Complete AI coding workflow system. Orchestration patterns, 18 hook events, 8 agents, cross-agent support, reference guides, and searchable learnings. Works with Claude Code, Cursor, and 32+ agents.
Install
The skill file stays in the publisher's repository.
Paste the copied command in a terminal. It installs this SKILL.md into that agent.
npx skills add rohitg00/pro-workflow --skill pro-workflow -a cursor -y
Download skills/pro-workflow/SKILL.md from the publisher, or copy the path and open the repository. PlugMCP lists the skill and does not host the file.
Where does pro-workflow live?
The publisher keeps it at https://github.com/rohitg00/pro-workflow.
Which agents can use pro-workflow?
Claude, Cursor, and Codex can read a SKILL.md you add from the publisher's repository.
Complete AI coding workflow system. Orchestration patterns, 18 hook events, 8 agents, cross-agent support, reference guides, and searchable learnings. Works with Claude Code, Cursor, and 32+ agents.
name: pro-workflow
description: Complete AI coding workflow system. Orchestration patterns, 18 hook events, 8 agents, cross-agent support, reference guides, and searchable learnings. Works with Claude Code, Cursor, and 32+ agents.
Pro Workflow
Complete AI coding workflow system from production use. Orchestration patterns, reference guides, and battle-tested habits that compound over time.
Works with: Claude Code, Cursor, Codex, Gemini CLI, and 32+ AI coding agents via skills add. Sections marked (Claude Code) use features specific to Claude Code — Cursor users can skip those or use the noted alternatives.
The Core Insight
"80% of my code is written by AI, 20% is spent reviewing and correcting it." — Karpathy
This skill optimizes for that ratio. Every pattern here reduces correction cycles.
1. The Self-Correction Loop
The single most powerful pattern. Your CLAUDE.md trains itself through corrections.
How It Works
When you correct Claude:
Claude acknowledges the mistake
Proposes a rule to prevent it
You approve → rule goes into memory
Future sessions avoid the same mistake
Add to CLAUDE.md
## Self-Correction Protocol
When the user corrects me or I make a mistake:
1. Acknowledge specifically what went wrong
2. Propose a concise rule: `[LEARN] Category: One-line rule`
3. Wait for approval before adding to LEARNED section
### LEARNED
Trigger Phrases
"Add that to your rules"
"Remember this"
"Don't do that again"
Example Flow
User: You edited the wrong file
Claude: I edited src/utils.ts when you meant src/lib/utils.ts.
[LEARN] Navigation: Confirm full path before editing files with common names.
Should I add this?
1b. Pre-Flight Discipline
Self-correction catches mistakes after the fact. This catches them before.
Karpathy's observations on LLM coding pitfalls name the upstream failures: silent assumptions, overcomplicated diffs, drive-by edits, vague success criteria. Four rules prevent each one.
200-line diffs that should be 50, speculative abstractions
Stay in your lane
Drive-by refactors, "improvements" to adjacent code
Verifiable goals
Endless re-clarification, "make it work" loops
Full rules in rules/pre-flight-discipline.mdc (alwaysApply: true). Pairs with self-correction: pre-flight stops the mistake, self-correction captures the lesson when one slips through.
Add to CLAUDE.md
## Pre-Flight Discipline
Before coding: state assumptions, present ambiguity, push back if simpler exists.
Every changed line traces to the request - no drive-by edits.
Convert imperatives to verifiable goals: "fix bug" → "failing test → make it pass".
2. Parallel Sessions with Worktrees
Zero dead time. While one Claude thinks, work on something else.
Setup
Claude Code:
claude --worktree # or claude -w (auto-creates isolated worktree)
Ctrl+F — Kill all background agents (two-press confirmation)
Ctrl+B — Send task to background
Subagents support isolation: worktree in agent frontmatter
When to Parallelize
Scenario
Action
Waiting on tests
Start new feature in worktree
Long build
Debug issue in parallel
Exploring approaches
Try 2-3 simultaneously
Add to CLAUDE.md
## Parallel Work
When blocked on long operations, use `claude -w` for instant parallel sessions.
Subagents with `isolation: worktree` get their own safe working copy.
3. The Wrap-Up Ritual
End sessions with intention. Capture learnings, verify state.
/wrap-up Checklist
Changes Audit - List modified files, uncommitted changes
State Check - Run git status, tests, lint
Learning Capture - What mistakes? What worked?
