performance-reviewer

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.

Clone the repo

git clone --depth 1 https://github.com/Tenon-Net/TenonAdmin

Made for: Claude Code, Codex.

Wrote 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.

README.md

[![agentmods](https://agentmods.dev/badge/commands/tenon-net/tenonadmin/performance-reviewer/github.svg)](https://agentmods.dev/commands/tenon-net/tenonadmin/performance-reviewer)

Your own site

<a href="https://agentmods.dev/commands/tenon-net/tenonadmin/performance-reviewer"><img src="https://agentmods.dev/badge/commands/tenon-net/tenonadmin/performance-reviewer/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.

Your own site · 80×15

<a href="https://agentmods.dev/commands/tenon-net/tenonadmin/performance-reviewer"><img src="https://agentmods.dev/badge/commands/tenon-net/tenonadmin/performance-reviewer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 16 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.

When invoked 517 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.

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.

Model	Per session	Once invoked
Fable 5.1	$0.00016	$0.00517
Opus 5.5	$0.00006	$0.00207
Sonnet 5.5	$0.00003	$0.00103
Haiku 4.5	$0.00002	$0.00052

Measured today against content hash 48a3a7bc7ea8, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

performance-reviewer 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 today.

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.

.codex/prompts/performance-reviewer.md · 46 lines

How it starts

The opening of the file, as written. The whole thing — 46 lines — stays where its author put it; the contents beside it link to each section on GitHub.

<review_focus>

Identify code that runs frequently or processes large data sets.
Analyze time and space complexity, nested loops, repeated searches, sort-in-loop patterns, and unbounded work.
Check allocations in hot loops, object lifetimes, string construction, closure captures, serialization, and memory retention.
Check blocking I/O, N+1 queries, unbatched network calls, and unnecessary parsing or serialization.
Identify repeated computations and caching opportunities; review concurrency, contention, lock granularity, and safe parallelism.
Do not flag startup code unless it exceeds about one second, rare work below about once per minute and 100 ms, or a readable implementation where microseconds do not matter. </review_focus>

<severity_and_evidence>

Quantify complexity and expected impact; “slow” is not a finding. Include input size, frequency, latency or memory estimate, and confidence when possible.
Recommend profiling before optimization unless the defect is algorithmically obvious (for example, O(n²) in a hot loop).
Cite each concern with file:line, distinguish “measure first” from an obvious fix, and state when current performance is acceptable.
Prioritize findings by production impact rather than count of micro-optimizations. </severity_and_evidence>

<output_contract>