Automate Claude Code workflow management with memory, parallel sessions, and wrap-up rituals.
npx skills add https://github.com/rohitg00/pro-workflow --skill pro-workflow
Please help me install this Agent Skill. Skill: pro-workflow Source: https://github.com/rohitg00/pro-workflow/tree/main/skills/pro-workflow Command: npx skills add https://github.com/rohitg00/pro-workflow --skill pro-workflow
This skill tackles the overhead of building reliable Claude Code workflows by combining memory management, parallel sessions, and structured wrap-up rituals to reduce correction cycles and context switching.
Switch to dev mode and begin a session by establishing CLAUDE.md structure, then enable parallel worktrees and the wrap-up ritual as you complete tasks.
High-intent search queries and answers about installing and using this skill.
Managing Claude Code memory across multiple sessions requires a split architecture that organizes rules and learnings into modular files like CLAUDE.md, AGENTS.md, SOUL.md, and LEARNED.md. This structure preserves context and applies past corrections to future coding tasks.
Yes, you can run parallel Claude Code sessions using parallel worktrees. This workflow pattern allows you to execute multiple sessions simultaneously, significantly reducing idle time while waiting for individual coding or thinking tasks to complete.
The best way to capture learnings after a coding session is to use a structured wrap-up ritual. This enforces a documented checklist at the end of sessions to automatically document insights, capture rules, and plan next steps for future reference.
Claude Code handles context limits through strict context discipline, which manages token budgets and prunes explorations to stay within limits. This is combined with 80/20 batch reviews at checkpoints to optimize iteration time and maintain efficiency.
Your Claude Code workflow requires constant corrections because it lacks a self-correction loop. By building memory of user corrections and storing them in modular files, Claude learns from past mistakes and improves future sessions without repeated guidance.
No, you do not need to manually configure model selection for every task. The workflow includes adaptive thinking and multi-model guidance that automatically adjusts the model selection based on the specific requirements of different software development tasks.
Moonsong-LabsCreate and validate isolated Git worktrees for feature development.
AndreJorgeLopesReview completed sessions and retain approved categorized learnings in Hindsight.
xxihRoute LLM requests by complexity and track immutable costs against budgets.
kivo360Manage Claude Code session memory with just-in-time retrieval and context summaries.
