52 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.
Set up the OpenViking long-term memory plugin for OpenClaw through conversation. The skill handles installation, configuration, and verification so chat facts can be saved and relevant memories recalled across sessions, using an existing OpenViking server.
Install and configure the memory-tencentdb plugin for OpenClaw to retain and retrieve conversation information. Verify the setup through logs and searches, adjust memory settings, and troubleshoot missing recall or vector search results.
Investigate memory growth and out-of-memory failures in OpenClaw tests. Compare memory measurements and heap snapshots, records of objects held in memory, to distinguish cleanup leaks from retained test modules and verify targeted fixes.
Maintain an OpenClaw memory wiki using original sources and stored notes. Update existing pages, preserve human-written sections, and check for contradictions, missing source information, and unresolved questions after meaningful changes.
Move memories, persona files, skills, and compatible settings from OpenClaw into Hermes. Preview the migration, choose how to handle conflicts and supported secrets, and receive a report of migrated, archived, and skipped items.
Move an existing OpenClaw setup to NanoClaw v2 through a guided conversation. Review identity, memory, credentials, groups, and scheduled tasks, then decide what to transfer and preview changes before applying them.
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.
Measure and improve OpenClaw test and plugin-suite performance by investigating slow imports, repeated startup work, and memory use. Compare results before and after changes while preserving test coverage and verifying the affected tests.
Use OpenViking through OpenClaw to manage long-term memories, search archived conversations, and find imported resources or Agent Skills. Inspect recall traces to understand search results and recover full tool outputs stored outside the conversation.
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.
Investigate slow OpenClaw flows by counting operations and measuring performance. Use it to remove unnecessary work, evaluate whether caching is justified, and prepare or review performance changes with clear before-and-after evidence.
Turn OpenClaw conversations, actions, and tool history into structured long-term memory. Agents can retrieve past facts, decisions, and summaries when needed, helping them continue work across sessions and recover context after it is shortened.
Helps agents choose the right agentmemory tool through MCP, a protocol for connecting agents to tools. It explains tool roles and points to parameter references for saving, retrieving, organizing, and maintaining stored knowledge.
Understand how agentmemory automatically records tool activity, prompts, and session boundaries through hooks, actions triggered by session events. Use it to check integration setup, investigate missing records, and distinguish capture from optional summaries and context injection.
Manually sync OpenClaw chat transcripts to OpenViking and search stored memories using explicit commands. Receive a sync summary or relevant memory entries while leaving the OpenClaw contextEngine slot available for its existing use.
Design, configure, debug, and optimize OpenClaw AI agent deployments. Master guide for gateway configuration, openclaw.json settings, model routing and fallback chains, skills development and publishing, cron job scheduling, memory systems (Qdrant, Neo4j, SQLite), Docker infrastructure, and Tailscale VPN networking. Includes config analyzer that audits your openclaw.json and suggests improvements, plus health checker that validates all OpenClaw subsystems. Built for AI agents — Python stdlib only, no dependencies. Use for OpenClaw setup, gateway debugging, skill building, cron management, model optimization, cost reduction, and infrastructure troubleshooting.
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Maintain a memory wiki for use in Obsidian by organizing note links, stable filenames, and metadata at the top of files. Repair links after renames and check available tools before using Obsidian commands.
Investigate Node.js bugs by pausing execution, examining variables, and stepping through code. Diagnose stalled asynchronous work and unreliable tests, or collect memory snapshots and CPU profiles to investigate growing memory use and slow execution.
Migrate a user's OpenClaw customization footprint into Hermes Agent. Imports Hermes-compatible memories, SOUL.md, command allowlists, user skills, and selected workspace assets from ~/.openclaw, then reports exactly what could not be migrated and why.
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Ingest OpenClaw session/history data into Obsidian as distilled knowledge. Use for importing or mining ~/.openclaw sessions, memory files, or logs; use wiki-agent for targeted topic-only cross-agent recall.
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AI knowledge engine - a memory system in 6 lines of code. remember/recall/forget/improve loop, vector + graph search, OpenClaw plugin supported.
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Sync OpenClaw + ClawHub + Skills docs into a local mirror for the QMD memory backend.
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Indestructible agent memory — permanently stored, never lost. Save decisions, identity, and context as a memory chain on the Autonomys Network. Rebuild your full history from a single CID, even after total state loss.
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