pro-workflow

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 SkillKit. 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:

1. Claude acknowledges the mistake
2. Proposes a rule to prevent it
3. You approve → rule goes into memory
4. 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
<!-- Auto-populated through corrections -->

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.

| Rule | Prevents | |------|----------| | Surface, don't assume | Wrong interpretation, hidden confusion, missing tradeoffs | | Minimum viable code | 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)

Cursor / Any editor:

git worktree add ../project-feat feature-branch
git worktree add ../project-fix bugfix-branch

Background Agent Management (Claude Code)

• 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

1. Changes Audit - List modified files, uncommitted changes
2. State Check - Run git status, tests, lint
3. Learning Capture - What mistakes? What worked?
4. Next Session - What's next? Any blockers?
5. 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

4. Split Memory Architecture

For complex projects, modularize Claude memory.

Structure

.claude/
├── CLAUDE.md        # Entry point
├── AGENTS.md        # Workflow rules
├── SOUL.md          # Style preferences
└── LEARNED.md       # Auto-populated

AGENTS.md

# 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

1. After plan approval
2. After each milestone
3. Before destructive operations
4. 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

Opus 4.6 and Sonnet 4.6 both support adaptive thinking and 1M-token context (as of 2025-08). The 1M context is available as a beta option (via the context-1m-2025-08-07 beta header); the default context window remains 200K. Sonnet 4.5 (200K context) has been retired from the Max plan in favor of Sonnet 4.6. See Models overview for current capabilities.

| Task | Model | |------|-------| | Quick fixes, exploration | Haiku 4.5 | | Features, balanced work | Sonnet 4.6 | | Refactors, architecture | Opus 4.6 | | Hard bugs, multi-system | Opus 4.6 |

Adaptive Thinking

Opus 4.6 and Sonnet 4.6 automatically calibrate reasoning depth per task — lightweight for simple operations, deep analysis for complex problems. No configuration needed. Extended thinking is built-in.

Add to CLAUDE.md

## Model Hints (as of 2025-08)
Opus 4.6 and Sonnet 4.6 auto-calibrate reasoning depth — no need to toggle thinking mode.
Use subagents with Haiku for fast read-only exploration, Sonnet 4.6 for balanced work.
Docs: https://docs.anthropic.com/en/docs/about-claude/models/overview

7. Context Discipline

200k tokens is precious. Manage it.

Rules

1. Read before edit
2. Compact at task boundaries
3. Disable unused MCPs (<10 enabled, <80 tools)
4. Summarize explorations
5. Use subagents to isolate high-volume output (tests, logs, docs)

Context Compaction

• Auto-compacts at ~95% capacity (keeps long-running agents alive)
• Configure earlier compaction: CLAUDE_AUTOCOMPACT_PCT_OVERRIDE=50
• 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