Discover Data & AI skills, compare source repositories and read the instructions for each workflow.
Advanced binary analysis with runtime execution and symbolic path exploration (RE Levels 3-4). Use when need runtime behavior, memory dumps, secret extraction, or input synthesis to reach specific program state...
Multi-model consensus using Karpathy LLM Council pattern for critical decisions
Apply quantization to reduce memory by 4-32x. Enable HNSW indexing for 150x faster search. Configure caching strategies and implement batch operations. Use when optimizing memory usage, improving search speed,...
Systematic technology and market reconnaissance for extracting actionable intelligence from repositories, papers, and competitive landscapes.
Use Codex CLI sandbox mode to try risky changes safely. Isolated experimentation with network disabled and directory restrictions.
Structured data extraction from web pages using claude-in-chrome MCP with sequential-thinking planning. Focus on READ operations, data transformation, and pagination handling for multi-page extraction.
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Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems,...
Ralph Wiggum persistence loop with intelligent multi-model routing (Gemini, Codex, Claude, Council)
Build feature command
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Use Codex CLI for sandboxed auditing, debugging, and autonomous prototyping
Comprehensive cognitive mode management skill for the VERILINGUA x VERIX x DSPy x GlobalMOO integration. Enables automatic mode selection, frame configuration, VERIX epistemic notation, and GlobalMOO optimizati...
Use Gemini CLI's 1M token context to understand entire codebases in one pass. Full architecture mapping, pattern discovery, and onboarding documentation.
Extract learnings from session corrections and patterns, update skill files with persistent memory. Implements Loop 1.5 - per-session micro-learning between execution and meta-optimization.
AgentDB Reinforcement Learning Training operates on 3 fundamental principles:
AgentDB Vector Search Optimization operates on 3 fundamental principles:
Comprehensive truth scoring, code quality verification, and automatic rollback system with 0.95 accuracy threshold for ensuring high-quality agent outputs and codebase reliability.
Complex browser automation workflow using claude-in-chrome MCP with mandatory sequential-thinking planning. Use when automating multi-step web interactions, form filling, navigation sequences, or web scraping.
Create AI learning plugins using AgentDBs 9 reinforcement learning algorithms. Train Decision Transformer, Q-Learning, SARSA, and Actor-Critic models. Deploy these plugins to build self-learning agents, impleme...
Modular image generation - supports local SDXL Lightning, OpenAI DALL-E, Replicate, or custom providers
Fast binary analysis with string reconnaissance and static disassembly\ \ (RE Levels 1-2). Use when triaging suspicious binaries, extracting IOCs quickly,\ \ or performing initial malware analysis. Completes in...
AgentDB Persistent Memory Patterns operates on 3 fundamental principles:
Implement ReasoningBank adaptive learning with AgentDBs 150x faster vector database. Includes trajectory tracking, verdict judgment, memory distillation, and pattern recognition. Use when building self-learning...
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