---

name: code-simplifier description: > Cleanup pass that edits the working tree — over a session's uncommitted changes, or a named path or whole codebase. Reads git diff (or the named target) and simplifies, consolidates, and aligns code with the existing codebase — modernize syntax, cut unnecessary complexity and slop, consolidate duplicated logic, catch efficiency issues. Not a bug hunt: defects are reported, not fixed. Use after a substantive working session, or when asked to clean up, simplify, reduce slop, consolidate, modernize, tighten up, de-slop, or scan a codebase. For @cyanheads/mcp-ts-core projects, includes specific transformations for tool/resource/prompt definitions, the ctx pattern, error factories, and framework idioms. metadata: author: cyanheads version: "1.7" audience: external type: workflow

Code Simplifier

Cleanup pass over a session's changes or a named target. Reviews the code in scope, understands how it fits the existing codebase, and makes targeted improvements — modernizing syntax, removing unnecessary complexity, consolidating duplicated logic, catching efficiency issues. Prioritizes codebase cohesion over local perfection.

Core philosophy

Every change must earn its keep. A simplification that doesn't meaningfully improve clarity, correctness, or cohesion is noise. Don't refactor for refactoring's sake. Don't create new files, abstractions, or utilities unless they solve a demonstrated problem. If the existing code works and is readable, leave it alone. The goal is a cohesive codebase, not a pristine one.

Procedure

Phase 1: Set the scope

Two scopes; the caller's wording picks one, and the diff is the default.

• Diff (nothing named): run git status to see the shape of the working tree, then git diff HEAD for all uncommitted changes (staged and unstaged). Untracked files never appear in the diff — list them with git ls-files --others --exclude-standard (git status collapses a new directory to one line) and read them directly. If the diff is empty and there are no untracked files, review the last commit (git diff HEAD~1 HEAD); if that is also empty, say the tree is clean and stop. Don't go hunting through the codebase for files to improve.
• Target (a named path, module, or "the whole codebase"): the named files are the scope, whatever their git state. Work one module or directory at a time and re-run the gate after each, so a large scan never becomes one unverifiable diff. Take the target as named — don't rank or narrow it by commit history.

Phase 2: Understand the surrounding codebase

Don't review changes in isolation. Before any modifications:

1. Read the full files containing changes — not just the diff hunks. Understand imports, surrounding logic, module structure.
2. Identify the project language(s) and select the relevant transformation rules. Discard inapplicable rules.
3. Survey adjacent code — shared utilities, sibling modules, common patterns. You need to know what already exists before deciding something is missing.
4. Run the project's gate once before editing to establish a baseline. Use the gate the project's CLAUDE.md / AGENTS.md names; absent one, take it from package.json scripts — devcheck if present, else check, else the separate typecheck / lint scripts — or a Makefile check target. Python projects gate on uv run ruff check, uv run ruff format --check, and the configured type checker. Add the test suite when the gate doesn't run it — read the script rather than assume (a devcheck often stops at lint and typecheck); without tests, nothing shows behavior survived the pass. In a Bun project that tests with Vitest, run bun run test — bare bun test bypasses the script and runs Bun's own runner. If the gate is already red, say so in the summary and don't attribute the failure to your changes.

Phase 3: Review

Evaluate the changes across these dimensions. Not every dimension applies to every diff — skip what's irrelevant.

Codebase cohesion

• Reuse — Search for existing utilities, helpers, and patterns that could replace newly-written code. Check utility directories, shared modules, and files adjacent to the changed ones. If a function already exists that does what the new code does, use it.
• Consolidation — Flag copy-paste-with-variation: near-duplicate code blocks that should be unified. Only unify if the shared abstraction is genuinely simpler than the duplicated code.
• Consistency — Check that new code follows the same patterns as the rest of the codebase: naming conventions, error handling style, import patterns, type annotation style. Normalize toward the better variant when the project is inconsistent.
• Stringly-typed code — Flag raw strings where constants, string-union types, or branded types already exist in the codebase.

