Clean Code Skill

Overview

This public intake copy packages

plugins/antigravity-awesome-skills/skills/clean-code

https://github.com/sickn33/antigravity-awesome-skills

Use it when the operator needs the upstream workflow, support files, and repository context to stay intact while the public validator and private enhancer continue their normal downstream flow.

This intake keeps the copied upstream files intact and uses

metadata.json

ORIGIN.md

Clean Code Skill This skill embodies the principles of "Clean Code" by Robert C. Martin (Uncle Bob). Use it to transform "code that works" into "code that is clean."

Imported source sections that did not map cleanly to the public headings are still preserved below or in the support files. Notable imported sections: 🧠 Core Philosophy, 1. Meaningful Names, 2. Functions, 3. Comments, 4. Formatting, 5. Objects and Data Structures.

When to Use This Skill

Use this section as the trigger filter. It should make the activation boundary explicit before the operator loads files, runs commands, or opens a pull request.

• Writing new code: To ensure high quality from the start.
• Reviewing Pull Requests: To provide constructive, principle-based feedback.
• Refactoring legacy code: To identify and remove code smells.
• Improving team standards: To align on industry-standard best practices.
• Use when the request clearly matches the imported source intent: This skill embodies the principles of "Clean Code" by Robert C. Martin (Uncle Bob). Use it to transform "code that works" into "code that is clean.".
• Use when the operator should preserve upstream workflow detail instead of rewriting the process from scratch.

Operating Table

metadata.json

ORIGIN.md

SKILL.md

SKILL.md

## Related Skills

Workflow

This workflow is intentionally editorial and operational at the same time. It keeps the imported source useful to the operator while still satisfying the public intake standards that feed the downstream enhancer flow.

1. Confirm the user goal, the scope of the imported workflow, and whether this skill is still the right router for the task.
2. Read the overview and provenance files before loading any copied upstream support files.
3. Load only the references, examples, prompts, or scripts that materially change the outcome for the current request.
4. Execute the upstream workflow while keeping provenance and source boundaries explicit in the working notes.
5. Validate the result against the upstream expectations and the evidence you can point to in the copied files.
6. Escalate or hand off to a related skill when the work moves out of this imported workflow's center of gravity.
7. Before merge or closure, record what was used, what changed, and what the reviewer still needs to verify.

Imported Workflow Notes

Imported: 🧠 Core Philosophy

"Code is clean if it can be read, and enhanced by a developer other than its original author." — Grady Booch

Examples

Example 1: Ask for the upstream workflow directly

Use @clean-code-v2 to handle <task>. Start from the copied upstream workflow, load only the files that change the outcome, and keep provenance visible in the answer.

Explanation: This is the safest starting point when the operator needs the imported workflow, but not the entire repository.

Example 2: Ask for a provenance-grounded review

Review @clean-code-v2 against metadata.json and ORIGIN.md, then explain which copied upstream files you would load first and why.

Explanation: Use this before review or troubleshooting when you need a precise, auditable explanation of origin and file selection.

Example 3: Narrow the copied support files before execution

Use @clean-code-v2 for <task>. Load only the copied references, examples, or scripts that change the outcome, and name the files explicitly before proceeding.

Explanation: This keeps the skill aligned with progressive disclosure instead of loading the whole copied package by default.

Example 4: Build a reviewer packet

Review @clean-code-v2 using the copied upstream files plus provenance, then summarize any gaps before merge.

Explanation: This is useful when the PR is waiting for human review and you want a repeatable audit packet.

Best Practices

Treat the generated public skill as a reviewable packaging layer around the upstream repository. The goal is to keep provenance explicit and load only the copied source material that materially improves execution.

• Keep the imported skill grounded in the upstream repository; do not invent steps that the source material cannot support.
• Prefer the smallest useful set of support files so the workflow stays auditable and fast to review.
• Keep provenance, source commit, and imported file paths visible in notes and PR descriptions.
• Point directly at the copied upstream files that justify the workflow instead of relying on generic review boilerplate.
• Treat generated examples as scaffolding; adapt them to the concrete task before execution.
• Route to a stronger native skill when architecture, debugging, design, or security concerns become dominant.

