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second-brain
second-brain에는 baekenough에서 수집한 skills 69개가 있으며, 저장소 수준 직업 범위와 사이트 내 skill 상세 페이지를 제공합니다.
이 저장소의 skills
Invoke and resume YAML-defined pipelines by name — /pipeline auto-dev runs the full release pipeline
Full Self Driving — autonomous release loop that processes all auto-dev-eligible GitHub issues until none remain, by repeatedly running /pipeline auto-dev then /homework.
On explicit /homework invocation, analyze the current and linked previous sessions, extract mistakes (찐빠), and report them via omcustom-feedback with a confirmation gate. Auto-activation on session cleanup/session-end signals is OPT-IN (default OFF) — requires an explicit project/user directive. Use when explicitly auditing recent work for harness gaps.
hada.io RSS feed monitoring for AI agent/harness articles with automated /scout analysis
Pre-action boundary checking — validates agent tool calls against declared capabilities and task contracts
Auto-detect project context and optimize harness — deactivate unused agents/skills, suggest missing experts, generate project profile
Adversarial code review using attacker mindset — trust boundary, attack surface, business logic, and defense evaluation
Multi-LLM adversarial consensus loop — 3+ LLMs compete to find flaws in designs/specs until unanimous agreement is reached
Apache Airflow best practices for DAG authoring, testing, and production deployment
Analyze project and auto-configure agents, skills, rules, and guides
AWS patterns from Well-Architected Framework
Monitor Claude Code releases and auto-generate GitHub issues for each new version
Execute OpenAI Codex CLI prompts and return results
YAML-based DAG workflow engine with topological execution and failure strategies
dbt best practices for SQL modeling, testing, and analytics engineering workflows
Routes data engineering tasks to the correct DE expert agent. Use when user requests data pipeline design, DAG authoring, SQL modeling, stream processing, or warehouse optimization.
Research-validated planning — research → plan → verify cycle for high-confidence implementation plans
Multi-angle release quality verification using parallel expert review teams
Routes development tasks to the correct language or framework expert agent. Use when user requests code review, implementation, refactoring, or debugging.
Review code against language-specific best practices
Django patterns for production-ready Python web applications
Docker patterns for optimized containerization
Parameterized evaluator-optimizer loop for quality-critical output with configurable rubrics
Flutter/Dart development best practices for widget composition, state management, and performance
Execute Gemini CLI prompts and return results
Structured SE task evaluation using 15 benchmark definitions from claude-code-harness research
Show help information for commands and system
Automatically detect user intent and route to appropriate agent
Modern Java 21 patterns from Virtual Threads, Pattern Matching, Records, and Sealed Classes
Apache Kafka best practices for event streaming, topic design, and producer-consumer patterns
Show all available commands
Memory persistence operations using claude-mem
Search and recall memories from claude-mem
Save current session context to claude-mem
Enable/disable OpenTelemetry console monitoring for Claude Code usage tracking
Parallel code verification using multiple models with severity classification
Apply verified improvement suggestions from eval-core analysis to omcustom configuration
Submit feedback about oh-my-customcode (supports anonymous submission)
Read-only report of improvement suggestions from eval-core analysis engine
Prevent session idle during background agent work via SubagentStop prompt hook auto-continuation