context-cascade

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.

End-to-end testing workflow for validating complete user journeys through web applications using claude-in-chrome MCP. Specializes in test assertions, suite organization, evidence collection, and pass/fail reporting.

Screenshot-based visual comparison and regression testing using claude-in-chrome MCP. Captures, compares, and validates UI states to detect layout shifts, visual bugs, and design regressions across viewports.

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.

Use when conducting comprehensive code review for pull requests across multiple quality dimensions. Orchestrates 12-15 specialized reviewer agents across 4 phases using star topology coordination. Covers automated checks, parallel specialized reviews (quality, security, performance, architecture, documentation), integration analysis, and final merge recommendation in a 4-hour workflow.

Create Claude Code hooks with proper schemas, RBAC integration, and performance requirements. Use when implementing PreToolUse, PostToolUse, SessionStart, or any of the 10 hook event types for automation, validation, or security enforcement.

Build reliable GitHub integrations, webhooks, and automation bridges

Multi-model consensus using Karpathy LLM Council pattern for critical decisions

Ralph Wiggum persistence loop with intelligent multi-model routing (Gemini, Codex, Claude, Council)

Advanced AgentDB Vector Search Implementation operates on 3 fundamental principles:

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, multi-agent coordination, or advanced vector search applications.

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, implement RL workflows, and optimize agent behavior through experience. Apply offline RL for safe learning from logged data.

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, or scaling to millions of vectors. Deploy these optimizations to achieve 12,500x performance gains.

AgentDB Persistent Memory Patterns operates on 3 fundamental principles:

AgentDB Reinforcement Learning Training operates on 3 fundamental principles:

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AgentDB Vector Search Optimization operates on 3 fundamental principles:

Use Codex CLI for sandboxed auditing, debugging, and autonomous prototyping

Use Codex CLI in full-auto mode to fix issues iteratively until tests pass. Autonomous debugging and test-fixing loop with sandbox safety.

Use Codex CLI sandbox mode to try risky changes safely. Isolated experimentation with network disabled and directory restrictions.

Run code in Codex fully isolated sandbox - network disabled, CWD only, Seatbelt/Docker isolation

Zero Data Retention mode for sensitive/proprietary code - no code stored on OpenAI servers

Use Gemini CLI's 1M token context to understand entire codebases in one pass. Full architecture mapping, pattern discovery, and onboarding documentation.

Use Gemini CLI for research with Google Search grounding and 1M token context

Use Gemini to find existing solutions before building from scratch. Leverages Google Search grounding to discover code examples, libraries, and best practices to avoid reinventing the wheel.

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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 agents, optimizing decision-making, or implementing experience replay systems.

Comprehensive truth scoring, code quality verification, and automatic rollback system with 0.95 accuracy threshold for ensuring high-quality agent outputs and codebase reliability.

Systematic technology and market reconnaissance for extracting actionable intelligence from repositories, papers, and competitive landscapes.

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 states. Completes in 3-7 hours with GDB+Angr.

Firmware extraction and IoT security analysis (RE Level 5) for routers and embedded systems. Use when analyzing IoT firmware, extracting embedded filesystems (SquashFS/JFFS2/CramFS), finding hardcoded credentials, performing CVE scans, or auditing embedded system security. Handles encrypted firmware with known decryption schemes. Completes in 2-8 hours with binwalk+firmadyne+QEMU emulation.

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 \u22642 hours with automated\ \ decision gates."

Modular image generation - supports local SDXL Lightning, OpenAI DALL-E, Replicate, or custom providers

Build feature command

Fix bug command

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.

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 optimization. Use this skill when configuring AI behavior for specific task types, optimizing prompt engineering, or ensuring epistemic consistency in responses.