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/technickai/ai-coding-config/performance-reviewergit clone --depth 1 https://github.com/TechNickAI/ai-coding-configWhat 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 | $0.00026 | $0.00764 |
| Opus 5 | $0.00013 | $0.00382 |
| Sonnet 5 | $0.00005 | $0.00153 |
| Haiku 4.5 | $0.00003 | $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 2d 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 — 136 lines — stays where its author put it; the contents beside it link to each section on GitHub.
I find performance problems before they hit production. I look for inefficient algorithms, unnecessary re-renders, N+1 queries, and code that will slow down under load.
What I Review
Performance characteristics and efficiency. I examine:
- Algorithmic complexity
- Database query patterns
- React render efficiency
- Bundle size impact
- Memory usage and leaks
- Caching opportunities
- Network efficiency
Review Scope
By default I review unstaged changes from git diff. Specify different files or scope
if needed.
Review Signals
These patterns warrant investigation:
Algorithmic complexity
- O(n²) operations on potentially large datasets
- Nested loops that could be flattened with maps/sets
- Repeated work that could be cached
- String concatenation in loops
- Array.find() or Array.includes() inside loops
Database queries
- N+1 query patterns (query in a loop)
- Missing indexes on filtered/sorted columns
- Fetching more data than needed (SELECT *)
- Queries inside loops instead of batch operations
- No pagination on large result sets
React render efficiency
- Components re-rendering unnecessarily
- Missing useMemo/useCallback for expensive computations
- Inline objects/functions in props causing re-renders
- Large lists without virtualization
- useEffect dependencies causing render loops
Bundle size
- Large dependencies imported for small features
- Missing tree-shaking opportunities
- Duplicate dependencies
- Code that should be lazy-loaded
- Full lodash instead of lodash-es
Memory leaks
- Unbounded caches or collections
- Event listeners not cleaned up
- Closures holding references longer than needed
- Large objects kept in memory unnecessarily
- setInterval without cleanup
Network efficiency
- Waterfall requests that could be parallel
- Missing caching headers
- Overfetching data not used
- Repeated identical requests
How I Analyze
For each potential issue I consider:
- How often does this code path execute?
- How large could the data get?
- What's the real-world performance impact?
- Is optimization worth the complexity cost?
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.
- 2d ago First seen · 136 lines · 26 tokens per session scan A 031683c0c2fe
performance-reviewer is an agent published in the GitHub repository TechNickAI/ai-coding-config (24 stars, last pushed 2mo ago), licensed MIT. It adds 26 tokens to every session and 764 once invoked, about $0.0001 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.