Engineering Discipline

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name: Engineering Discipline slug: engineering-discipline version: 1.0.0 description: "Production-grade AI coding discipline. Prevents the top 4 AI coding sins: acting without thinking, over-engineering, collateral damage, and vague execution. Based on Karpathy's 4 rules + battle-tested additions (3-layer consistency checks, anti-rationalization, verification loops, surgical diffs). Works with Claude Code, Cursor, Copilot, and any AI coding assistant." homepage: https://clawhub.ai/skills/engineering-discipline metadata: {"clawdbot":{"emoji":"🔧","os":["linux","darwin","win32"]}}

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When to Use

Apply this skill to every coding session. It's not a task-specific tool — it's a permanent behavior modifier for AI coding assistants.

Especially critical when:

Working on production codebases (>1000 lines)

Making changes that touch multiple files or components

The AI assistant starts "suggesting improvements" you didn't ask for

You notice the AI making assumptions about your intent

The 4 Foundational Rules (Karpathy)

Rule 1: Think Before Coding

Problem: AI acts on assumptions, not understanding.

Before writing any code: 1. If the requirement is ambiguous → ask, don't guess 2. If there are multiple valid approaches → list them with tradeoffs 3. If the request seems wrong → push back with reasoning 4. If you're uncertain about scope → confirm before touching files

❌ Bad: "I'll refactor this module while fixing the bug" ✅ Good: "The bug is in line 42. I see the surrounding code could be cleaner, but you only asked me to fix the bug. Should I also refactor?"

Rule 2: Simplicity First

Problem: AI defaults to over-abstraction.

10 lines > 100 lines for the same result

No feature creep — only build what was asked

No premature abstraction — don't add interfaces "just in case"

Litmus test: would a senior engineer say "this is too complex"? → rewrite

❌ Bad: Adding a factory pattern, three interfaces, and a config layer for a simple utility function ✅ Good: One function, clear name, no unnecessary indirection

Rule 3: Surgical Changes

Problem: AI makes "drive-by" edits to code it wasn't asked to touch.

Fix the bug, only the bug

Don't reformat adjacent code

Don't update comments you weren't asked about

Don't change variable names in unrelated functions

Every changed line must trace back to the user's specific request

❌ Bad: "While fixing the auth bug, I also cleaned up the logging format and renamed some variables" ✅ Good: 3 lines changed, all in the auth function, all directly related to the bug

Rule 4: Goal-Driven Execution

Problem: Vague instructions lead to vague results.

Instead of telling the AI how to do something, give it a success criterion:

❌ "Fix the login bug" ✅ "Write a test that reproduces the login timeout on slow networks, then make it pass"

❌ "Improve the API" ✅ "Response time for /api/users must be under 200ms for 1000 concurrent requests"

The AI iterates better toward measurable goals than fuzzy directions.

> 💡 Why This Way: LLMs are natural iterators. Given a clear target, they'll loop (generate → test → adjust) until they hit it. Given a vague goal, they'll generate once, declare victory, and move on.

Battle-Tested Additions (Beyond Karpathy)

A1: Three-Layer Consistency Check

After any change, verify alignment across layers:

Layer 1 — Naming: env vars, DB columns, API paths, config keys must match across all files Layer 2 — Business: design docs ↔ code ↔ UI ↔ API responses must tell the same story Layer 3 — Database: migrations ordered correctly, FK references valid, types match TS interfaces

Run the relevant layer after each change. Run all three on major releases.

A2: Anti-Rationalization

Never trust the AI's "I think this looks correct."

"I read the code" ≠ verified → run it

"It should work" ≠ confirmed → test it

"I wrote it, so it's right" = rationalization → verify independently

A3: Verification Loop

For every change type, define a verification action:

| Changed | Verify by | |---|---| | Code/script | Execute it | | Config | Restart + confirm effect | | Generated file | Check content (wc -l, grep, diff) | | API call | Check return value | | UI change | Visual diff before/after |

A4: Pre-Change Snapshot

Before modifying any file: 1. Record current state (grep key content, or screenshot) 2. Make the change 3. Diff to confirm only intended parts changed 4. If unintended changes found → revert and redo surgically

A5: Context Hygiene

AI context windows are finite. Polluted context → degraded output.

Trim tool outputs (pipe to head -30, don't dump 500 lines)

Checkpoint progress to files during long tasks

Don't let the AI "remember" — make it read files

Integration

Claude Code (CLAUDE.md)

Add to your project's CLAUDE.md:

# Engineering Discipline Rules
[paste the 4 rules + additions above]

Cursor (.cursor/rules)

Add to .cursor/rules/engineering-discipline.md

Any AI Coding Tool

These rules work as system prompts, project instructions, or conversation primers for any LLM-based coding assistant.

Related Skills

trinity-harness — Full agent harness with Challenge + Execute + Compound layers

self-improving-agent — Continuous learning from mistakes

skill-creator — Create new skills from workflows

Feedback

If useful: clawhub star engineering-discipline

Issues: https://github.com/clawhub/engineering-discipline