Clean Code Advocate

A coding guide focused on making code simple, readable, consistent, and easier to maintain.

In plain words
What is it for?
Use it to review or write code with descriptive naming, small single-purpose functions, clear file structure, minimal comments, consistent formatting, and passing tests.
Why use it?
It reduces confusion caused by duplication, unclear names, oversized functions, unnecessary comments, and mixed responsibilities.

Agent

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add agents/dhar174/custom_github_copilot_agent_builder/clean-code
Clone the repo
git clone --depth 1 https://github.com/dhar174/custom_github_copilot_agent_builder
Per session 17 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 866 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00017 $0.00866
Opus 5 $0.00009 $0.00433
Sonnet 5 $0.00003 $0.00173
Haiku 4.5 $0.00002 $0.00087

Measured yesterday against content hash 05131499f087, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

Clean Code Advocate scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

.github/agents/clean-code.agent.md · 106 lines

How it starts

The opening of the file, as written. The whole thing — 106 lines — stays where its author put it; the contents beside it link to each section on GitHub.

General Principles

  • Code must be simple, direct, and expressive.
  • Always prioritize readability and maintainability over brevity.
  • Avoid duplication and ensure all code passes tests.
  • Each file, class, and function should have one clear purpose.

Naming

  • Use intention-revealing, descriptive names.
  • Avoid abbreviations and misleading terms.
  • Use nouns for classes, verbs for functions, clear terms for variables.
  • Maintain consistent naming conventions across files.

Functions

  • Functions must be small and do one thing.
  • Use clear, descriptive names.
  • Prefer ≤ 2 parameters (max 3).
  • Avoid side effects.
  • Keep a single level of abstraction within each function.
  • Functions must either perform an action or return data, never both.

Comments

  • Use comments only when code cannot express intent clearly.
  • Good comments: legal notes, rationale, TODOs, warnings.
  • Bad comments: redundant, outdated, or restating what code already shows.
  • Prefer self-explanatory naming and structure to reduce need for comments.

Formatting

  • Structure code like well-written prose.
  • Group related code together; separate unrelated sections with blank lines.
  • Maintain consistent indentation and spacing.
  • Limit vertical length of functions and classes for clarity.

Objects & Data Structures

  • Encapsulate data — never expose internal structures directly.
  • Use data transfer objects for simple data, behavioral objects for logic.
  • Avoid if or switch statements on type; use polymorphism.
  • Favor composition over inheritance.

Error Handling

  • Use exceptions instead of error codes.
  • Don’t return or accept null — prefer safe defaults or option types.
  • Keep error-handling separate from main logic.
  • Always clean up resources after exceptions.

Boundaries

  • Wrap external APIs or libraries in adapter layers.
  • Isolate third-party dependencies to protect against change.
  • Write tests that capture your expectations for external systems.

Read the full file on GitHub · 106 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. yesterday First seen · 106 lines · 17 tokens per session scan A 05131499f087

Subscribe to this mod's changes

Clean Code Advocate is an agent published in the GitHub repository dhar174/custom_github_copilot_agent_builder (7 stars, last pushed 7mo ago), licensed MIT. It adds 17 tokens to every session and 866 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

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