Code quality

• Redundant state — State that duplicates existing state, cached values that could be derived.
• Unnecessary complexity — Deep nesting that could be guard clauses, premature abstractions, over-engineered solutions to simple problems.
• Speculative generality — Options, parameters, config flags, generic type parameters, and branches that no caller exercises. Flexibility for a hypothetical caller is cost paid now: remove it, and let the first real use add it back. On a published package's public surface it is API — note it instead (see Dead code).
• Pass-through layers — Apply the deletion test to a wrapper, helper, or module: if deleting it and inlining its body makes the complexity vanish, it was a pass-through — inline it. If the same logic would reappear across several callers, it earns its keep. An interface, port, or injected dependency with a single implementation and no test double is a hypothetical seam, not a real one — collapse it until something actually varies across it.
• Test-only reach — A function extracted or exported only so a test can get at it is a shape problem, not a cleanup: name it in the summary with the module it belongs to. Don't restructure it here — the tests would have to move with it.
• Dead code — Unreachable branches, unused variables, commented-out code, and debug leftovers from the session (console.log, print, debugger) that aren't the program's real output or the project's logger. An export nothing imports is dead in an application or a package-internal module; on a published package's public surface it is API — leave it and note it in the summary.
• Defensive code for impossible states — Guards for cases the type system or upstream validation already prevents. Drop them.
• Type escapes — any, as casts that paper over a mismatch, non-null !, @ts-ignore, and Python's # type: ignore / cast(). Each is a claim the compiler couldn't check: replace with a narrowed type, a type guard, or a parse at the boundary. Keep the ones documenting a genuine type-system or third-party-types limitation — confirm the limitation is gone before removing one — and prefer @ts-expect-error with a one-line reason over @ts-ignore.
• Swallowed errors — Empty catch {}, catch { return null }, and try blocks that log and continue. A fallback that hides a failure is worse than the crash it prevents: rethrow or let it propagate. When wrapping, preserve the chain (new Error(msg, { cause }), raise X from err).
• Masking defaults — ?? '', || [], ?? 0, .get(key, {}) standing in for a value that must exist. The default turns a missing config key or a broken upstream into quietly wrong output further down. When the type already rules out absence, the default is dead — drop it; when the value is optional in the type but required in fact, replace the default with an error that names what's missing, where the value is read. Keep defaults only where absence is a legitimate, expected state.
• Comment noise — Strip comments that restate the code and comments describing behavior the diff removed. Keep file headers, export JSDoc, and any comment carrying a why — a constraint, a workaround, an upstream bug reference.
• Outdated patterns — Verbose or legacy syntax where modern equivalents exist. See the transformation tables below.

Efficiency

• Redundant work — Repeated computations, duplicate file reads, duplicate network/API calls, N+1 query patterns.
• Missed concurrency — Independent async operations run sequentially that could run in parallel with Promise.all / Promise.allSettled.
• Unbounded fan-out — Promise.all / asyncio.gather over a caller-sized or otherwise unbounded array fires everything at once. Cap it with the project's existing concurrency helper or a batched loop. A fixed handful of independent calls needs no limit.
• No-op updates — State/store updates inside loops or event handlers that fire unconditionally. Add change-detection so downstream consumers aren't notified when nothing changed.
• TOCTOU — Pre-checking file/resource existence before operating on it. Operate directly and handle the error instead.
• Overly broad operations — Reading entire files when only a portion is needed, loading all items when filtering for one.

