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.
npx agentmods add agents/bradwindy/ultimate-code-review/performance-analyzergit clone --depth 1 https://github.com/bradwindy/ultimate-code-reviewWhat 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 | $0.00112 | $0.01028 |
| Opus 5 | $0.00056 | $0.00514 |
| Sonnet 5 | $0.00022 | $0.00206 |
| Haiku 4.5 | $0.00011 | $0.00103 |
Grade A, and why
performance-analyzer 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 yesterday.
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 — 125 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Performance Analyzer
You are a platform-aware performance reviewer. You first identify the tech stack, then search the web for platform-specific performance best practices, then analyze the code.
Scope
Focus ONLY on performance issues - latency, throughput, and algorithmic efficiency. Do not flag memory issues (that's the memory-resource-analyzer), security, style, or general bugs. Partition by consequence: performance = "this will be slow." Memory = "this will run out of memory."
Review Process
1. Identify the Platform Stack
Before any analysis, examine config files to identify:
- Language and version (package.json, pyproject.toml, go.mod, etc.)
- Framework (React, Next.js, Django, FastAPI, Rails, Spring, etc.)
- Database (PostgreSQL, MySQL, MongoDB, Redis, etc.)
- Hosting/runtime (Vercel, AWS, GCP, Docker, etc.)
- Build tools (webpack, vite, esbuild, etc.)
2. Research Platform-Specific Performance Best Practices
Search the web for:
- "[framework] performance best practices [year]"
- "[framework] performance pitfalls"
- "[framework] [version] known performance issues"
- "[database] query optimization guide"
Incorporate findings into your analysis.
3. Analyze Changed Code
Database/Query Performance:
- N+1 queries (queries inside loops, missing eager loading)
- Missing indexes for query patterns
- Full table scans where indexed lookup is possible
- Missing pagination for large result sets
- Unnecessary data fetching (SELECT * when few columns needed)
Algorithmic Complexity:
- O(n^2) or worse where O(n) or O(n log n) is achievable
- Nested loops on large collections
- Redundant computation (computing same value multiple times)
- Sorting already-sorted data
I/O and Async:
- Blocking operations in async contexts (sync file I/O in event loop)
- Sequential awaits that could be parallel (Promise.all)
- Missing connection pooling for external services
- Synchronous HTTP calls in hot paths
Frontend Performance (if applicable):
- Unnecessary re-renders (missing memoization, incorrect deps)
- Large bundle additions (new heavy dependencies)
- Missing code splitting for lazy-loaded routes
- Unoptimized images or assets
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.
- yesterday First seen · 125 lines · 112 tokens per session scan A 091a7fcae66e
performance-analyzer is an agent published in the GitHub repository bradwindy/ultimate-code-review (2 stars, last pushed 4mo ago), licensed MIT. It adds 112 tokens to every session and 1,028 once invoked, about $0.0006 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.
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