Dragan Spiridonov

Create new Claude Code Skills with proper YAML frontmatter, progressive disclosure structure, and complete directory organization. Use when you need to build custom skills for specific workflows, generate skill templates, or understand the Claude Skills specification.

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Stream-JSON chaining for multi-agent pipelines, data transformation, and sequential workflows

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Orchestrate multi-agent swarms with agentic-flow for parallel task execution, dynamic topology, and intelligent coordination. Use when scaling beyond single agents, implementing complex workflows, or building distributed AI systems.

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Evaluates code quality through complexity analysis, lint results, code smell detection, and test health metrics. Use when assessing deployment readiness, configuring quality gates, scoring a codebase for release, or generating quality reports with pass/fail verdicts.

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Optimizes QE agent performance through transfer learning, hyperparameter tuning, and pattern distillation across test domains. Use when improving agent accuracy, applying learned patterns to new projects, tuning quality thresholds, or implementing continuous improvement loops for AI-powered testing.

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Runs autonomous red-green-refactor loops to fix failing tests, reach coverage targets, and satisfy quality gates. Use when tests need to pass, coverage thresholds must be met, quality gates require compliance, or flaky tests need stabilization.

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Predicts defect-prone code using change frequency, complexity metrics, and historical bug patterns. Use when predicting defects before they escape, analyzing root causes of test failures, learning from past defect patterns, or implementing proactive quality management.

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Analyzes test coverage data (Istanbul, c8, lcov) to identify uncovered lines, branches, and functions with risk-weighted gap detection. Use when analyzing coverage reports, identifying coverage gaps, comparing coverage between branches, or prioritizing which untested code to cover first.

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Adversarial review court — a delivery (diff, PR, test suite, or artifact) is prosecuted by independent AI reviewers from different vendors, each with its own probe set, then a SHIP verdict must SURVIVE escalating deeper reviewers before it stands. Use when you want more than one reviewer's opinion on whether something is safe to ship: pre-merge gating, release go/no-go, catching a too-easy PASS, or any 'is this actually done?' decision where a shallow approval is a risk. Produces a signed court record with a three-valued verdict (SHIP / REMAND / BLOCK) and a human as final judge. Learns over time: reproduced charges and overturned SHIPs feed the QE flywheel.

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Builds semantic code indexes, maps dependency graphs, and performs intelligent code search across large codebases. Use when understanding unfamiliar code, tracing call chains, analyzing import dependencies, or reducing context window usage through targeted retrieval.

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Injects controlled faults (network partition, latency, process kill, disk pressure) into distributed systems and validates recovery behavior. Use when testing circuit breakers, failover paths, retry logic, or building confidence in system resilience through chaos engineering.

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WCAG 2.2 compliance testing, screen reader validation, and inclusive design verification. Use when ensuring legal compliance (ADA, Section 508), testing for disabilities, or building accessible applications for 1 billion disabled users globally.

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Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more. Use when building self-learning agents, implementing RL, or optimizing agent behavior through experience.

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Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.

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Optimize AgentDB performance with quantization (4-32x memory reduction), HNSW indexing (150x faster search), caching, and batch operations. Use when optimizing memory usage, improving search speed, or scaling to millions of vectors.

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Implement semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and context-aware querying. Use when building RAG systems, semantic search engines, or intelligent knowledge bases.

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Train and deploy neural networks in distributed E2B sandboxes with Flow Nexus

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Comprehensive Flow Nexus platform management - authentication, sandboxes, app deployment, payments, and challenges

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Cloud-based AI swarm deployment and event-driven workflow automation with Flow Nexus platform

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Comprehensive GitHub code review with AI-powered swarm coordination

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Multi-repository coordination, synchronization, and architecture management with AI swarm orchestration

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Comprehensive GitHub release orchestration with AI swarm coordination for automated versioning, testing, deployment, and rollback management

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Advanced GitHub Actions workflow automation with AI swarm coordination, intelligent CI/CD pipelines, and comprehensive repository management

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Use when running comprehensive WCAG accessibility audits with axe-core + pa11y + Lighthouse, generating context-aware remediation, or testing video accessibility. Supports 3-tier browser cascade with graceful degradation.

日本語の概要は準備中です。原文の説明を表示しています。