152 skills · sorted by relevance
Set up, configure, update, and troubleshoot persistent memory for OpenClaw agents. The plugin stores conversations locally and supports automatic recall, task summaries, reusable skills derived from successful tasks, and a visual memory manager.
Use MemOS Local to find past conversations, preferences, and decisions, then retrieve original messages or task context. The skill also guides sharing memories and reusable procedures with local agents or a configured team.
Move legacy NanoClaw memory and Claude auto-memory into a shared structure used across providers. The skill separates lasting facts from standing instructions, updates indexes, records each file's outcome, and verifies recall after approval.
Set up and troubleshoot a local agentmemory server by checking MCP connections, authentication, ports, and environment settings. Understand which optional memory features require additional configuration, model downloads, or token usage before enabling them.
Guides HTTP integration with the agentmemory server for saving and searching knowledge. It explains authentication, request conventions, and endpoint references, including how to connect systems that do not support MCP, the agent tool connection protocol.
Connect an AI agent client to Memory Proxy, which adds team memory, skills, and knowledge to model requests. The guided workflow checks existing settings, tests connectivity, and writes the appropriate client configuration.
Review an agent’s saved memory alongside project and personal instructions. Receive proposed destinations for useful entries and a cleanup report covering duplicates, outdated facts, conflicts, and unclear items, with approval required before changes.
Gives AI agents persistent project memory for architecture, implementation patterns, and decisions. Supports saving, searching, and retrieving knowledge, automatically syncing it with project documentation, and viewing memory usage through a dashboard.
Orchestrate the remote rules registry — the shared registry (often connected as a local folder or git repo) that the project's rules are published to and pulled from. Three modes, picked from the user's wording: create scaffolds a new local rules registry ("create a local rules registry", "init a rules registry"); update migrates/promotes local knowledge-base rules from .unikit/memory/ into the remote registry with automatic semver bumps ("migrate local rules to the remote registry", "promote my rules to the registry", "push local rules", "publish local rules", "update the rules registry"); sync pulls registry updates back into memory ("sync the rules registry", "pull registry updates"). Pass "--module <id>" to scope to one knowledge-base module. This skill ONLY orchestrates the CLI (`unikit-ai rules *`) — it never edits .unikit.json or .unikit/memory/ sources directly.
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Find and run local AI models with llama.cpp. Choose GGUF model files and quantization levels that fit available memory, launch a command line session or local server, and test an OpenAI-compatible API on your computer.
Help Claude interpret workplace shorthand by storing people, acronyms, project names, and preferences in local files. Keep frequent references in CLAUDE.md and detailed context in memory/, so requests can be understood using recorded workplace knowledge.
Help Claude interpret workplace shorthand by storing people, acronyms, project names, and preferences in local files. Keep frequent references in CLAUDE.md and detailed context in memory/, so requests can be understood using recorded workplace knowledge.
Review candidate learnings in Claude Code's native auto memory (`~/.claude/projects/<project>/memory/`, machine-local) and run them through a five-critic council in parallel: generality, staleness, redundancy, evidence, format. Majority vote (3+ of 5) promotes the entry to MEMORY.md. Use when user says "promote memory", "review my learnings", "what should graduate to MEMORY.md", "five-critic council", or as monthly memory maintenance.
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Find language models on Hugging Face Hub and run them locally with llama.cpp. The skill helps compare GGUF model files, choose compressed versions that fit available memory, and prepare commands for a local chat server or command-line session.
Inspect CPU, memory, disk, and accelerator information alongside container and scheduler limits. Produce a JSON snapshot with identifying details removed and conservative worker and memory estimates for planning local workloads, without running stress tests.
Ask questions about your journal, Obsidian vault, or other text notes, and continue conversations that remember earlier chats. The companion uses cognee memory with local databases, reads your notes without changing them, and saves conversations for later reference.
Run smoke tests, or basic operational checks, for Mastra projects locally or in staging and production. Verify Studio screens, agents, tools, workflows, traces, and memory, then report results, blockers, and skipped checks.
Use Memori Cloud from Claude Code to consult stored preferences, decisions, and project history before substantive work, then save the exchange afterward. Recall can span sessions and use filters for dates and information types.
Teaches when to recall from long-term memory before acting and when to save durable decisions, corrections and failures afterwards. Use when a memory tool or MCP memory server is connected but the agent is not using it consistently, when the user complains that the assistant forgets preferences, conventions or past decisions between sessions, or when setting up persistent memory for a project. Works with any memory backend: a folder of Markdown files, a local MCP server, or a managed service.
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Use when a project contains docs/memory/, or when the user asks to remember, retrieve, update, compact, forget, or audit project memory. MemoryCustodian manages local plain-text project memory with minimal context loading.
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Save project context and task handoffs as local Markdown documents that Claude, Codex, and other agents can share. The skill guides searching prior knowledge, recording evidence and remaining work, and checking the shared memory vault for structural problems.
Save, organize, synchronize, and retrieve documents, conversations, and project information across local files, memory tools, knowledge repositories, and databases. Check for duplicates before storing information and choose a suitable home for each type of knowledge.
Guides agents in choosing Cherry Studio tools for research, document conversion, memory, notifications, and other tasks. It explains when to use app tools or local runtimes, with checks for availability, configuration, and approval.
Provides persistent project memory for AI agents through a local MCP server, a tool interface for agents. Stores and searches architecture decisions and implementation patterns, syncs with project documentation, and includes a dashboard for viewing memory usage.