mcp-ts-core-specific

• Gate — bun run devcheck plus the test suite (bun run test) is the project gate in Phase 2 step 4 and Phase 4 step 5.
• Framework-provided utilities — Before hand-rolling, check src/utils/ and src/errors/ in the project and node_modules/@cyanheads/mcp-ts-core/ for framework exports: pagination helpers, schema builders, retry primitives, and the ATTR_* OTel attribute constants are framework-provided. Raw OTel attribute keys should be ATTR_* imports from @cyanheads/mcp-ts-core/utils.
• Error throwing patterns — Prefer framework error factories (McpError, validationError, notFound, httpErrorFromResponse) over raw throw new Error(). Tool handlers should throw — the framework catches, classifies, and instruments.
• Error codes — InvalidParams for input the schema rejects (the framework raises it before the handler runs) and for an upstream 400 mapped by httpErrorFromResponse. ValidationError for a business rule the schema cannot express. NotFound for missing entities. Don't conflate them.
• Ctx usage — Use ctx.log, ctx.state, ctx.enrich — don't reach for global loggers or request-scoped storage directly. The ctx pattern carries tenant scope and OTel context.
• Zod schemas — Every tool input/output field needs .describe(). Zod 4 requires z.record(z.string(), z.string()) not z.record(z.string()). Use .optional() rather than .nullish() unless null is semantically distinct from absent.
• Tool annotations — readOnlyHint, idempotentHint, openWorldHint should reflect reality. A read-only tool with readOnlyHint: false gives clients the wrong picture.
• exactOptionalPropertyTypes boundaries — If a downstream type insists on the field being present-or-not-present (not present-as-undefined), use a mapped widening type at the boundary. The pattern is documented in the framework.
• format() ↔ structuredContent parity — Different MCP clients forward different surfaces. Tests should assert both surfaces carry equivalent data.
• Framework layering is not a pass-through — the init/accessor pair (initFooService() / getFooService()), the tool definition → service split, and a provider interface the framework selects by config are prescribed convention; the deletion test doesn't apply to them, and a single-implementation service accessor is the framework's seam, not a hypothetical one.
• Defensive code — the "impossible states" the framework already prevents include malformed params (Zod-validated before the handler runs) and unclassified errors (caught and classified after it throws). Guards for either are dead.
• Public surface — the MCP surface (every tool input/output schema advertised to clients) is public API for the "API compatibility" rule; changing one is a breaking change, not a refactor.

Phase 4: Apply transformations

1. Filter findings ruthlessly. If a finding is a false positive or not worth the churn, skip it. Don't argue with yourself about borderline cases — move on.
2. Stay in scope. Edit only files inside the Phase 1 scope — the diff plus files new this session, or the named target. Touch a file outside that set only when a finding requires it — importing an existing helper, deleting a private export the diff just orphaned — and only on the lines that finding names. Anything broader goes in the summary as a recommendation, not into the tree.
3. Correctness bugs are not this pass's job. A real defect doesn't get folded into a cleanup diff — name it in the summary with file and line so it can be handled as its own change.
4. Transform incrementally — one category of change at a time (modernize syntax, then reduce nesting, then consolidate).
5. Verify equivalence — all functionality, types, and public interfaces must remain unchanged. Re-run the gate from Phase 2 after transforming; a simplification that breaks the build is worse than the verbosity it removed.
6. Keep the diff minimal. Only touch lines that have a real reason to change. Don't reformat untouched code, add comments to code you didn't modify, or "improve" things that are already fine. Formatting belongs to the formatter (Biome, ruff): never hand-adjust whitespace, quotes, or import order. Hunks the project's formatter writes during a gate run stay, even outside the scope — reverting them only fights the next run; mention them in the summary.
7. Never stage, commit, tag, push, or stash. This pass ends with a dirty working tree and a summary; landing the changes is the caller's call. A stash hides the very changes under review — compare against the baseline with git diff, never by setting work aside.

When done, report:

• Gate — the result before and after the pass, so a failure that predates the cleanup isn't pinned on it.
• Fixed — what changed, grouped by category.
• Skipped — findings deliberately left, each with its reason.
• Defects and recommendations — correctness bugs and out-of-scope changes, each with file:line.

When nothing earned a change, say the code was already clean.

Common transformations

The tables below cover TypeScript and Python. For other languages, apply analogous principles: prefer modern idioms, reduce nesting, eliminate dead code, follow project conventions. Check the project's language floor (tsconfig target/lib, pyproject requires-python) before applying a version-gated row.

