Search Results for "DNYoussef" | AgentCC

fix-bug

Fix bug command

agentdb-performance-optimization

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.

reconnaissance

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

multi-model-discovery

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

when-reviewing-pull-request-orchestrate-comprehensive-code-review

Use when conducting a comprehensive code review of a pull request across multiple quality dimensions. Orchestrates 12–15 specialized reviewer agents across four phases using a star-topology coordination pattern. Covers automated gates, parallel expert reviews (quality, security, performance, architecture, documentation), integration analysis, and a final merge recommendation within a four-hour timebox.

build-feature

Build feature command

gemini-codebase-onboard

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

reverse-engineering-firmware-analysis

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), discovering hardcoded credentials, performing CVE scans, or auditing embedded-system security. Supports encrypted firmware with known decryption schemes. Typical completion time: 2–8 hours using binwalk + Firmadyne + QEMU emulation.

ralph-multimodel

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

github-integration

Build reliable GitHub integrations, webhooks, and automation bridges

e2e-test

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.

testinge2eautomation+3

visual-testing

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.

testingvisualregression+3

codex-sandbox

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

codex-zdr

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

hook-creator

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.

reasoningbank-with-agentdb

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.

cognitive-mode

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.

reverse-engineering-quick-triage

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."

agentdb-advanced-features

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.

reverse-engineering-deep-analysis

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.

verification-and-quality-assurance

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

llm-council

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

web-scraping

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.

data-extractionwebscraping+3

agentdb-semantic-vector-search

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image-gen

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

codex-iterative-fix

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

gemini-research

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

reasoningbank-adaptive-learning-with-agentdb

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agentdb-learning-plugins

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.

codex-audit

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

reflect

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.