AgentStack
SKILL verified MIT Self-run

Code Simplifier

skill-yz5e-agent-skills-code-simplifier · by yz5e

>

No reviews yet
0 installs
16 views
0.0% view→install

Install

$ agentstack add skill-yz5e-agent-skills-code-simplifier

✓ scanned · ✓ verified — works with Claude Code, Cursor, and more.

Security review

✓ Passed

No issues found. Passed automated security review. · v0.1.0 How review works →

  • Prompt-injection patterns
  • Secret / credential exfiltration
  • Dangerous shell & filesystem operations
  • Untrusted network calls
  • Known-malicious package signatures

What it can access

  • Network access No
  • Filesystem access No
  • Shell / process execution No
  • Environment & secrets No
  • Dynamic code execution No

From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.

Are you the author of Code Simplifier? Claim this listing to set pricing, connect Stripe payouts, and keep 70% of every sale.
Sign up to claim

About

Code Simplifier

Simplify only the relevant changed code unless the user explicitly asks for a broader sweep. Preserve behavior, outputs, error semantics, side effects, and public interfaces.

Default to a single-agent simplification pass. Use spawn_agent only when the user explicitly asks for delegation, parallel subagents, or a multi-agent review and the touched scope is broad enough to benefit.

Workflow

  1. Identify the scope.
  • Prefer the files or hunks changed in the current task.
  • If the scope is unclear, inspect the named function, file, or recent diff.
  • Avoid unrelated cleanup outside the touched area.
  1. Read the project's local conventions.
  • Check AGENTS.md, nearby code, formatter and lint rules, and type patterns.
  • Match the repository's style instead of forcing a generic one.
  • Reuse existing helpers before introducing new abstractions or dependencies.
  1. Decide whether to delegate.
  • Keep the immediate blocking work local. Form a short plan before spawning anything.
  • Do not delegate small, tightly coupled, or urgent blocking work where your next step depends on the answer.
  • If the user explicitly asks for parallelism and the work is broad enough, use spawn_agent for bounded subtasks that can run in parallel with your local work.
  • Prefer narrow prompts and explicit file or symbol scope over copying the full thread context. Use fork_context only when the subagent truly needs the same conversation history.
  • Prefer explorer agents for read-heavy simplification analysis. Reserve worker agents for implementation only when the write scope can be split into disjoint files or modules.
  1. Review the code through three simplification lenses.
  • Reuse: prefer existing helpers, utilities, and local patterns before adding new abstractions; remove duplication when the shared shape is stable.
  • Quality: improve readability, naming, branching, cohesion, and type clarity; preserve logging, validation, and error semantics.
  • Efficiency: remove unnecessary work in the touched code such as redundant passes, allocations, awaits, or lookups when the improvement is obvious and behavior stays identical.
  • If the lenses conflict, prefer behavioral safety and readability over extra DRYing or micro-optimizations.
  1. Simplify with restraint.
  • Flatten unnecessary nesting and branching.
  • Remove duplication when the shared pattern is stable and improves readability.
  • Replace dense one-liners and nested ternaries with explicit control flow.
  • Prefer clear names, direct data flow, and small cohesive helpers.
  • Keep comments only when they explain non-obvious intent.
  • Preserve logging, validation, and error handling behavior.
  1. Verify before finishing.
  • Re-read the edited paths for behavior drift, async changes, type regressions, and altered edge cases.
  • Run the smallest relevant validation available when practical.
  • If a "simplification" would change behavior or conflict with project conventions, stop and explain the tradeoff instead of forcing it.

Subagent Strategy

  • Apply all three lenses inline by default.
  • If the user explicitly asks for subagents and the task is broad enough to benefit, split the review into bounded subtasks instead of asking multiple agents to rewrite the same hunk.
  • A strong default is up to three parallel explorer subagents: one for reuse opportunities, one for quality/readability issues, and one for obvious efficiency cleanup in the touched scope.
  • Keep the main agent focused on the core simplification: reviewing conventions, checking behavior-sensitive code paths, and preparing the final edit plan while the subagents run.
  • Ask each subagent for concrete findings with file or symbol references and a short rationale. Prefer recommendations over patches unless the write scopes are clearly disjoint.
  • Call wait_agent sparingly. Only wait when their result is needed for the next step, and do meaningful non-overlapping local work while they run.
  • Reuse an existing delegated thread with send_input when a follow-up stays on the same narrow problem; otherwise spawn a new bounded task.
  • Use worker subagents only when the user explicitly wants parallel implementation and each worker can own a disjoint write set. Give each worker clear file ownership and tell it not to revert edits made by others.
  • If you override the delegated model for a hard simplification review, prefer the current strongest available model for ambiguous or high-risk reasoning; as of 2026-04, prefer gpt-5.5 when available and fall back to gpt-5.4 when it is not.
  • Use gpt-5.4-mini only for fast read-heavy scans where latency or cost matters more than depth.
  • Let simple passes inherit the current model unless there is a clear reason to override.
  • Only merge recommendations that preserve behavior and improve the touched code without widening scope.
  • Treat efficiency as a restraint check, not a license for speculative performance work.

Guardrails

  • Do not change public APIs, storage formats, or user-visible text unless explicitly requested.
  • Do not widen a refactor just because more cleanup is possible.
  • Do not optimize for fewer lines at the expense of clarity.
  • Do not merge separate concerns into one helper or expression just to be concise.
  • Keep existing import, module, and file-organization conventions.
  • Do not spawn subagents just because the environment supports them. Use them only for clear, parallelizable subtasks.
  • Do not duplicate work between the main agent and subagents.
  • Do not ask multiple subagents to produce overlapping edits for the same code unless the user explicitly wants competing proposals.
  • Do not keep polling or waiting on subagents by reflex. If you are not blocked, keep working locally.

Good Triggers

  • Simplify this code
  • Clean up this implementation
  • Make this clearer without changing behavior
  • Polish the refactor
  • Reduce the complexity in this function
  • Review this refactor for reuse, quality, and efficiency
  • Review this refactor in parallel with subagents
  • Use subagents to scan this diff for reuse, quality, and efficiency

Expected Output

  • Make the code changes directly when the request is actionable.
  • Call out any uncertainty that could affect behavior before editing.
  • Summarize only the meaningful simplifications and any validation that was run.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

Install and usage instructions live in the source repository linked above.

Reviews

No reviews yet — be the first.

Versions

  • v0.1.0 Imported from the upstream source.