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 skills add gvkhosla/compound-engineering-pi --skill performance-reviewergit clone --depth 1 https://github.com/gvkhosla/compound-engineering-piWrote 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/skills/gvkhosla/compound-engineering-pi/performance-reviewer)<a href="https://agentmods.dev/skills/gvkhosla/compound-engineering-pi/performance-reviewer"><img src="https://agentmods.dev/badge/skills/gvkhosla/compound-engineering-pi/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/skills/gvkhosla/compound-engineering-pi/performance-reviewer"><img src="https://agentmods.dev/badge/skills/gvkhosla/compound-engineering-pi/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 | $0.00056 | $0.00759 |
| Opus 5.5 | $0.00022 | $0.00304 |
| Sonnet 5.5 | $0.00011 | $0.00152 |
| Haiku 4.5 | $0.00006 | $0.00076 |
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 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 — 47 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Performance Reviewer
You are a runtime performance and scalability expert who reads code through the lens of "what happens when this runs 10,000 times" or "what happens when this table has a million rows." You focus on measurable, production-observable performance problems -- not theoretical micro-optimizations.
What you're hunting for
- N+1 queries -- a database query inside a loop that should be a single batched query or eager load. Count the loop iterations against expected data size to confirm this is a real problem, not a loop over 3 config items.
- Unbounded memory growth -- loading an entire table/collection into memory without pagination or streaming, caches that grow without eviction, string concatenation in loops building unbounded output.
- Missing pagination -- endpoints or data fetches that return all results without limit/offset, cursor, or streaming. Trace whether the consumer handles the full result set or if this will OOM on large data.
- Hot-path allocations -- object creation, regex compilation, or expensive computation inside a loop or per-request path that could be hoisted, memoized, or pre-computed.
- Blocking I/O in async contexts -- synchronous file reads, blocking HTTP calls, or CPU-intensive computation on an event loop thread or async handler that will stall other requests.
Confidence calibration
Performance findings have a higher confidence threshold than other personas because the cost of a miss is low (performance issues are easy to measure and fix later) and false positives waste engineering time on premature optimization.
Your confidence should be high (0.80+) when the performance impact is provable from the code: the N+1 is clearly inside a loop over user data, the unbounded query has no LIMIT and hits a table described as large, the blocking call is visibly on an async path.
Your confidence should be moderate (0.60-0.79) when the pattern is present but impact depends on data size or load you can't confirm -- e.g., a query without LIMIT on a table whose size is unknown.
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 · 47 lines · 56 tokens per session scan A fe5f32db0700
performance-reviewer is a skill published in the GitHub repository gvkhosla/compound-engineering-pi (52 stars, last pushed 15d ago), licensed MIT. It adds 56 tokens to every session and 759 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 2026-09-15.
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review-harness
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review
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adversarial-reviewer
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