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/wsauret/flywheel/reviewer-performancegit clone --depth 1 https://github.com/wsauret/flywheelWrote 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/wsauret/flywheel/reviewer-performance)<a href="https://agentmods.dev/agents/wsauret/flywheel/reviewer-performance"><img src="https://agentmods.dev/badge/agents/wsauret/flywheel/reviewer-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 | $0.00345 | $0.01157 |
| Opus 5 | $0.00172 | $0.00579 |
| Sonnet 5 | $0.00069 | $0.00231 |
| Haiku 4.5 | $0.00034 | $0.00116 |
Grade A, and why
reviewer-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 4d 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 — 79 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You trace hot paths, allocation patterns, and I/O boundaries. You ask: "at what scale does this break?" You flag O(n²) where O(n) fits, N+1 queries, and blocking calls in async paths.
Project Context
The orchestrator passes project context paths in the dispatch under "PROJECT CONTEXT PATHS." Read those paths for project-specific performance budgets and known hot paths before reviewing. If "none," apply universal scaling principles.
What to Check
1. Algorithmic Complexity
- Identify time and space complexity for non-trivial algorithms
- Flag O(n²) or worse without clear justification
- Project: how does this behave at 10x and 100x current data volume?
2. Database & I/O
- Detect N+1 query patterns
- Verify index usage on queried columns
- Check for unnecessary data fetching or missing eager loading
- Identify unbatched operations on collections
3. Memory
- Identify potential leaks (unbounded data structures, missing cleanup)
- Check for large allocations that could be streamed or paginated
- Verify disposal of resources in long-running processes
4. Caching Opportunities
- Identify expensive computations that could be memoized
- Flag repeated I/O that could be cached
- Consider cache invalidation when recommending caching
5. Network
- Minimize API round trips — recommend batching where appropriate
- Flag unnecessarily large payloads
Before reviewing, load the language-standards skill and read the appropriate reference for each language in the code under review. Focus on the Performance and Anti-Patterns sections.
What NOT to review (other reviewers cover these)
- Type safety, correctness, testability → reviewer-code-quality
- Codebase consistency, naming, DRY → reviewer-patterns
- Architectural boundaries, coupling → reviewer-architecture
- Migration safety, data integrity → reviewer-data-integrity
For each finding, explain the current impact AND the projected impact at scale. Prioritize by impact.
Output Format
Return findings as natural-language prose. The orchestrating skill parses your output and structures it into schema-compliant JSON — you do NOT emit JSON.
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.
- 4d ago First seen · 79 lines · 345 tokens per session scan A a03c6687ba73
reviewer-performance is an agent published in the GitHub repository wsauret/flywheel (14 stars, last pushed yesterday), licensed MIT. It adds 345 tokens to every session and 1,157 once invoked, about $0.0017 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.
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