nousresearch/hermes-agent/simplify-code

Simplify Code — Parallel Review & Cleanup

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

This is a cleanup pass, not a bug hunt. You are improving the quality of code that already works — removing duplication, flattening needless complexity, cutting waste, and deepening band-aid fixes. Do not go hunting for correctness bugs here; that's what requesting-code-review is for.

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

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 (also accepts simplification), efficiency, altitude.

• Dry run: "simplify but don't change anything" / "just report" → run the

four 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 or tack it onto the end of unrelated tasks. It costs four 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 four 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 four reviewers in parallel

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

No delegation available? If you can't call delegate_task in this context (you're a leaf subagent, delegation is disabled, or the budget is exhausted), do NOT skip the review or drop angles. Work through all four reviewer angles yourself, sequentially, in this context — same search standards, same finding format. Then say clearly in your final summary that this was a single-pass inline review, not the parallel fan-out, so the user knows what actually ran.

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 the concrete cost, confidence,

and risk: `` file:line → problem → cost (what's duplicated/wasted/harder to maintain) → suggested fix | confidence: high/medium/low | risk: SAFE/CAREFUL/RISKY `` The cost field forces each finding to justify itself — a finding that can't articulate what the problem actually costs is probably a nit.

• 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 four 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); deeply nested conditionals (ternary

chains, 3+-level if/else pyramids — flatten with guard clauses, early

returns, or a lookup table); 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; long-lived callbacks or objects built as

closures that capture the whole enclosing scope — everything captured

stays alive as long as the object does, so prefer a small class or

explicit-fields struct that copies only what it needs); 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.

Reviewer 4 — Altitude

Review this diff for changes implemented at the wrong depth — band-aids

layered on top of shared infrastructure instead of fixes to the

infrastructure itself. Signs of a too-shallow fix: a special case added to

a generic code path to handle one caller (an if (caller == X) branch, a

type check, a magic-value escape hatch); a symptom patched at the call

site while sibling call sites keep the same flaw; a workaround stacked on

an earlier workaround; a wrapper added to avoid touching the thing that

actually needs changing; configuration or flags introduced to route around

a broken default instead of fixing the default. For each, identify the

underlying mechanism the change is dodging and describe the deeper fix —

generalize the shared path, fix the root default, or fix the whole bug

class — and honestly note when the deeper fix is large enough that it

should be its own task rather than part of this cleanup. Read the

surrounding code and git blame first: what looks like a band-aid is

sometimes a deliberate boundary (compat shims, staged migrations,

vendored-code isolation). Don't flag those.

Phase 3 — Aggregate and apply

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

1. Merge the findings into one list, deduping where reviewers overlap —

when two findings target the same line or the same underlying mechanism, collapse them into one.

1. Discard false positives — you have the most context; you don't have to

argue with a reviewer, just drop weak or wrong suggestions silently.

1. 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.

1. 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. Altitude findings usually land here — deepening a fix means touching shared infrastructure, so present the deeper fix and let the user decide whether to do it now or as a follow-up. If the user opted for a dry run, present all three tiers and apply nothing.

1. 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.

1. 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. If you ran inline (no delegation), say so here.

Pitfalls