coding-principles - SKILL.md Agent Skill

name: coding-principles description: Behavioral guidelines to reduce common LLM coding mistakes. Emphasizes thinking before coding, simplicity, surgical changes, and goal-driven execution. Load this when writing or modifying code to avoid overengineering and unnecessary changes. user-invocable: true

Coding Principles

Behavioral guidelines to reduce common LLM coding mistakes.

Tradeoff: These guidelines bias toward caution over speed. For trivial tasks, use judgment.

1. Think Before Coding

Don't assume. Don't hide confusion. Surface tradeoffs.

Before implementing:

State your assumptions explicitly. If uncertain, ask.
If multiple interpretations exist, present them - don't pick silently.
If a simpler approach exists, say so. Push back when warranted.
If something is unclear, stop. Name what's confusing. Ask.

2. Simplicity First

Minimum code that solves the problem. Nothing speculative.

No features beyond what was asked.
No abstractions for single-use code.
No "flexibility" or "configurability" that wasn't requested.
No error handling for impossible scenarios.
If you write 200 lines and it could be 50, rewrite it.

Ask yourself: "Would a senior engineer say this is overcomplicated?" If yes, simplify.

3. Surgical Changes

Touch only what you must. Clean up only your own mess.

When editing existing code:

Don't "improve" adjacent code, comments, or formatting.
Don't refactor things that aren't broken.
Match existing style, even if you'd do it differently.
If you notice unrelated dead code, mention it - don't delete it.

When your changes create orphans:

Remove imports/variables/functions that YOUR changes made unused.
Don't remove pre-existing dead code unless asked.

The test: Every changed line should trace directly to the user's request.

4. Goal-Driven Execution

Define success criteria. Loop until verified.

Transform tasks into verifiable goals:

"Add validation" → "Write tests for invalid inputs, then make them pass"
"Fix the bug" → "Write a test that reproduces it, then make it pass"
"Refactor X" → "Ensure tests pass before and after"

For multi-step tasks, state a brief plan:

1. [Step] → verify: [check]
2. [Step] → verify: [check]
3. [Step] → verify: [check]

Strong success criteria let you loop independently. Weak criteria ("make it work") require constant clarification.

5. Minimal Comments

Code should be self-documenting. Comments are a last resort.

Remove comments that state the obvious (e.g., # Save the file before file.save())
Remove section header comments (e.g., # Configuration, # Helper functions)
Keep docstrings for public APIs (functions, classes) with Args/Returns
Keep comments only when explaining non-obvious why, not what

When a comment is necessary:

First word must be lowercase (e.g., # handling edge case not # Handling edge case)
Use -ing form (e.g., # parsing response not # Parse response)

Examples:

# bad - stating the obvious
# Get the user
user = get_user(id)

# bad - capitalized, imperative
# Parse the JSON response
data = json.loads(response)

# good - lowercase, -ing, explains why
# handling malformed responses from legacy API
if "data" not in response:
    response = {"data": response}

6. Avoid AI Slop

Before committing, review your diff for LLM-specific anti-patterns.

Common AI code smells to remove:

Type casts to any (TS) or # type: ignore (Python) to silence errors instead of fixing them
Overly verbose variable names or redundant intermediate variables
"Just in case" fallbacks that can never trigger given the call site
Inconsistent style with surrounding code (see Section 3)
Redundant comments (see Section 5)

Review workflow: Check your diff against main and ask "Would a human have written this?" If code looks defensive, verbose, or out of place—it's probably AI slop. Remove it.

These guidelines are working if: fewer unnecessary changes in diffs, fewer rewrites due to overcomplication, and clarifying questions come before implementation rather than after mistakes.