sickn33/agentic-awesome-skills/clean-code-guard

clean-code-guard

You are reviewing generated or changed code before it ships. Apply the rules below as a guard pass after the first implementation pass — and once this skill is active, keep applying it to every later code change in the same session, re-running the self-check before delivery after each edit rather than reverting to unguarded output because the skill loaded earlier. If the user explicitly invokes this skill before writing code, use the same rules while writing and still run the self-check before delivery.

When to Use

Use this skill when reviewing generated or changed code before it ships. Activate it reactively after an agent writes, edits, or refactors production code — especially after a first implementation pass. Re-run the guard pass before delivery after each edit.

Compatibility

This is a portable instruction skill. It requires no MCP server, network access, API key, shell command, local executable, or bundled script. It can be used in any runtime that supports SKILL.md plus directly linked references/ files. This bundle contains no runtime helper or host metadata file.

This skill does not replace project linters, formatters, type checkers, or test runners. Use the project's own tools for mechanical verification; use this skill for the judgement layer around code quality and review.

How to use this skill

This skill has three modes — pick based on the user's request.

Guard-pass mode (recommended): after code has been generated, edited, refactored, or fixed, check the diff or target files against the Always-applied imperatives below. Fix violations before presenting, committing, or merging the work.

Live mode (explicit): when the user invokes this skill before a risky code edit, apply the same imperatives while writing, then run the Self-check before delivery checklist. If you violate any rule, fix it before showing the user.

Review mode (triggered when the user asks you to review, audit, critique, or rate code): walk references/review-checklist.md against the target file(s) and produce a structured findings report. Do not edit code in review mode unless asked.

Across all three modes, the rule bodies live in references/. Read the relevant reference file when:

• You hit a rule you don't fully remember the reasoning for.
• The user pushes back on a rule and you need the source citation.
• You're in review mode and need the full checklist.
• The code under review touches a specific principle (e.g., subclassing → references/solid.md; deduplication → references/dry-kiss-yagni.md).

The reference files are:

• references/naming-and-functions.md — names, function size, parameters, command/query separation.
• references/comments-and-formatting.md — when to comment, when to delete, matching neighbor style.
• references/solid.md — SRP, OCP, LSP, ISP, DIP with the modern phrasings and detection smells.
• references/dry-kiss-yagni.md — knowledge vs code duplication, Sandi Metz's re-inline rule, McCabe complexity, Fowler's YAGNI cost categories.
• references/ai-failure-modes.md — the 15 systematic ways LLMs produce bad code. Read this one first if you are an AI agent reading this skill. It is the highest-leverage file in the skill.
• references/review-checklist.md — structured walk-through for review mode.
• references/sources.md — central bibliography for source URLs. Read it only when you need to verify or cite an external source.

Examples

• A coding agent implements an endpoint: use guard-pass mode on the diff before

the work is presented or committed.

• User asks "review this PR" or "should I merge this?": use review mode and

report findings from references/review-checklist.md; do not edit unless asked.

• User asks "implement this endpoint using clean-code-guard": use live mode

while writing, then run the self-check before delivery.

• User asks "refactor this function, same behavior": preserve observable

behavior exactly and treat any bug fix as a separate change.

Success criteria

This skill is working when code-writing tasks avoid the listed failure modes, code-review tasks produce prioritized findings with concrete evidence, and refactors preserve behavior unless the user explicitly asks for a behavior change. It should stay silent for conceptual, CI, git workflow, prose, data analysis, and test-running tasks covered by the scope exclusions below.

Why this skill exists

LLM-generated code has measurable, systematic failure modes that generic "follow clean code" instructions do not catch. Examples backed by published research:

• GitClear reports increased copy/paste and cloning in its observational code-change dataset; this does not establish that AI caused each change.
• Spracklen et al. measured package hallucinations across 16 models; the paper reports at least 5.2% for commercial and 21.7% for open-source models in its tested setting.
• LLMs often wrap risky operations in broad catch-all handlers that swallow errors (Karpathy).
• AI agents "declare success despite failing tests" by returning hardcoded fixture values (Fowler, Patterns for Reducing Friction).
• Do not infer a particular function-size or complexity increase without checking the underlying metric and study population.

The classic principles (Clean Code, SOLID, DRY/KISS/YAGNI) are still the foundation — but this skill adds the AI-specific layer most rule packs miss.

Always-applied imperatives

These are the rules to follow on every code change. They are imperative, not suggestions.

Functions and names

1. Names reveal intent. Never use data, data2, result, result_final, item, temp, value, obj, info, helper, manager, utils, or handle_*/process_*/do_* without a qualifier. A name must answer why it exists and what it does. (Clean Code Ch. 2)
2. Functions stay small. Target ≤20 lines, one level of abstraction, one thing. If you can extract a function with a name that doesn't restate the body, the parent was doing more than one thing. (Clean Code Ch. 3)
3. Four arguments is the hard ceiling. At five, stop and introduce a request/config object (record, struct, DTO, or equivalent). Never use boolean flag arguments — split into two functions instead.
4. No output arguments. A function either returns a value (query) or has a side effect (command). Never both. Command names use verbs; query names use nouns or getter-style names. (CQS)

Comments and structure

1. Comments explain *why*, never *what*. Delete any comment that paraphrases the line below it. Delete step-number scaffolding comments. Delete commented-out code — version control exists. (Clean Code Ch. 4)
2. Match the file's existing style. Read the file you're editing and at least one neighbor before writing. Mirror the casing, import order, error handling, logging, and HTTP/DB client choices. Do not introduce a second pattern.

SOLID

1. One actor per module. A class should be answerable to one stakeholder group (Accounting, Auth, Reporting). If two unrelated subsystems both reach into the same class, split it. (SRP, Uncle Bob 2014)
2. Extension via new code, not edits. If adding a new variant requires another type-tag branch in an existing function, refactor to a registry, strategy, or polymorphic dispatch first. (OCP)
3. No subclass refuses its parent's contract. Never override a method to signal "not implemented" or "unsupported operation." Never strengthen preconditions or weaken postconditions in an override. If you need to do that, the inheritance is wrong. (LSP)
4. Abstractions live with the client, not the implementation. When you introduce an interface, protocol, or abstract contract, put it in the package that consumes it, not next to the concrete class. (DIP)

DRY, KISS, YAGNI

1. Delete duplicated *knowledge*, not duplicated *text*. Two functions that look alike but encode different rules are not a DRY violation. One rule expressed in code + docs + schema is. (Pragmatic Programmer, "DRY")
2. The wrong abstraction is worse than duplication. If an abstraction has accumulated branches for each caller's special case, re-inline it back into callers, then delete the dead branches before re-abstracting. (Sandi Metz, "The Wrong Abstraction")
3. Complexity ceiling: cyclomatic ≤10, nesting depth ≤5. Refactor before exceeding. (McCabe 1976)
4. No speculative anything. No optional parameter, config flag, env var, feature toggle, interface, factory, or base class without a present-day caller. If you find yourself adding enable_*, use_*_v2, or *_mode, delete it and ship the concrete behavior. (Fowler, "Yagni")

AI-specific guardrails — the highest-leverage section