optimize

An analysis of code that looks for performance bottlenecks and gives prioritized recommendations with before-and-after examples. A performance bottleneck is a part of a program that makes it slower or uses more resources than necessary.

In plain words
What is it for?
Use it before shipping performance-sensitive features or when an application feels slow. It examines algorithm complexity, database access, input/output, and related code patterns.
Why use it?
Slow code is often caused by repeated work, inefficient algorithms, excessive database queries, or unnecessary input and output. The analysis explains the cost of each problem and how to improve it.

Skill for Claude CodeCodex

Install

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.

agentmods
npx agentmods add skills/viknesh20-20/claude-code-tool-kit/optimize
Any agent
npx skills add viknesh20-20/claude-code-tool-kit --skill optimize
Clone the repo
git clone --depth 1 https://github.com/viknesh20-20/claude-code-tool-kit

Made for: Claude Code, Codex.

Per session 36 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 803 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00036 $0.00803
Opus 5 $0.00018 $0.00402
Sonnet 5 $0.00007 $0.00161
Haiku 4.5 $0.00004 $0.00080

Measured 2d ago against content hash 6e5c1a67eed8, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

optimize 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 2d 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.

.claude/skills/optimize/SKILL.md · 90 lines

How it starts

The opening of the file, as written. The whole thing — 90 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Performance Optimization Analysis

Project Context

!ls package.json requirements.txt go.mod Cargo.toml *.csproj pom.xml 2>/dev/null !wc -l $(find . -type f \( -name "*.ts" -o -name "*.js" -o -name "*.py" -o -name "*.go" -o -name "*.rs" -o -name "*.java" -o -name "*.cs" -o -name "*.rb" \) -not -path "*/node_modules/*" -not -path "*/.git/*" -not -path "*/vendor/*" 2>/dev/null | head -5) 2>/dev/null | tail -1


Analysis Categories

Examine the specified code for issues in each category. For every issue found:

  1. Show the problematic code snippet (with file:line)
  2. Explain why it's a problem with complexity analysis
  3. Show the optimized version
  4. Rate impact: High / Medium / Low

1. Algorithmic Complexity

  • O(n²) or worse loops that can be reduced
  • Redundant iterations over the same data
  • Sorting in loops (sort once, iterate many)
  • Nested loops that can be replaced with hash maps/sets
  • Unnecessary full-collection scans when early exit is possible

2. Database & I/O

  • N+1 query patterns (loop of individual queries instead of batch)
  • Missing database indexes for frequent queries
  • Unbounded queries without LIMIT/pagination
  • Sequential I/O that could be parallelized
  • Missing connection pooling
  • Redundant file reads (read once, use many)

3. Memory & Allocation

  • Large object copies that could use references/pointers
  • String concatenation in loops (use builder/buffer)
  • Accumulating data in memory that could be streamed
  • Memory leaks: event listeners not removed, subscriptions not cancelled
  • Caching opportunities for expensive computations

4. Concurrency & Async

  • Sequential await calls that could use Promise.all / asyncio.gather / goroutines
  • Blocking the main thread/event loop with CPU-heavy work
  • Missing cancellation for abandoned async operations
  • Thread-safety issues with shared mutable state

5. Language-Specific

JavaScript/TypeScript:

  • Unnecessary re-renders (missing React.memo, useMemo, useCallback)
  • Barrel imports pulling in entire modules
  • Missing tree-shaking opportunities
  • Synchronous operations that should be async

Read the full file on GitHub · 90 lines

Changes

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.

  1. 2d ago First seen · 90 lines · 36 tokens per session scan A 6e5c1a67eed8

Subscribe to this mod's changes

optimize is a skill published in the GitHub repository viknesh20-20/claude-code-tool-kit (7 stars, last pushed 4mo ago), licensed MIT. It adds 36 tokens to every session and 803 once invoked, about $0.0002 per session on Opus 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.

Related

Other skills, from other repositories

feishu

Work with Feishu or Lark bots, docs, sheets, bitables, approval flows, and OpenAPI/MCP setup without hardcoding credentials.

Hmbown/CodeWhale · 33 tokens

interview

Ask one useful structured question at a time only when material product/implementation choices are genuinely missing; remember answers and produce a brief/spec. Discoverable facts should be investigated instead of asked.

Hmbown/CodeWhale · 40 tokens

local-frontend-check

Smoke-test or verify UI behaviour on the local Jarvis Registry frontend running at http://localhost/gateway. Use for manual regression checks, bug-fix verification, and end-to-end confirmation of specific flows without running the automated test suite.

ascending-llc/jarvis-registry · 52 tokens

ppt-review

Read this skill only after current officereview structural output and relevant rendered images exist. Do not use source code, a tool success flag, or the first-slide preview as a substitute for deck evidence.

Orkas-AI/Orkas · 2 tokens

helmor-debug-operate

Operate, reproduce, and debug a running local Helmor desktop development build through the Tauri MCP bridge. Use when the user asks to use Tauri MCP, towery MCP, the local dev build, the Tauri webview, visual end-to-end validation, UI automation, screenshots, DOM/accessibility snapshots, IPC or log tracing…

dohooo/helmor · 117 tokens

autoprompt

Explicit-only useful-first orchestration. Invoke /autoprompt to turn a mission into one executable roadmap, build dependency-safe lanes, and verify the result with independent reviewers. Never infer invocation from ordinary requests. Never resume from leftover artifacts without an explicit resume instruction.

Spielewoy/autoprompt-skill · 56 tokens