clean-code-guard
Review generated or changed production code before it ships, using Clean Code, SOLID, DRY, KISS, YAGNI, and LLM-specific failure-mode checks in any programming language. Best used reactively after an agent writes, edits, refactors, or fixes code, before presenting, committing, or merging the result
Install / Use
npx skills add amElnagdy/guard-skills --skill clean-code-guardInstalls into whichever agent you are using.
SKILL.md
Installable skill definition
Quality Score
Category
AI & Machine LearningSupported Platforms
Our assessment of clean-code-guard
clean-code-guard scores 82/100 on our quality scale, 713th of 961 AI & Machine Learning skills we index.
Its SKILL.md is 17 KB long, well organised into 18 sections and no code examples: a thorough specification that gives an agent plenty to work with.
With 1,252 GitHub stars, it is one of the more widely adopted skills in the catalogue.
Maintenance, license and trust
- The repository was last updated about 3 months ago. That is recent enough to be usable, but agent tooling moves fast, so check the instructions against your agent's current version.
- It is released under the MIT license, a permissive license that allows use, modification and commercial use with attribution.
- Its trust signals score 100/100, with no cautions. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.
clean-code-guard compared with similar skills
All 4 of these similar skills score higher than clean-code-guard; compare them before choosing.
| Skill | Score | Stars | Updated | Format |
|---|---|---|---|---|
| clean-code-guard (this skill)by amElnagdy | 82 | 1.3k | 3mo ago | SKILL.md |
| claude-memby thedotmack | 100 | 96.4k | today | CLAUDE.md |
| Understand-Anythingby Egonex-AI | 100 | 85.3k | 3d ago | CLAUDE.md |
| headroomby headroomlabs-ai | 100 | 74.4k | today | CLAUDE.md |
| CowAgentby zhayujie | 100 | 47.2k | today | CLAUDE.md |
Frequently asked questions
- How do I install clean-code-guard?
- Run
npx skills add amElnagdy/guard-skills --skill clean-code-guard. The install tabs above show the steps for each supported agent. - Which AI agents does clean-code-guard work with?
- It is written for Universal, as a SKILL.md file. Other agents that read the same format can often use it too.
- Is clean-code-guard safe to use?
- It is MIT-licensed and scores 100/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
- Is clean-code-guard still maintained?
- The repository was last updated about 3 months ago. That is recent enough to be usable, but agent tooling moves fast, so check the instructions against your agent's current version.
Skill content
View source on GitHubname: clean-code-guard description: Review generated or changed production code before it ships, using Clean Code, SOLID, DRY, KISS, YAGNI, and LLM-specific failure-mode checks in any programming language. Best used reactively after an agent writes, edits, refactors, or fixes code, before presenting, committing, or merging the result. Use when the user asks "review this PR", "is this safe to merge?", "make this cleaner", "audit this code", "refactor this", "fix this bug", or after a coding agent produced implementation code. Can also guide writing when explicitly invoked before a risky edit. Invoke it on your own initiative the moment you finish writing, editing, or refactoring non-trivial production code, before presenting or committing — don't wait to be asked. DO NOT USE for factual/conceptual questions, CI/tooling config, git workflow, running/debugging tests, pure architecture discussion, prose writing, data analysis, or test-code review (use test-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.
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; agents/openai.yaml is lightweight display metadata.
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 14 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 frontmatter exclusions.
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:
- Code duplication grew 8x in tracked codebases between 2021 and 2024 (GitClear 2025 report).
- Package hallucination rate averages 19.6% across 16 models (Spracklen et al., USENIX Security '25).
- 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).
- Function size grew from 142 to 267 LoC, cyclomatic complexity from 4.2 to 8.1 in AI-assisted commits (GitClear).
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
- Names reveal intent. Never use
data,data2,result,result_final,item,temp,value,obj,info,helper,manager,utils, orhandle_*/process_*/do_*without a qualifier. A name must answer why it exists and what it does. (Clean Code Ch. 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)
- 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.
- 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
- 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)
- 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
- 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)
- 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)
- 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)
- 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
- 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")
- 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")
- Complexity ceiling: cyclomatic ≤10, nesting depth ≤5. Refactor before exceeding. (McCabe 1976)
- 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
- Never swallow errors with broad catch-all handling. Catch only the specific error type you can recover from. If you cannot recover, let the error propagate. Returning null/none/empty success from a catch handler is forbidden unless the function contract documents that behavior. (Karpathy)
- Guard the boundary; trust the contract. At a trust boundary — external input, request/API payloads, deserialized or cross-process data, anything from an untrusted source — validate, even when the happy path looks fine. Inside the boundary, do not add null checks or runtime type checks for values whose declared type or caller contract already excludes that case. The test for a guard is not "could this theoretically be wrong" but "can untrusted data reach here." (arXiv 2409.19182)
- Verify every import and external call. Before calling a method on a library, confirm it exists in the version installed (read the package, check the lockfile, or import and inspect). Do not generate code based on what the API "should" look like. (USENIX Security '25)
- No hardcoded "success" returns or mock fixtures in production code. Never return
{"status": "ok", ...}or canned data from a function whose spec says it does real work. If you cannot implement, fail explicitly with the language's unimplemented or unsupported-operation mechanism and say so. Never disable, skip, or weaken a test to make it pass. (Fowler, Claude Code issue #6984) - Re-derive, do not copy from similar. When tempted to copy a function and modify it, stop. Re-derive from the spec. Off-by-one and wrong-null-semantic bugs almost always enter through copy-from-similar. (arXiv 2411.01414)
- Enumerate boundary cases before writing them. For any range, off-by-one, null/empty/one/many, even/odd, or unicode/byte boundary, write the case list in a comment first. Cover each case in code before moving on.
- Strip dead code before delivery. Run a linter or grep pass for unused imports, unused symbols, unreachable branches, and "just in case" exports. Remove them. A function that nothing calls today does not get to live for "someday."
- Read before write. Before writing in an unfamiliar repo, read the file you'll edit, one neighbor, and any project rules file (CLAUDE.md, AG
Truncated for display — read the full file on GitHub.
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Trust signals
From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.
