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pro-workflow

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Complete AI coding workflow system. Orchestration patterns, 18 hook events, 5 agents, cross-agent support, reference guides, and searchable learnings. Works with Claude Code, Cursor, and 32+ agents.

AI Agents

What this skill does


# 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

```markdown
## 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

```text
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](https://x.com/karpathy/status/2015883857489522876) 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

```markdown
## 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:**
```bash
claude --worktree    # or claude -w (auto-creates isolated worktree)
```

**Cursor / Any editor:**

```bash
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

```markdown
## 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`:

```markdown
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

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

### AGENTS.md

```markdown
# 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

```markdown
# 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

```markdown
## 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](https://docs.anthropic.com/en/docs/about-claude/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

```markdown
## 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

```markdown
## 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:

```markdown
## Pro Workflow

### Self-Correction
When corrected, propose rule → add to LEARNED
Files: 1
Size: 15.9 KB
Complexity: 20/100
Category: AI Agents

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