simplify-code

name	simplify-code
description	Parallel 3-agent cleanup of recent code changes.
version	1.0.0
author	Fabric (inspired by Claude Code /simplify)
license	MIT
platforms	["linux","macos","windows"]
metadata	{"fabric":{"tags":["code-review","cleanup","refactor","delegation","subagent","parallel","simplify"],"related_skills":["requesting-code-review","test-driven-development","plan"]}}

Simplify Code — Parallel Review & Cleanup

Review your recent code changes with three focused reviewers running in parallel, aggregate their findings, and apply the fixes worth applying.

Core principle: Three narrow reviewers beat one broad reviewer. Each one deeply searches the codebase for a single class of problem — reuse, quality, efficiency — without diluting its attention across all three. They run concurrently, so you pay the latency of one review, not three.

When to Use

Trigger this skill when the user says any of:

• "simplify" / "simplify my changes" / "simplify these changes"
• "review my code" / "review my recent changes" / "clean up my changes"
• "/simplify" (if they're carrying the Claude Code habit over)

Optional modifiers the user may add — honor them:

• Focus: "simplify focus on efficiency" → run only the efficiency reviewer (or weight the aggregation toward it). Recognized focuses: reuse, quality, efficiency.
• Dry run: "simplify but don't change anything" / "just report" → run the three reviewers, present findings, apply NOTHING. Ask before applying.
• Scope: "simplify the last commit" / "simplify staged" / "simplify src/foo.py" → narrow the diff source accordingly (see Phase 1).

Do NOT auto-run this after every edit. It costs three subagents' worth of tokens — invoke it only when the user explicitly asks.

The Process

Phase 1 — Identify the changes

Capture the diff to review. Pick the source by what the user asked for, in this default order:

# 1. Default: uncommitted working-tree changes (tracked files)
git diff

# 2. If that's empty, include staged changes
git diff HEAD

# 3. Scoped variants the user may request:
git diff --staged                 # "staged changes"
git diff HEAD~1                    # "the last commit"
git diff main...HEAD              # "this branch" / "my PR"
git diff -- src/foo.py            # specific file(s)

If git diff and git diff HEAD are both empty and there's no git repo or no changes, fall back to the files the user explicitly named or that were recently created/edited in this session. If you genuinely can't find any changed code, say so and stop — there's nothing to simplify.

Capture the full diff text. Note its size: if it's very large (say >2000 changed lines), warn the user that three subagents each carrying the full diff will be token-heavy, and offer to scope it down (per-directory, per-commit) before proceeding.

Phase 2 — Launch three reviewers in parallel

Use delegate_task batch mode — pass all three tasks in one tasks array so they run concurrently. Three is the right fan-out for this pattern; it's well within the delegation.max_concurrent_children budget on any default install.

Give every reviewer the complete diff (not fragments — cross-file issues hide in the gaps) plus the absolute repo path so they can search the wider codebase. Each reviewer gets terminal, file, and search toolsets (so they can git, read_file, and search_files/grep).

Tell each reviewer to:

• Search the existing codebase for evidence (don't reason from the diff alone).
• Apply Chesterton's Fence: before flagging anything for removal, run git blame on the line to understand why it exists. If you can't determine the original purpose, mark it confidence: low — don't guess.
• Report findings as structured output with confidence and risk:

  file:line → problem → suggested fix | confidence: high/medium/low | risk: SAFE/CAREFUL/RISKY
  

  • SAFE = proven not to affect behavior (unused imports, commented-out code, pass-through wrappers). Auto-apply these.
  • CAREFUL = improves without changing semantics (rename local variable, flatten nested ternary, extract helper). Apply with test verification.
  • RISKY = may change behavior or breaks public contracts (N+1 restructuring, public API rename, memory lifecycle change). Flag for human review — do NOT auto-apply.
• Skip nits and style-only churn. Only flag things that materially improve the code.

Pass these three goals (drop any the user's focus excludes):

Reviewer 1 — Code Reuse

Review this diff for code that duplicates functionality already in the codebase. Search utility modules, shared helpers, and adjacent files (use search_files / grep) for existing functions, constants, or patterns the new code could call instead of reimplementing. Flag: new functions that duplicate existing ones; hand-rolled logic that an existing utility already does (manual string/path manipulation, custom env checks, ad-hoc type guards, re-implemented parsing). For each, name the existing thing to use and where it lives.

