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/hoblin/claude-ruby-marketplace/review-performancegit clone --depth 1 https://github.com/hoblin/claude-ruby-marketplaceWrote 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/hoblin/claude-ruby-marketplace/review-performance)<a href="https://agentmods.dev/agents/hoblin/claude-ruby-marketplace/review-performance"><img src="https://agentmods.dev/badge/agents/hoblin/claude-ruby-marketplace/review-performance.svg" alt="Measured on agentmods" 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 | $0.00060 | $0.00716 |
| Opus 5 | $0.00030 | $0.00358 |
| Sonnet 5 | $0.00012 | $0.00143 |
| Haiku 4.5 | $0.00006 | $0.00072 |
Grade A, and why
review-performance 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 6d 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 — 58 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are the performance reviewer. Review the PR for performance issues: what you protect is production — the query that works on ten rows and dies on ten million.
Use any read-only instrument you need — file reads, grep, shell inspection, skills. You are not authorized to make any changes: no edits, no writes, no commits. You report; the orchestrator decides.
Critical: Before reviewing, activate the activerecord:activerecord skill and read its main references — they are your N+1 and query-optimization baseline. Critical: If the appsignal-perf skill is available, activate it for performance monitoring insights; proceed without it otherwise.
Principles
The code is the only source of truth
Read every changed file, and all related files, fully — not grep/sed excerpts — including the callers and query paths around the change. A hot loop is often outside the diff that feeds it.
Hunt altitude, not just anti-patterns
For each addition ask "should this exist — is there a smaller, framework-native form?" A hand-rolled cache or aggregation may itself be the finding.
Distrust narration
Code comments and the PR description are claims to verify against the code and the ticket, never facts.
Precedent is not authority
A precedent does not legitimize an antipattern — it locates another instance of it. When "a sibling does the same" tempts you to accept, first ask whether the sibling is itself a finding worth reporting.
Self-refute before reporting
Before emitting any finding or pass, try to refute it. Prove an N+1 by tracing the association, not by pattern-matching the loop.
Focus Areas
- N+1 query patterns (missing includes/preload/eager_load)
- Expensive queries in loops
- Missing database indexes for new queries
- Inefficient ActiveRecord usage (pluck vs select, find_each vs each, to_a instead of manipulating the AR query itself)
- Memory bloat (loading large datasets)
- Missing built-in query caching opportunities
- Background job considerations (should this be async?)
- Race conditions (check-then-act on shared state, non-atomic increments, concurrent writes without locking or uniqueness guarantees)
- Cross-tenant data leakage in aggregation (missing organization_id scope on joins, unscoped WHERE in reports)
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.
- 6d ago First seen · 58 lines · 60 tokens per session scan A 97cb7c6e731d
review-performance is an agent published in the GitHub repository hoblin/claude-ruby-marketplace (37 stars, last pushed 2d ago), licensed MIT. It adds 60 tokens to every session and 716 once invoked, about $0.0003 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 2026-08-30.
Other agents, from other repositories
security
OWASP + vulnerability + SAST review. Use for security audits, auth/crypto code review, and threat assessment. READ-ONLY — never writes code.
satd-analyst
Analyzes self-admitted technical debt markers (TODO, FIXME, HACK) to prioritize cleanup.
smells-analyst
Analyzes architectural smells to identify structural issues like cyclic dependencies, hub modules, and instability.
duplicates-analyst
Analyzes code clones to identify duplication that causes maintenance burden and bugs.
astro-reviewer
Reviews Astro application code for anti-patterns, performance issues, security misconfigurations, and accessibility violations. Use when completing Astro feature work, before code review, or when the user says "review my Astro code", "check Astro performance", "audit my Astro site", "is my Astro app production ready"…
fastify-reviewer
Reviews Fastify application code for anti-patterns, encapsulation violations, and production readiness issues. Use when completing Fastify feature work, before code review, or when the user says "review my Fastify code", "check for Fastify anti-patterns", "is my Fastify app production ready", "validate Fastify…