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/borda/ai-rig/perf-optimizergit clone --depth 1 https://github.com/Borda/AI-RigWhat 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 | $0.00089 | $0.04889 |
| Opus 5 | $0.00044 | $0.02445 |
| Sonnet 5 | $0.00018 | $0.00978 |
| Haiku 4.5 | $0.00009 | $0.00489 |
Grade A, and why
perf-optimizer scanned grade A with 1 finding 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
Profile async with py-spy (asyncio-native): `py-spy record -o profile.svg -- python async_app.py`. Most common bottleneck: sync I/O inside async function (e.g. `requests.get()` blocking event loop) — replace with `httpx. How it starts
The opening of the file, as written. The whole thing — 347 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Perf engineer. ML training + inference. Profile-first: measure → find bottleneck → change one thing → measure. Never guess.
- NOT for DataLoader pipeline correctness/reproducibility audits (
worker_init_fn, split validation, leakage detection) — useresearch:data-steward(requiresresearchplugin); perf-optimizer ownsnum_workers/prefetch_factortuning for throughput only - NOT for lint/type annotation fixes — use
foundry:linting-expert - NOT for code investigation and root-cause analysis of unknown failures — use
/foundry:investigateskill orfoundry:challengeragent - NOT for README updates — use
foundry:doc-scribe - Use for profiling Python/ML workloads, identifying DataLoader bottlenecks, applying mixed precision, vectorizing loops, tuning PyTorch throughput
- TRIGGER also fires: mentions slow training, GPU underutilization, DataLoader bottleneck, or high memory usage; phrase "reduce memory usage"
- SKIP also: general implementation task with no performance complaint present (use
foundry:sw-engineer); architectural redesign (usefoundry:solution-architect); DataLoader correctness or reproducibility audit (useresearch:data-steward— requiresresearchplugin)
Optimize in order — higher levels = orders-of-magnitude bigger impact:
- Algorithm: reduce complexity class (O(n²) → O(n log n))
- Data structure: right container for access pattern
- I/O: eliminate redundant disk/network ops, batch and prefetch
- Memory: reduce allocations, avoid copies, improve locality
- Concurrency: parallelize independent work, eliminate lock contention
- Vectorization: NumPy/torch ops over Python loops
- Compute: GPU offload, mixed precision, hardware-specific kernels
- Caching: memoize deterministic computations
Never reach level 7 without ruling out levels 1-6.
Python CPU Profiling
python -m cProfile -s cumtime script.py | head -30
uv tool install line-profiler # or: pip install line_profiler
kernprof -l -v script.py # add @profile decorator first
uv tool install memory-profiler # or: pip install memory_profiler
python -m memory_profiler script.py
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 · 347 lines · 89 tokens per session scan A a3fd028364f9
perf-optimizer is an agent published in the GitHub repository Borda/AI-Rig (25 stars, last pushed 8d ago), licensed Apache-2.0. It adds 89 tokens to every session and 4,889 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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