Reviewer 2 — Code Quality

Review this diff for quality problems. Look for: redundant state (values that duplicate or could be derived from existing state; caches that don't need to exist); parameter sprawl (new params bolted on where the function should have been restructured); copy-paste-with-variation (near-duplicate blocks that should share an abstraction); leaky abstractions (exposing internals, breaking an existing encapsulation boundary); stringly-typed code (raw strings where a constant/enum/registry already exists — check the canonical registries before flagging); AI-generated slop patterns (extra comments restating obvious code like // increment counter above count++; unnecessary defensive null-checks on already-validated inputs; as any casts that bypass the type system; patterns inconsistent with the rest of the file). For each, give the concrete refactor.

Reviewer 3 — Efficiency

Review this diff for efficiency problems. Look for: unnecessary work (redundant computation, repeated file reads, duplicate API calls, N+1 access patterns); missed concurrency (independent ops run sequentially); hot-path bloat (heavy/blocking work on startup or per-request paths); TOCTOU anti-patterns (existence pre-checks before an op instead of doing the op and handling the error); memory issues (unbounded growth, missing cleanup, listener/handle leaks); overly broad reads (loading whole files when a slice would do); silent failures (empty catch blocks, ignored error returns, except: pass, .catch(() => {}) with no handling, error propagation gaps — these hide bugs and should at minimum log before swallowing). For each, give the concrete fix and why it's faster or safer.

Phase 3 — Aggregate and apply

Wait for all three to return (batch mode returns them together).

1. Merge the findings into one list, deduping where reviewers overlap.
2. Discard false positives — you have the most context; you don't have to argue with a reviewer, just drop weak or wrong suggestions silently.
3. Resolve conflicts. Reviewers can disagree (Reviewer 1: "use existing util X"; Reviewer 3: "X is slow, inline it"). Default resolution order: correctness > the user's stated focus > readability/reuse > micro-perf. Don't apply a perf "fix" that hurts clarity unless the path is genuinely hot. When two suggestions are mutually exclusive and both defensible, pick the one that touches less code and note the alternative.
4. Apply in risk-tier order:
   • SAFE first (auto-apply): unused imports, commented-out code, pass-through wrappers, redundant type assertions. Run tests after.
   • CAREFUL next (apply with verification, one file at a time): rename locals, flatten ternaries, extract helpers, consolidate dupes. Run tests after each file. Revert any that break.
   • RISKY last (flag for review — do NOT auto-apply): N+1 restructuring, public API changes, concurrency fixes, error-handling changes. Present each with risk description and test coverage status. If the user opted for a dry run, present all three tiers and apply nothing.
5. Verify you didn't break anything: run the project's targeted tests for the touched files (not the full suite), and re-run any linter/type check the repo uses. If a fix breaks a test, revert that one fix and report it.
6. Summarize what you changed: a short list of applied fixes grouped by reviewer category and risk tier, plus any findings you deliberately skipped and why.

Pitfalls

• Don't fan out wider than ~3. More reviewers means more cost and more conflicting suggestions to reconcile, not better coverage. Three categories cover the space.
• Give the WHOLE diff to each reviewer. Splitting the diff across reviewers defeats the design — cross-file duplication and N+1s only show up with the full picture.
• Reviewers search, they don't guess. A reuse finding with no pointer to the existing utility ("there's probably a helper for this") is noise. Require file:line evidence; drop findings that lack it.
• Apply ≠ rewrite. This is cleanup of the user's recent changes, not a license to refactor the whole module. Keep edits scoped to what the diff touched plus the minimal surrounding change a fix requires.
• Respect project conventions. If the repo has AGENTS.md / CLAUDE.md / FABRIC.md or a linter config, fold those rules into the reviewer prompts so suggestions match house style instead of fighting it.
• Large diffs blow context. If the diff is huge, scope it down before delegating — three subagents each carrying a 5000-line diff is expensive and may truncate.
• Over-trusting dead code tools. knip, ts-prune, and depcheck flag exports that ARE used dynamically (string-based imports, reflection). Always grep for the symbol name before removing — a clean tool report is not proof.
• Renaming without checking public contracts. Export names, API route paths, DB column names, and config keys are contracts — even if the name is bad, renaming breaks consumers. Tag public-contract changes as RISKY; never auto-rename them.
• Removing "unnecessary" error handling. An empty catch block or ignored error might be intentional — the error is expected and benign in that context. Flag it, don't remove it; let the human decide.

Related

If your install has the subagent-driven-development skill (optional), it covers the complementary case: parallel review during implementation, per task. This skill is the standalone after-the-fact cleanup pass. Use requesting-code-review for the pre-commit security/quality gate.