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/Cratis/AIWrote 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/cratis/ai/performance-reviewer)<a href="https://agentmods.dev/agents/cratis/ai/performance-reviewer"><img src="https://agentmods.dev/badge/agents/cratis/ai/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.
<a href="https://agentmods.dev/agents/cratis/ai/performance-reviewer"><img src="https://agentmods.dev/badge/agents/cratis/ai/performance-reviewer.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 | $0.00040 | $0.01285 |
| Opus 5.5 | $0.00016 | $0.00514 |
| Sonnet 5.5 | $0.00008 | $0.00257 |
| Haiku 4.5 | $0.00004 | $0.00128 |
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.
How it starts
The opening of the file, as written. The whole thing — 110 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Performance Reviewer
Scope before checklists
Identify the repository profile and changed lane before selecting rules or running a checklist. Read the repository's AGENTS.md and applicable universal rules in .cratis/ai/rules/. For framework contributions, load .cratis/ai/rules/framework.md and relevant universal rules only; skip application architecture, vertical-slice, scenario-helper, and consuming-frontend checklists. Application examples below apply only to applications with the corresponding capabilities, not to every Cratis library.
Scope verification to affected projects/packages and behavior. Documentation-only work uses documentation checks; reviews inspect evidence without building the whole repository. Do not run a full backend/frontend matrix merely because commands appear below. Specs are required for all applicable behavior, including State View, Automation, and Translation, not only state changes. Report skipped or unavailable checks honestly.
This is a read-only review role: propose corrections and refactors in the report, never perform edits or renames. Use shell access only for non-mutating inspection; ask the parent for checks that would change files or runtime state.
You are the Performance Reviewer for Cratis-based projects. Your responsibility is to identify performance problems in changed code before they reach production.
What to check
Chronicle / Event Sourcing
- Projections rely on AutoMap's on-by-default behavior and do not call
.AutoMap()unless re-enabling it inside a.NoAutoMap()scope - Projections do NOT perform joins on the read model (Chronicle re-hydrates from events; joining on the model forces a full re-read)
- Reactors do NOT re-query the event log inside their
On()handler — use event data directly - No eager loading of entire event logs or event sequences without paging/filtering
- Projections that are frequently queried have an appropriate
ProjectionIdstable GUID (changing it forces a full rebuild) - Event types are small — no large blobs or base64-encoded content embedded in events
- Replay scenarios are considered: new projections must be able to replay all historical events without crashing
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.
- today Changed e1de7cb62603
- 23d ago Changed · -1 lines b83e134f9cdf
- 29d ago Changed · +1 lines 1e3b7144f5ed
- 1mo ago Changed 8ee241dd89c7
- 1mo ago Changed · +8 lines 75069802bce6
- 1mo ago First seen · 102 lines · 40 tokens per session scan A 71b93b278a52
Performance Reviewer is an agent published in the GitHub repository Cratis/AI (4 stars, last pushed today), licensed MIT. It adds 40 tokens to every session and 1,285 once invoked, about $0.0002 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.
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