simplicity-first
Prevents overengineering by enforcing minimum viable code. No speculative features, no premature abstractions, no unnecessary complexity.
git clone --depth 1 https://github.com/DevelopersGlobal/ai-agent-skills /tmp/simplicity-first && cp -r /tmp/simplicity-first/skills/simplicity-first ~/.claude/skills/simplicity-firstSKILL.md
## Overview AI agents trend toward complexity. They add abstractions "for flexibility," error handling for impossible cases, and configuration for things that will never change. Left unchecked, they turn 50-line solutions into 500-line systems. This skill enforces a hard constraint: **write the minimum code that solves the stated problem and nothing more.** Andrej Karpathy's observation: *"They really like to overcomplicate code and APIs, bloat abstractions, don't clean up dead code... implement a bloated construction over 1000 lines when 100 would do."* ## When to Use - Starting any new implementation - When you feel the urge to add "just one more abstraction" - When reviewing your own generated code - When a simple task balloons into a complex solution ## Process ### Step 1: Define the Minimum Viable Solution 1. State what the code **must** do — write it as a list of requirements. 2. For each potential addition, ask: *"Was this explicitly requested?"* - If no → do not add it. 3. Write the simplest possible implementation that satisfies each requirement. **Verify:** You can trace every line of code back to a stated requirement. ### Step 2: Apply the Simplicity Test 4. After writing, read through the code and flag: - Abstractions used only once → inline them - Parameters never customized → hardcode them - Error handling for impossible scenarios → remove it - "Future-proofing" that wasn't asked for → delete it - Configuration flags for things that won't vary → remove them 5. Ask: *"Would a senior engineer call this overcomplicated?"* If yes, simplify. **Verify:** Each abstraction is used in at least 2 places; each parameter is actually varied. ### Step 3: Count the Lines 6. If your solution is more than 3× the length you'd expect for the task, something is wrong. 7. Actively look for ways to reduce line count without sacrificing readability. > Rule of thumb: If 200 lines could be 50, rewrite it. **Verify:** You've made at least one active attempt to reduce complexity. ### Step 4: Verify Functional Correctness 8. Run the tests. All pass? 9. Manually check the primary use case. 10. Check that no pre-existing tests regressed. **Verify:** All tests pass. No regressions. ## Common Rationalizations (and Rebuttals) | Excuse | Rebuttal | |--------|----------| | "We might need this later" | YAGNI. Add it when you need it. Unused code is a liability. | | "The abstraction makes it more flexible" | Flexibility you don't need adds complexity you always pay for. | | "I'm following the existing patterns" | Don't cargo-cult complex patterns into simple contexts. | | "It's only a few more lines" | Every extra line is a line to maintain, debug, and understand. | | "This is more robust" | Robust against what? Name the failure mode you're defending against. | ## Red Flags - You added a factory for a class that's instantiated once - You added configuration for values that never change - You wrote error handling for an operation that cannot fail - The abstraction layer is larger than the code it abstracts - You added a parameter "just in case" - Your PR adds 400 lines but the feature is 40 lines of actual logic ## Verification - [ ] Every line traces back to a stated requirement - [ ] No speculative features added - [ ] All abstractions used in 2+ places - [ ] No error handling for impossible scenarios - [ ] Line count is proportional to task complexity - [ ] All tests pass ## References - [surgical-changes skill](../surgical-changes/SKILL.md) - [refactoring skill](../refactoring/SKILL.md) - YAGNI principle — Martin Fowler
Validates, parses, and sanitizes AI-generated outputs before they reach end users or downstream systems. Structured output enforcement, schema validation, and fallback handling.
Design stable, versioned, self-documenting APIs. Easy to use correctly, hard to use incorrectly. Apply Hyrum's Law from day one.
Automated quality gates from commit to production. Every merge to main is potentially shippable. No manual steps in the deployment path.
Get layered, context-aware explanations of unfamiliar code. Understand what it does, why it was written that way, and how to work with it safely.
Structured code review focusing on correctness, security, and maintainability. Correctness before style. Every reviewer comment must be actionable.