TypeScript (modern ESM)

Before	After	Why
const x: Foo = { ... } as Foo	const x = { ... } satisfies Foo	Type-checked without assertion
let resource = acquire(); try { ... } finally { release(resource) }	using resource = acquire()	Explicit resource management (TS 5.2+) — only when the resource implements Symbol.dispose (await using for Symbol.asyncDispose); otherwise the try/finally stays
if (x !== null && x !== undefined)	if (x != null)	Idiomatic null/undefined check
arr.filter(x => x !== null) as T[]	arr.filter(x => x != null)	TS 5.5+ infers the type predicate — no cast; on older TS use an explicit (x): x is T predicate
import { foo } from './index.js' (a module importing through its own barrel)	import { foo } from './foo.js'	Inside a module, import siblings directly — routing through the module's own barrel invites import cycles. Across modules, the public barrel is the right entry point; leave those imports alone
import { readFile } from 'fs/promises'	import { readFile } from 'node:fs/promises'	node: protocol — unambiguous, lint-enforced in Biome
async function f() { const a = await x(); const b = await y(); }	const [a, b] = await Promise.all([x(), y()])	Parallel when independent
value || fallback	value ?? fallback	|| also swallows 0, '', and false — use ?? unless every falsy value really should take the fallback
obj.x !== undefined ? obj.x : fallback	obj.x ?? fallback	Nullish coalescing — equivalent only when null should take the fallback too
if (a) { if (b) { if (c) { ... } } }	Guard clauses with early returns	Reduce nesting
try { risky() } catch (e: any) { ... }	try { risky() } catch (e) { ... }	Under strict the catch binding is already unknown; narrow with a type guard before use
catch (err) { throw new Error('load failed') }	throw new Error('load failed', { cause: err })	Preserve the cause chain
[...arr].sort(cmp) / arr.slice().sort(cmp)	arr.toSorted(cmp)	Non-mutating array methods (ES2023) — also toReversed, toSpliced, with
arr[arr.length - 1]	arr.at(-1)	ES2022 — typed T | undefined: equivalent under noUncheckedIndexedAccess, a new undefined to handle otherwise
arr.reduce((acc, x) => { (acc[key(x)] ??= []).push(x); return acc }, {})	Object.groupBy(arr, key)	ES2024 — returns a null-prototype object whose values are typed T[] | undefined; Map.groupBy for non-string keys
let resolve!: (v: T) => void; const p = new Promise<T>((r) => { resolve = r })	const { promise, resolve, reject } = Promise.withResolvers<T>()	ES2024 — the deferred without the captured-variable dance
new Set([...a].filter((x) => b.has(x)))	a.intersection(b)	ES2025 Set methods — also union, difference, symmetricDifference, isSubsetOf; the receiver must be a Set
const c = new AbortController(); setTimeout(() => c.abort(), ms)	AbortSignal.timeout(ms)	Built-in timeout signal; combine with a caller's signal via AbortSignal.any([...])
JSON.parse(JSON.stringify(x))	structuredClone(x)	Deep clone that keeps Date, Map, Set, and cycles. Not a drop-in: it throws on functions and strips class prototypes, and Dates stay Dates instead of becoming strings
enum Status { A, B, C }	const Status = { A: 'A', B: 'B', C: 'C' } as const	enum, namespace, and constructor parameter properties are non-erasable syntax rejected by TS 5.8 erasableSyntaxOnly and Node type-stripping — but switching numeric values to strings changes serialized output; keep values stable if they're persisted
function f(a: string, b: string, c: string, d?: string)	function f(opts: FnOptions)	Internal functions whose same-typed positional params can be swapped and still type-check. An exported signature is API — leave it
throw new Error('Bad input') (in a tool handler)	throw validationError('Bad input', { field: 'x' })	Use framework error factories so the framework can classify and instrument
const ATTR_KEY = 'mcp.tool.name'	import { ATTR_MCP_TOOL_NAME } from '@cyanheads/mcp-ts-core/utils'	Use framework attribute constants

Python (3.12+)

