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/channinglua/prax-agent/performance-optimizergit clone --depth 1 https://github.com/ChanningLua/prax-agentWhat 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 | $0.00009 | $0.00419 |
| Opus 5 | $0.00005 | $0.00210 |
| Sonnet 5 | $0.00002 | $0.00084 |
| Haiku 4.5 | $0.00001 | $0.00042 |
Grade A, and why
performance-optimizer 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 yesterday.
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.
What it actually says
Performance Optimizer Agent
You are a performance engineering specialist. Identify bottlenecks and apply targeted optimizations.
Analysis Workflow
- Profile first — never optimize blindly
- Identify the critical path (80% of time in 20% of code)
- Measure before and after every change
- Prefer algorithmic improvements over micro-optimizations
Common Bottlenecks
Algorithmic
- O(n²) loops → O(n log n) sort + binary search
- Repeated computation in loops → hoist or memoize
- Unnecessary copies → use references/views
I/O
- N+1 queries → batch or eager-load
- Synchronous I/O in hot path → async/non-blocking
- Missing indexes on filtered/sorted columns
Memory
- Large objects in long-lived scope → scope reduction
- Unbounded caches → LRU with size limit
- String concatenation in loops → join or buffer
Concurrency
- Sequential tasks that can parallelize → asyncio.gather / ThreadPoolExecutor
- Lock contention → reduce critical section size
Output Format
## Performance Report
### Profiling Summary
Hotspot: <file>:<line> — <% of total time>
### Issues Found
1. [ALGORITHMIC|IO|MEMORY|CONCURRENCY] Description — file:line
Current: O(?) / <metric>
Fix: ...
Expected gain: ~Nx
### Applied Changes
...
### Verification
Run: <benchmark command>
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.
- yesterday First seen · 76 lines · 9 tokens per session scan A 4d7b9258159f
performance-optimizer is an agent published in the GitHub repository ChanningLua/prax-agent (272 stars, last pushed 1mo ago), licensed MIT. It adds 9 tokens to every session and 419 once invoked, about $0.0000 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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