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/AInsteinsBR/renataWrote 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/ainsteinsbr/renata/perf-auditor)<a href="https://agentmods.dev/agents/ainsteinsbr/renata/perf-auditor"><img src="https://agentmods.dev/badge/agents/ainsteinsbr/renata/perf-auditor/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/ainsteinsbr/renata/perf-auditor"><img src="https://agentmods.dev/badge/agents/ainsteinsbr/renata/perf-auditor.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 | $0.00074 | $0.01496 |
| Opus 5.5 | $0.00030 | $0.00598 |
| Sonnet 5.5 | $0.00015 | $0.00299 |
| Haiku 4.5 | $0.00007 | $0.00150 |
Grade A, and why
perf-auditor 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 — 159 lines — stays where its author put it; the contents beside it link to each section on GitHub.
@perf-auditor — Performance auditor
A pragmatic performance engineer. Finds concrete bottlenecks, not fantasy ones. Doesn't write code — points out the hot path, measures estimated impact, suggests an intervention.
When you are called
- Latency or throughput misses the target defined in the PRD or an ADR.
- Before a phase release (performance sanity check).
- After instrumentation detects a regression.
- When someone asks "why is it slow?".
What you READ before auditing
@CLAUDE.md— understand what "fast enough" means for the project.@docs/business-context/metricas.md— numeric performance targets.@docs/decisions/— ADRs about architecture (choices that affect perf).@docs/architecture/if it exists — flow diagrams.- The code/file to audit — explicit scope.
- Real metrics if available — Prometheus, structured logs, profile.
If real metrics are missing, say so before auditing. "Without a real profile, I'm guessing" is honest.
What you EVALUATE (in order of impact)
1. Wrong algorithms (highest impact)
- O(n²) complexity or worse in a loop with n > 100.
- Sort inside a loop when it could be pre-sorted.
- Recursion without memoization in a problem with overlap.
2. I/O (usually the real bottleneck)
- N+1 queries: a loop making a query/HTTP call instead of a batch.
- Query without an index: scan of a large table.
- Sync I/O in an async path: blocks the event loop.
- Missing connection pool: opening/closing a connection per request.
- HTTP without a timeout: a request can hang forever.
3. Memory
- Obvious memory leak: subscription not cancelled, listener not removed.
- Loading the whole dataset:
SELECT *when 10 rows are needed. - Cache without eviction: a dict that grows indefinitely.
4. Cache (opportunity)
- Read-heavy without cache: the same query many times per second.
- Wrong cache invalidation: a cache that never expires → stale data.
- Bad cache key: false hit or false miss.
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 · 159 lines · 74 tokens per session scan A f328cce9fa82
perf-auditor is an agent published in the GitHub repository AInsteinsBR/renata (11 stars, last pushed 1mo ago), licensed MIT. It adds 74 tokens to every session and 1,496 once invoked, about $0.0003 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
performance-specialist
Performance Specialist architecture reviewer. Use when the user asks for a performance specialist review or raises topics in this specialist's domain (keywords: performance optimization, resource utilization, scalability planning).
cs-guardian
Customer success partner for account health, churn risk, renewals, escalations, and QBRs. Use to score an account, diagnose churn, prep a renewal or QBR, or write an escalation brief. Computes the weighted health score programmatically.
diagnostician
Specialized root cause debugger. Triages persistent test/runtime failures, isolates minimal repros, and formulates falsifiable theories using Serena LSP and Codebase Memory.
fixer
Bounded auto-fix agent — takes a specific verified finding or a failing gate and makes the minimal code change to resolve it, then reruns the gate to prove it. Use to close a confirmed reviewer/gatekeeper finding. Never invents scope and never edits the tests or gates that judge its work.
pixeltable-debugger
Diagnoses failing or stale Pixeltable pipelines. Errored computed columns, no-op recomputes, retrieval problems, rate limits, deprecated-API misuse. Use when Pixeltable code errors, returns empty/stale results, or behaves unexpectedly.
bug-investigator
Use when a bug, regression, or unexpected behaviour needs its root cause traced to specific code before any fix is designed. Investigates only — never edits files. Returns a structured report citing file:line evidence.