Borrowing it
Nothing to install: this file belongs to albumentations-team/albucore. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/albumentations-team/albucore/main/.codex/skills/albucore-conventions/SKILL.mdgit 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/albucore-conventions)<a href="https://agentmods.dev/skills/albumentations-team/albucore/albucore-conventions"><img src="https://agentmods.dev/badge/skills/albumentations-team/albucore/albucore-conventions/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/albumentations-team/albucore/albucore-conventions"><img src="https://agentmods.dev/badge/skills/albumentations-team/albucore/albucore-conventions.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 5.1 | $0.00077 | $0.04912 |
| Opus 5.5 | $0.00031 | $0.01965 |
| Sonnet 5.5 | $0.00015 | $0.00982 |
| Haiku 4.5 | $0.00008 | $0.00491 |
Grade A, and why
albucore-conventions 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.
How it starts
The opening of the file, as written. The whole thing — 190 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Albucore Conventions
For runtime implementation or review, also read ../performance-optimization/SKILL.md and its canonical reference
completely before acting.
When to Apply
- Implementing or changing code under
albucore/. - Adding or modifying tests.
- Reviewing image-processing code.
- Answering questions about shapes, dtypes, backend choice, or dependency lock consistency.
Critical Rules
1. Image Shapes - Always Explicit Channel Dimension
# Correct
grayscale = (H, W, 1)
rgb = (H, W, 3)
batch = (N, H, W, C)
volume = (D, H, W, C)
volume_batch = (N, D, H, W, C)
# Wrong - never use implicit channels
gray = (H, W)
gray_vol = (D, H, W)
Dimension indexing is always:
num_channels = image.shape[-1]
width = image.shape[-2]
height = image.shape[-3]
NumPy images use HWC, NHWC, DHWC, or NDHWC. An API that accepts a torch.Tensor defines its layout
independently; never infer a Tensor layout from its rank. Common image operations use explicit CHW, NCHW,
CDHW, or NCDHW layout where channel semantics matter. resize3d declares NumPy DHWC and Torch CDHW.
warp_affine3d and remap3d also accept NumPy NDHWC and Torch NCDHW batches. Their shared matrix or grid
applies to every item. Other 3D routers accept one volume. AlbumentationsX checks CPU, layout, strides, eager
(requires_grad=False) execution, and control data before the call.
1a. Precondition Boundary - Do Not Revalidate in Albucore
Albucore routers receive prevalidated inputs from the caller. The upstream transform layer owns validation of:
- container type, rank, layout, and explicit channel dimension;
- dtype, device, contiguity, and autograd state;
- spatial sizes, control-data shapes, interpolation, border, and fill contracts.
Do not add duplicate runtime checks for these preconditions inside Albucore routers. Keep only the dispatch needed to select the NumPy or Torch implementation and the normalization/conversion required by the selected kernel. A caller contract violation is outside the low-level router contract; tests and benchmarks must use valid inputs. Document the precondition boundary in public-router docstrings.
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 Changed 5ad8c1fa3070
- 2d ago Changed a39b930db535
- 4d ago Changed · +30 lines c7a58ae8caac
- 24d ago Changed · +29 lines · +3 tokens per session 75b635fce7c8
- 1mo ago Changed · +2 lines ae2030153976
- 2mo ago First seen · 129 lines · 74 tokens per session scan A 5b702d3cfe9c
albucore-conventions is a skill published in the GitHub repository albumentations-team/albucore (124 stars, last pushed yesterday), licensed MIT. It adds 77 tokens to every session and 4,912 once invoked, about $0.0003 per session on Opus 5.5. 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.
albucore-conventions compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| albucore-conventionsalbumentations-team/albucore | Skill | 124 | yesterday | MIT | A | 77 | Codex |
| lepton-clileptonai/leptonai | Skill | 2.8k | yesterday | Apache-2.0 | A | 122 | Claude Code |
| Computer Vision Helpereddiebelaval/squire | Skill | 21 | 2mo ago | MIT | A | 16 | Claude Code, Codex |
| platform-cliultralytics/skills | Skill | 35 | yesterday | AGPL-3.0 | A | 159 | Claude Code, Codex |
| ml-for-aecAbhinavbwj/Claude-skills-for-Computational-Designers | Skill | 226 | 7mo ago | MIT | A | 39 | Claude Code |
| yolo-exportultralytics/skills | Skill | 35 | yesterday | AGPL-3.0 | A | 120 | Claude Code, Codex |
| yolo-trainingultralytics/skills | Skill | 35 | yesterday | AGPL-3.0 | A | 95 | Claude Code, Codex |