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 skills/albumentations-team/albucore/performance-optimizationnpx skills add albumentations-team/albucore --skill performance-optimizationgit clone --depth 1 https://github.com/albumentations-team/albucoreWrote 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/albumentations-team/albucore/performance-optimization)<a href="https://agentmods.dev/skills/albumentations-team/albucore/performance-optimization"><img src="https://agentmods.dev/badge/skills/albumentations-team/albucore/performance-optimization.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 | $0.00060 | $0.00577 |
| Opus 5 | $0.00030 | $0.00289 |
| Sonnet 5 | $0.00012 | $0.00115 |
| Haiku 4.5 | $0.00006 | $0.00058 |
Grade A, and why
performance-optimization 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 — 56 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Performance Optimization
Read ../../../docs/performance-optimization.md completely before inspecting or changing runtime code. That document
is the canonical shared workflow for Albucore and AlbumentationsX.
When the sibling AlbumentationsX checkout is available, verify the fallback copy with
cmp -s docs/performance-optimization.md ../AlbumentationsX/.codex/skills/performance-optimization/references/performance-optimization.md.
A mismatch blocks completion.
Workflow
- Establish correctness and public-router performance baselines before editing.
- Run every stage of the canonical optimization pass. Do not stop after finding the first plausible improvement.
- Treat delete-first, vectorization, LUT, random generation,
bincount, backend selection, routing thresholds, and in-place mutation as benchmark questions. - Compare the complete public path, including dispatch, dtype conversion, contiguity, allocation, clipping, and shape repair.
- Read
../albucore-benchmarks/SKILL.mdcompletely before measuring. Extend the matrix along the dimension that controls the candidate. - Add or preserve correctness tests for every accepted route and its boundary cases.
- Update benchmark evidence and route documentation when public routing changes.
Albucore Boundary
Accept reusable atomic array operations with stable image semantics. Keep augmentation policy, stochastic parameter sampling, target dispatch, and annotation rules in AlbumentationsX.
Before adding an atom:
- search Albucore for an existing router or lower-level operation;
- search AlbumentationsX for duplicate local implementations;
- define dtype, shape, channel, contiguity, aliasing, and error contracts;
- benchmark all viable backends before exposing a route.
Albucore routers are called after the upstream validation boundary. The caller owns container type, rank/layout, dtype, device, contiguity, autograd, and operation-control validation. Do not duplicate those checks in the runtime router; retain only backend dispatch and kernel-required normalization. Invalid direct calls are outside the low-level contract and must not add hot-path branches.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 56 lines · 60 tokens per session scan A 42a7da0dfd99
performance-optimization is a skill published in the GitHub repository albumentations-team/albucore (123 stars, last pushed 6d ago), licensed MIT. It adds 60 tokens to every session and 577 once invoked, about $0.0003 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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