workflows▌

def product_list(request): products = Product.objects.all() return JsonResponse({'products': list(products.values())})

Orchestrate a comprehensive git workflow from code review through PR creation, leveraging specialized agents for quality assurance, testing, and deployment readiness. This workflow implements modern git best practices including Conventional Commits, automated testing, and structured PR creation.

Advanced Git history management with rebasing, cherry-picking, bisect, worktrees, and reflog recovery. \n \n Interactive rebase enables commit squashing, rewording, reordering, and dropping to clean history before merging \n Cherry-pick applies specific commits across branches; bisect uses binary search to find commits that introduced bugs \n Worktrees allow simultaneous work on multiple branches without stashing or switching contexts \n Reflog tracks all ref movements and recovers deleted commi

Write comprehensive failing tests following TDD red phase principles.

You are an expert onboarding specialist and knowledge transfer architect with deep experience in remote-first organizations, technical team integration, and accelerated learning methodologies. Your role is to ensure smooth, comprehensive onboarding that transforms new team members into productive contributors while preserving institutional knowledge.

Execute a comprehensive Test-Driven Development (TDD) workflow with strict red-green-refactor discipline:

Use these patterns to automate build and deploy actions and invoke workflow jobs.

Write and validate EAS CI/CD workflow YAML files for Expo projects. \n \n Fetches the latest JSON schema from Expo's API to ensure job types, parameters, triggers, and runner configurations are current \n Supports dynamic expressions using ${{ }} syntax with contexts for GitHub events, workflow inputs, job outputs, and step results \n Includes built-in validation script that checks workflow structure against the schema and reports errors before deployment \n Provides reference documentation for

Automate AI workflows combining multiple models and services via bash scripting and the inference.sh CLI. \n \n Supports five core patterns: batch processing, sequential pipelines, parallel execution, conditional branching, and retry logic with fallbacks \n Integrates with inference.sh CLI for model invocation, bash for orchestration, and Python SDK for programmatic workflows \n Includes cron job setup for scheduled automation, logging wrappers for monitoring, and webhook-based error alerting \n

Orchestrate parallel AI agent sessions using dmux, a tmux pane manager for agent harnesses.