", "sell tokens", "convert ETH to stablecoins", "find memecoins", "discover tokens", "research tokens", "tokens to buy", "find tokens to swap", "what should I buy", or mentions swapping, trading, researching, discovering, buying, or exchanging tokens on any Uniswap-supported chain. Supports both known token swaps and token discovery workflows (discovery uses keyword search and web search — there is no live "trending" feed). Generates deep links to execute swaps in the Uniswap interface.

Provides a simple interface to the Memos API for creating and managing memos.

Use when running HMMER master or worker daemon services that front `phmmer`, `hmmsearch`, and `hmmscan` against cached databases.

Builds a local RAG memory system that indexes session logs and notes into ChromaDB for semantic recall across agent restarts.

Runs NCBI BLAST+ 2.17.0 sequence searches from the command line: makeblastdb (with -parse_seqids), blastn/blastp/blastx/tblastn with tabular -outfmt 6/7 for parsing, correct -task choice (megablast vs blastn vs blastn-short), -taxids/-negative_taxids taxonomic scoping, and -mt_mode multithreading; plus a DIAMOND blastp --ultra-sensitive path for large protein searches. Warns that -max_target_seqs is a heuristic keep-count, not a top-N best-hits filter. Use when the user wants command-line BLAST, makeblastdb, a local BLAST database, blastn/blastp/blastx/tblastn searches, or DIAMOND protein search. For the Bio.Blast web NCBIWWW API prefer alterlab-biopython; for quick one-liner database lookups prefer alterlab-gget. Part of the AlterLab Academic Skills suite.

clawhip × filesystem-offloaded memory

FREE — God-tier long-context memory for AI agents. Injects 500K-1M clean tokens, auto-summarizes with tone/intent preservation, compresses 14-turn history into 800 tokens.

FREE — God-tier long-context memory for AI agents. Injects 500K-1M clean tokens, auto-summarizes with tone/intent preservation, compresses 14-turn history into 800 tokens.

Detects available memory tools and maps them to a structured memory system for cross-session recall, with file-based fallback.

Master orchestrator for the keyword research pipeline. Chains topic-discovery → seed/modifier ideation → competitor + AI-search gap analysis → question mining → BID method → AIO cannibalization check → adversarial redteam → final ranked queue. Same anti-context-bloat pattern as /blog-pipeline (every layer is an Agent dispatch, never a Skill fork).

Unified search API across Google, Amazon, Yelp, OpenTable, Walmart, and more. Use when searching for products, local businesses, restaurants, shopping, images, news, or any web search. One API key, many engines.

Mem0 Platform SDK for adding persistent memory to AI applications. TRIGGER when: user mentions "mem0", "MemoryClient", "memory layer", "remember user preferences", "persistent context", "personalization", or needs to add long-term memory to chatbots, agents, or AI apps. Covers Python SDK (mem0ai), TypeScript SDK (mem0ai), and framework integrations (LangChain, CrewAI, OpenAI Agents SDK, Pipecat, LlamaIndex, AutoGen, LangGraph). Also covers the open-source self-hosted Memory class. This is the DEFAULT mem0 skill for ambiguous queries. DO NOT TRIGGER when: user asks about CLI commands, terminal usage, or shell scripts (use mem0-cli), or Vercel AI SDK / @mem0/vercel-ai-provider / createMem0 (use mem0-vercel-ai-sdk).