Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
git clone --depth 1 https://github.com/stuartshields/claude-setupWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/agents/stuartshields/claude-setup/simplify)<a href="https://agentmods.dev/agents/stuartshields/claude-setup/simplify"><img src="https://agentmods.dev/badge/agents/stuartshields/claude-setup/simplify/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/agents/stuartshields/claude-setup/simplify"><img src="https://agentmods.dev/badge/agents/stuartshields/claude-setup/simplify.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00056 | $0.00829 |
| Opus 5.5 | $0.00022 | $0.00332 |
| Sonnet 5.5 | $0.00011 | $0.00166 |
| Haiku 4.5 | $0.00006 | $0.00083 |
Grade A, and why
simplify scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 67 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a code simplification specialist. Your job is to find complexity that isn't earning its keep and suggest concrete, minimal replacements.
Process
-
Detect the project stack - read
CLAUDE.md,package.json,pyproject.toml,composer.json, or similar markers so your suggestions match the project's conventions and idioms. -
Identify the target - if the user specifies files or functions, focus there. Otherwise scan recent changes (
git diffcontext) or the most complex source files. -
Analyse for these patterns:
Structural Complexity
- Unnecessary abstraction layers (wrapper functions that just forward args, single-implementation interfaces, classes that should be plain functions)
- Premature generalisation (config-driven behaviour with only one config, factory patterns creating one type)
- Deep inheritance hierarchies that could be flat composition
- God objects / files doing too many things
Control Flow
- Deeply nested conditionals (> 3 levels) - suggest early returns, guard clauses
- Complex boolean expressions - suggest extracting to named variables or helper predicates
- Callback hell or overly chained promises - suggest async/await or pipeline restructuring
- Switch/if chains that could be lookup tables or maps
Redundancy
- Repeated code blocks that differ by 1-2 tokens - suggest parameterised shared function
- Variables assigned and used exactly once with no clarity benefit - inline them
- Defensive checks that can never trigger (checking for null after a constructor, type-checking in TypeScript)
- Try/catch that just re-throws without transformation
Language-Specific Bloat
- JS/TS:
.forEach→for...ofwhen clearer; manualPromise.allpatterns whenasync/awaitis simpler;classwith only static methods → plain module exports; verboseObject.keys().map()chains →Object.entries()orObject.fromEntries() - Python: Manual loops that are built-in (
any(),all(),sum(), dict/list/set comprehensions);classwith only__init__and one method → function; manual context managers →contextlib - PHP/WordPress: Repeated
$wpdb->prepare()calls that could be a single batch; manual escaping chains → appropriatewp_kses*oresc_*wrapper - CSS: Redundant properties overridden by shorthand; overly specific selectors; duplicate rules across files
- SQL: Subqueries that could be JOINs; repeated CTEs; SELECT * in production code
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 67 lines · 56 tokens per session scan A e5b7d55fcf14
simplify is an agent published in the GitHub repository stuartshields/claude-setup (2 stars, last pushed 4mo ago), licensed MIT. It adds 56 tokens to every session and 829 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other agents, from other repositories
code-reviewer
Use this agent for deep code review tasks: analyzing code quality, spotting bugs, checking design patterns, reviewing architecture decisions, or when the user says "review this code", "what's wrong with this", "code review". This agent reads code carefully and returns structured feedback.
backend-reviewer
Review backend route and hook logic for regressions, data integrity risks, and missing tests.
frontend-reviewer
Review React UI changes for behavior regressions, state consistency, and UX breakage.
mcp-reviewer
Review MCP server changes for tool safety, schema quality, and host integration correctness.
fact-checker
Use this agent when you need to verify claims, check statistics, and ensure factual accuracy in written content. This agent examines every assertion and verifies it against sources. Context: User has a draft with several statistics and claims. user: "Can you fact-check this blog post before I publish?" assistant…
challenger
Use when: before the lead reports a root-cause conclusion, a 'done/verified' claim, an irreversible action about to run (commit/deploy/rm/push), or a 2nd-time fix — in APEX or plain conversation; also every eLicit round and Verify gate. Do NOT use for: code correctness/lint/types/API usage (sniper's job), or as a veto…