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Reduce a diff to its essential complexity, removing dead paths, over-engineering, premature abstraction, and incidental churn, without changing behavior.
Install with Codex or Claude Copy this prompt, paste it into Codex, Claude, or another assistant, and let it review the skill page and install it for you.
Based on SOC occupation classification
| name | simplify |
| description | Reduce a diff to its essential complexity, removing dead paths, over-engineering, premature abstraction, and incidental churn, without changing behavior. |
| scope | universal |
| backed-by | code-simplifier agent |
Run after implementation, before review. Takes the current diff and proposes removals and consolidations that preserve behavior while cutting complexity.
implement-review workflow.In Claude Code this delegates to the code-simplifier subagent. In a
single-agent runtime (Codex/Amp), perform the same pass inline.
Simplify never changes behavior. If a "simplification" would alter what the code does, that's a design change; route it back through planning, not through here.
Index of this project's coding-practice rules — architecture, coding style, testing, security, git workflow, task management, context layering, anti-slop, performance, and language-specific rules. The thin always-on essentials live in .claude/rules/common/house-rules.md; the full per-topic detail ships under .claude/rules/reference/ and .claude/rules/<lang>/ and is read on demand. Invoke this skill when you need the project's standards for a task, then open the specific rule file it points to.
Thin methodology for end-to-end tests of critical user journeys — define journeys by risk, use semantic locators and condition-based waits, quarantine flaky tests with a tracked reason, and capture artifacts on failure. Use when adding or stabilizing E2E coverage; the e2e-runner agent applies it in depth.
Design rigorous evaluations and benchmarks for AI agents, developer tools, retrieval systems, and repository-scale automation. Covers task selection, contamination control, metric choice tied to engineering decisions, and statistical validity. Use when asked to design an eval/benchmark, critique an existing benchmark, choose metrics for an agent or RAG system, or decide whether a measured improvement is real. NOT for running an existing performance-benchmark suite or a per-feature acceptance checklist, or one-off model spot-checks.
Interview the user relentlessly about a plan or design until reaching shared understanding, resolving each branch of the decision tree one at a time. Use for ambiguous or complex collaborative specs before any code is written.
Decision framework for choosing between regex and LLM when parsing structured text — start with regex, add LLM only for low-confidence edge cases. Use when parsing quizzes, forms, invoices, or documents with repeating structure and cost matters.
Review a repository for long-term architectural leverage rather than code quality — system structure, module boundaries, dependency graph, coupling, and drift. Produces a ranked set of highest-ROI improvements with evidence, effort, and risk. Use when asked to review the architecture, assess a codebase's structure/design, find where complexity is concentrated, or decide what to refactor next. NOT for style, naming, formatting, or line-level bugs (use a code-review skill for those).