Next Session - What's next? Any blockers?
Summary - One paragraph of what was accomplished
Create Command
~/.claude/commands/wrap-up.md:
Execute wrap-up checklist:
1. `git status` - uncommitted changes?
2. `npm test -- --changed` - tests passing?
3. What was learned this session?
4. Propose LEARNED additions
5. One-paragraph summary
# Workflow Rules
## Planning
Plan mode when: >3 files, architecture decisions, multiple approaches.
## Quality Gates
Before complete: lint, typecheck, test --related.
## Subagents
Use for: parallel exploration, background tasks.
Avoid for: tasks needing conversation context.
SOUL.md
# Style
- Concise over verbose
- Action over explanation
- Acknowledge mistakes directly
- No features beyond scope
5. The 80/20 Review Pattern
Batch reviews at checkpoints, not every change.
Review Points
After plan approval
After each milestone
Before destructive operations
At /wrap-up
Add to CLAUDE.md
## Review Checkpoints
Pause for review at: plan completion, >5 file edits, git operations, auth/security code.
Between: proceed with confidence.
6. Model Selection
Current lineup (2026): Fable 5.1, Opus 5.5, Sonnet 5, and Haiku 4.5. The flagship tiers carry a 1M-token context; Haiku 4.5 is 200K. Frontier models converged, so the harness and the effort setting decide output quality more than the model choice. See references/models-2026.md for strings, prices, and routing.
Task
Model
Effort
Quick fixes, lookups
Haiku 4.5
low
Features, balanced work
Sonnet 5
high
Refactors, architecture, hard debug
Opus 5.5
xhigh
Long-horizon autonomous builds
Fable 5.1
high / xhigh
Effort and adaptive thinking
Fixed thinking budgets are retired on the current tiers. Control depth with effort (low through xhigh to max); xhigh is the default for coding and agentic work. Adaptive thinking lets the model calibrate reasoning per step with no fixed budget. Run grunt subagents at low effort on Haiku and keep the reasoning path on the capable tier.
Add to CLAUDE.md
## Model Hints
Route by task: Haiku 4.5 for lookups, Sonnet 5 for features, Opus 5.5 for
architecture and hard debugging, Fable 5.1 for long-horizon builds.
Effort is the lever, not thinking budgets: xhigh for coding, low for subagents.
7. Context Discipline
Context is finite even at 1M tokens (200K on Haiku 4.5). Manage it.
Rules
Read before edit
Compact at task boundaries
Disable unused MCPs (<10 enabled, <80 tools)
Summarize explorations
Use subagents to isolate high-volume output (tests, logs, docs)
Context Compaction
Auto-compacts at ~95% capacity (keeps long-running agents alive)
Use PreCompact hooks to save state before compaction
Subagents auto-compact independently from the main session
Good Compact Points
After planning, before execution
After completing a feature
When context >70%
Before switching task domains
8. Learning Log
Auto-document insights from sessions.
Add to CLAUDE.md
## Learning Log
After tasks, note learnings:
`[DATE] [TOPIC]: Key insight`
Append to .claude/learning-log.md
Learn Claude Code
Run /learn for a topic-by-topic guide covering sessions, context, CLAUDE.md, subagents, hooks, and more (see commands/learn.md). Official docs: https://code.claude.com/docs/
Quick Setup
Minimal
Add to your CLAUDE.md:
## Pro Workflow
### Self-Correction
When corrected, propose rule → add to LEARNED after approval.
### Planning
Multi-file: plan first, wait for "proceed".
### Quality
After edits: lint, typecheck, test.
### LEARNED
Compound improvements - Small corrections lead to big gains
Trust but verify - Let AI work, review at checkpoints
Zero dead time - Parallel sessions keep momentum
Memory is precious - Yours and the AI's
Orchestrate, don't micromanage - Wire patterns together, let agents execute
Complete AI coding workflow system from production use across Claude Code, Cursor, and beyond.
How do I install pro-workflow?
Download skills/pro-workflow/SKILL.md from the publisher, or copy the path and open the repository. PlugMCP lists the skill and does not host the file.
Where does pro-workflow live?
The publisher keeps it at https://github.com/rohitg00/pro-workflow.
Which agents can use pro-workflow?
Claude, Cursor, and Codex can read a SKILL.md you add from the publisher's repository.