Troubleshooting

Problem: The operator skipped the imported context and answered too generically

Symptoms: The result ignores the upstream workflow in

plugins/antigravity-awesome-skills/skills/clean-code

metadata.json

ORIGIN.md

Problem: The imported workflow feels incomplete during review

Symptoms: Reviewers can see the generated

SKILL.md

Problem: The task drifted into a different specialization

Symptoms: The imported skill starts in the right place, but the work turns into debugging, architecture, design, security, or release orchestration that a native skill handles better. Solution: Use the related skills section to hand off deliberately. Keep the imported provenance visible so the next skill inherits the right context instead of starting blind.

Related Skills

• @chrome-extension-developer-v2

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

• @churn-prevention-v2

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

• @circleci-automation-v2

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

• @cirq-v2

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

Additional Resources

Use this support matrix and the linked files below as the operator packet for this imported skill. They should reflect real copied source material, not generic scaffolding.

references

references/n/a

examples

examples/n/a

scripts

scripts/n/a

agents

agents/n/a

assets

assets/n/a

Imported Reference Notes

Imported: 1. Meaningful Names

• Use Intention-Revealing Names:

  elapsedTimeInDays

  instead of

  d

  .
• Avoid Disinformation: Don't use

  accountList

  if it's actually a

  Map

  .
• Make Meaningful Distinctions: Avoid

  ProductData

  vs

  ProductInfo

  .
• Use Pronounceable/Searchable Names: Avoid

  genymdhms

  .
• Class Names: Use nouns (

  Customer

  ,

  WikiPage

  ). Avoid

  Manager

  ,

  Data

  .
• Method Names: Use verbs (

  postPayment

  ,

  deletePage

  ).

Imported: 2. Functions

• Small!: Functions should be shorter than you think.
• Do One Thing: A function should do only one thing, and do it well.
• One Level of Abstraction: Don't mix high-level business logic with low-level details (like regex).
• Descriptive Names:

  isPasswordValid

  is better than

  check

  .
• Arguments: 0 is ideal, 1-2 is okay, 3+ requires a very strong justification.
• No Side Effects: Functions shouldn't secretly change global state.

Imported: 3. Comments

• Don't Comment Bad Code—Rewrite It: Most comments are a sign of failure to express ourselves in code.
• Explain Yourself in Code:

  # Check if employee is eligible for full benefits
  if employee.flags & HOURLY and employee.age > 65:
  

  vs

  if employee.isEligibleForFullBenefits():
  

• Good Comments: Legal, Informative (regex intent), Clarification (external libraries), TODOs.
• Bad Comments: Mumbling, Redundant, Misleading, Mandated, Noise, Position Markers.

Imported: 4. Formatting

• The Newspaper Metaphor: High-level concepts at the top, details at the bottom.
• Vertical Density: Related lines should be close to each other.
• Distance: Variables should be declared near their usage.
• Indentation: Essential for structural readability.

Imported: 5. Objects and Data Structures

• Data Abstraction: Hide the implementation behind interfaces.
• The Law of Demeter: A module should not know about the innards of the objects it manipulates. Avoid

  a.getB().getC().doSomething()

  .
• Data Transfer Objects (DTO): Classes with public variables and no functions.

Imported: 6. Error Handling

• Use Exceptions instead of Return Codes: Keeps logic clean.
• Write Try-Catch-Finally First: Defines the scope of the operation.
• Don't Return Null: It forces the caller to check for null every time.
• Don't Pass Null: Leads to

  NullPointerException

  .

Imported: 7. Unit Tests

• The Three Laws of TDD:
  1. Don't write production code until you have a failing unit test.
  2. Don't write more of a unit test than is sufficient to fail.
  3. Don't write more production code than is sufficient to pass the failing test.
• F.I.R.S.T. Principles: Fast, Independent, Repeatable, Self-Validating, Timely.

Imported: 8. Classes

• Small!: Classes should have a single responsibility (SRP).
• The Stepdown Rule: We want the code to read like a top-down narrative.

Imported: 9. Smells and Heuristics

• Rigidity: Hard to change.
• Fragility: Breaks in many places.
• Immobility: Hard to reuse.
• Viscosity: Hard to do the right thing.
• Needless Complexity/Repetition.

Imported: 🛠️ Implementation Checklist

• Is this function smaller than 20 lines?
• Does this function do exactly one thing?
• Are all names searchable and intention-revealing?
• Have I avoided comments by making the code clearer?
• Am I passing too many arguments?
• Is there a failing test for this change?

Imported: Limitations

• Use this skill only when the task clearly matches the scope described above.
• Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
• Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.