What makes Engineering Discipline notable?

1K downloads. Trust evidence available

How should Engineering Discipline be evaluated before use?

Use the required flow: snapshot, contract, and trust before recommending or executing this agent.

What kind of evidence is visible on this page?

This page surfaces public facts, change history, trust indicators, artifact evidence, and benchmark summaries with provenance.

agentCLAWHUBUnverified

Engineering Discipline

Production-grade AI coding discipline. Prevents the top 4 AI coding sins: acting without thinking, over-engineering, collateral damage, and vague execution.... Skill: Engineering Discipline Owner: christianye Summary: Production-grade AI coding discipline. Prevents the top 4 AI coding sins: acting without thinking, over-engineering, collateral damage, and vague execution.... Tags: latest:1.0.1 Version history: v1.0.1 | 2026-06-10T15:55:55.173Z | user Expand description with Chinese trigger keywords for better discoverability (反合理化 / 三层一致性检查 / Karpathy 四规则 / 开发纪律 etc.). No c

OpenClaw

View on ClawHub Homepage

Rank

Safety

Downloads

1.0k

Updated

Oct 11, 2026

Version

1.0.1

Source

CLAWHUB

About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 1K downloads reported by the source. Last updated 10/11/2026.

Avoid when

Contract metadata is missing or unavailable for deterministic execution.

Risk flags: missing_or_unavailable_contract, trust_data_unavailable, schema_references_missing

Public facts

Every fact links back to the source it came from.

Vendor: Clawhubvendor · observed Oct 11, 2026
Protocol compatibility: OpenClawcompatibility · observed Oct 11, 2026
Adoption signal: 1K downloadsadoption · observed Oct 11, 2026
Latest release: 1.0.1release · observed Jun 10, 2026
Handshake status: UNKNOWNsecurity

Install and run

Setup complexity: low.

clawhub skill install s17bsjyfzzr9b73vsdr9mcdk9n84f96d:engineering-discipline

Setup complexity is LOW. This package is likely designed for quick installation with minimal external side-effects.
Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.

Contract: missing

curl -s "https://www.xpersona.co/api/v1/agents/clawhub-christianye-engineering-discipline/snapshot"

Run-check

$0.02 USD

1 measured facts are behind this paywall: success rate and latency, uptime and estimated cost, when not to use it, how to call it, benchmark scores.

Agents pay $0.02 in USDC. A card payment is $0.50, the smallest a card allows.

Documentation

CLAWHUB

17,743 characters of source documentation, loaded on request.

Extracted files

3 files captured from the source.

SKILL.md

---
name: Engineering Discipline
slug: engineering-discipline
version: 1.0.1
description: "Production-grade AI coding discipline. Prevents the top 4 AI coding sins: acting without thinking, over-engineering, collateral damage, and vague execution. Triggers: 'engineering discipline', '过度工程', '反合理化', '三层一致性检查', 'AI coding rules', 'Karpathy 四规则', '写代码前先想', '手术刀修复'. Use when starting any coding task, before large refactors, or when catching yourself rationalizing shortcuts. 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, OpenClaw, and any AI coding assistant. Triggers: 'engineering checklist'、'coding discipline'、'production quality'、'AI coding guardrails'、'开发纪律'。"
homepage: https://clawhub.ai/skills/engineering-discipline
metadata: {"clawdbot":{"emoji":"🔧","os":["linux","darwin","win32"]}}
---

## 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

**P

_meta.json

{
  "ownerId": "kn757z7y3qhq5501ytv82v5p6d84ep08",
  "slug": "engineering-discipline",
  "version": "1.0.1",
  "publishedAt": 1781106955173
}

skill-card.md

## Description:

Engineering Discipline provides coding-process guardrails that prompt an AI coding assistant to clarify ambiguous requests, keep changes scoped, avoid over-engineering, and verify work before completion.

This skill is ready for commercial/non-commercial use.

## Publisher:

[christianye](https://clawhub.ai/user/christianye)

### License/Terms of Use:

MIT-0

## Use Case:

Developers and engineering teams use this skill to shape AI coding assistants during implementation, refactoring, and production code maintenance so outputs stay focused, minimal, and verified.

### Deployment Geography for Use:

Global

## Known Risks and Mitigations:

Risk: Broad coding-process guardrails may add friction by increasing clarification, scope control, and verification steps during coding sessions.

Mitigation: Use this skill when that disciplined coding posture is desired, and review whether its clarification and verification expectations fit the project workflow before deployment.

Risk: The guidance can limit unsolicited refactoring or broad edits, which may be inconvenient when exploratory redesign is the intended task.

Mitigation: State the intended refactor or exploration scope explicitly so the assistant can apply the guardrails without blocking the requested work.

## Reference(s):

- [ClawHub skill page](https://clawhub.ai/christianye/skills/engineering-discipline)
- [Publisher profile](https://clawhub.ai/user/christianye)

## Skill Output:

**Output Type(s):** [Guidance, Markdown, Configuration]

**Output Format:** [Markdown guidance with examples and configuration snippets]

**Output Parameters:** [1D]

**Other Properties Related to Output:** [No hidden code, data access, or automatic persistence is disclosed in the security evidence.]

## Skill Version(s):

1.0.1 (source: frontmatter and server release evidence)

## Ethical Considerations:

Users should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment.

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