self-learning-agentKnowledge card memory system with semantic search. Agents wake up fresh each session but remember everything through atomic ~350-token cards with YAML frontm...
Install via ClawdBot CLI:
clawdbot install solomonneas/self-learning-agentGrade Fair — based on market validation, documentation quality, package completeness, maintenance status, and authenticity signals.
Sends data to undocumented external endpoint (potential exfiltration)
POST → http://localhost:5204/api/searchCalls external URL not in known-safe list
http://localhost:5204/api/searchAI Analysis
The skill appears to be a legitimate memory management system with local file operations as its primary function. The external API call to localhost:5204/api/search is likely for semantic search functionality, but the endpoint is undocumented and could potentially send memory data externally if misconfigured. No credential harvesting, hidden instructions, or obfuscation were found in the provided definition.
Audited Apr 17, 2026 · audit v1.0
Generated May 8, 2026
A customer support AI remembers past interactions, resolved issues, and customer preferences across sessions using knowledge cards. It can recall troubleshooting steps and escalate correctly without starting from scratch.
An educational agent tracks a student's progress, mistakes, and preferred learning methods, storing them as atomic cards. It adapts future lessons and provides targeted practice, improving learning efficiency over time.
An AI assistant helps DevOps teams by remembering past incidents, resolutions, and infrastructure quirks. It uses semantic search to quickly surface relevant knowledge cards during troubleshooting, reducing downtime.
A sales agent maintains a memory of customer interactions, objections, and win/loss reasons across deals. It automatically logs lessons from calls and suggests tailored approaches for future engagements.
A lab assistant AI records experimental protocols, results, and equipment configurations as knowledge cards. It enables reproducibility and quick retrieval of past methods, accelerating research.
Offer the self-learning agent as a cloud service with different storage limits (e.g., number of cards, daily logs). Free tier includes basic memory; paid tiers offer higher capacity and advanced semantic search.
License the memory system to enterprises for internal use, including custom integration with their tools (CRM, ticketing systems). Includes setup, training, and support contracts.
Release a basic version open-source to drive adoption. Sell premium features like cloud sync, advanced analytics, priority support, and team collaboration features.
💬 Integration Tip
Start by creating the directory structure and MEMORY.md master index (~2KB). Then integrate memory_search and daily log creation into the agent's main loop, using semantic embedding for card retrieval.
Scored May 8, 2026
Meta-skill for AI agent self-improvement. Analyzes runtime logs to detect error patterns, regressions, and inefficiencies, then generates structured improvem...
Stop waiting for prompts. Keep working.
Turn OpenClaw into a learning-loop agent with seeded workspace rules, skill promotion, reflective memory, and proactive maintenance.
Local Python orchestration skill: multi-agent workflows via shared blackboard file, permission gating, token budget scripts, and persistent project context....
Meta-agent skill for orchestrating complex tasks through autonomous sub-agents. Decomposes macro tasks into subtasks, spawns specialized sub-agents with dynamically generated SKILL.md files, coordinates file-based communication, consolidates results, and dissolves agents upon completion. MANDATORY TRIGGERS: orchestrate, multi-agent, decompose task, spawn agents, sub-agents, parallel agents, agent coordination, task breakdown, meta-agent, agent factory, delegate tasks
Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Claude's capabilities with specialized knowledge, workflows, or tool integrations.