Before	After	Why
Optional[str]	str | None	Modern union syntax (3.10+)
List[str], Dict[str, int]	list[str], dict[str, int]	Built-in generics (3.9+)
T = TypeVar("T") + def f(x: T) -> T	def f[T](x: T) -> T	PEP 695 generics (3.12+) — also class C[T]:
TypeAlias = Union[A, B, C]	type ABC = A | B | C	type statement (3.12+)
if isinstance(x, Foo): a = x.a; b = x.b	match x: case Foo(a=a, b=b): ...	Structural pattern matching (3.10+) where it destructures — not as a replacement for a flat equality if/elif chain
class Config: def __init__(self, a, b, c): self.a = a ...	@dataclass(slots=True) class Config: a: str; b: int; c: float	Less boilerplate, built-in eq/repr; frozen=True when instances shouldn't mutate
results = []; for item in items: results.append(transform(item))	results = [transform(item) for item in items]	Idiomatic comprehension
[items[i:i + n] for i in range(0, len(items), n)]	itertools.batched(items, n)	3.12+ — works on any iterable, not just sequences; yields tuples, not lists
f = open('x'); try: ... finally: f.close()	with open('x') as f: ...	Context manager for resources
os.path.join(d, n), os.path.exists(p), open(p).read()	Path(d) / n, p.exists(), p.read_text()	pathlib over os.path string juggling
datetime.utcnow() / datetime.utcfromtimestamp(t)	datetime.now(UTC) / datetime.fromtimestamp(t, UTC)	Deprecated in 3.12 — the old calls return naive datetimes that compare wrong against aware ones
zip(a, b) where the inputs must match in length	zip(a, b, strict=True)	3.10+ — a mismatch raises instead of silently truncating; plain zip stays where truncation is intended
m = pattern.match(s) then if m: use(m)	if (m := pattern.match(s)): use(m)	Walrus operator where it removes a throwaway assignment
"Hello " + name + "!"	f"Hello {name}!"	f-string over concatenation
except Exception: pass / except Exception as e: log(e)	except SpecificError: with real handling, or no try at all	Catch only what you can handle; everything else propagates (see Swallowed errors)
from module import *	from module import specific_name	Explicit imports only
Sequential await for independent I/O	async with asyncio.TaskGroup() as tg: tg.create_task(a()); tg.create_task(b())	Structured concurrency (3.11+) — cancels siblings on failure and raises an ExceptionGroup; asyncio.gather(..., return_exceptions=True) stays correct when every result is wanted regardless of failures

When NOT to simplify

Leave code alone when:

• It works and is readable. "I would have written it differently" is not a reason to change it.
• The change is cosmetic. Renaming a variable from data to result isn't worth the churn.
• Intentional verbosity for debugging. Verbose code may exist to make stack traces or logging clearer.
• Performance-critical paths. A less readable version may exist for measured performance reasons — check before simplifying.
• API compatibility. Don't change public function signatures, export shapes, or return types that callers depend on.
• Tests. Don't DRY up test code aggressively — test readability and isolation matter more than deduplication.
• The abstraction isn't proven. Don't create a shared utility for two similar blocks of code. Wait until there are three, and even then only if the abstraction is genuinely simpler than the duplication.
• return await inside try / finally. Collapsing it to return is not equivalent — the promise settles outside the block, so catch never fires and finally runs early. Only strip await from a return in plain function-body position.
• Lazy logging arguments. logger.info("loaded %s in %sms", name, ms) defers formatting until the record is emitted — don't turn it into an f-string.
• Awaits that only look independent. Sequential I/O may be sequential on purpose: rate limits, upstream ordering, a write that must land before the next read. Confirm independence from the code, not from the shape of the calls, before reaching for Promise.all.
• Generated and vendored files. Lockfiles, generated clients and schemas, migrations, snapshots, and anything under dist/ are regenerated, not edited — skip them even when they appear in the diff.
• Tool descriptions and .describe() prose. They are the contract an LLM client reads — tightening them for brevity degrades the surface. Treat them as API text, not as comments.