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 skills add AxGord/claude-workflow --skill domain-yologit clone --depth 1 https://github.com/AxGord/claude-workflowWrote 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/axgord/claude-workflow/domain-yolo)<a href="https://agentmods.dev/skills/axgord/claude-workflow/domain-yolo"><img src="https://agentmods.dev/badge/skills/axgord/claude-workflow/domain-yolo/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/axgord/claude-workflow/domain-yolo"><img src="https://agentmods.dev/badge/skills/axgord/claude-workflow/domain-yolo.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00010 | $0.00882 |
| Opus 5 | $0.00005 | $0.00441 |
| Sonnet 5 | $0.00002 | $0.00176 |
| Haiku 4.5 | $0.00001 | $0.00088 |
Grade A, and why
domain-yolo 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 10d 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.
YOLO — Verified Gotchas
YOLO26 — Current Default
YOLO26 is NMS-free end-to-end (no NMS post-processing step), drops DFL, and delivers ~43% faster CPU inference. Same Ultralytics API — change the .pt file. Prefer it for new projects, especially CPU/edge deployment.
YOLO11 vs YOLOv8
YOLO11: higher mAP than v8m with 22% fewer params. Refined C3k2 blocks. Drop-in replacement — change .pt file, code identical. Same Ultralytics API.
YOLOv10 — Legacy NMS-Free
YOLOv10 pioneered NMS-free inference, but its headline "1.8x faster" figure is vs an RT-DETR-R18 baseline, not vs other YOLOs. Superseded by YOLO26 for the NMS-free niche.
Zero-Shot: YOLOE Supersedes YOLO-World
YOLOE covers text prompts, visual prompts, AND a prompt-free mode at real-time speed. Reach for YOLO-World only if a dependency pins it.
predict(source=<list>) — List Length IS the Batch, batch= Is Ignored
Passing a list of image paths to model.predict(source=...) runs one forward pass with the list length as the batch dimension — the batch= kwarg has no effect on a list source, it does not sub-divide it. A 128-path list at high resolution becomes a single huge (128, 3, H, W) allocation and can OOM the GPU; the failing allocation size stays invariant no matter what else you tune (TTA, model count), because the batch never actually changes.
- WRONG:
model.predict(source=all_128_paths, batch=1)# runs ONE batch-128 forward → OOM - RIGHT:
for sub in chunks(paths, 8): model.predict(source=sub)# batch bounded to 8
Verified Gotcha — Matching Shapes ≠ Same Model
When picking a source model to convert/requantize into a new precision variant, don't infer "same model" from matching input shape or overlapping backbone conv-layer shapes alone. A single-class fine-tune and the 80-class COCO base model of the same YOLO family (same width/depth, same input resolution) share nearly identical backbones — dozens of conv layers will have IDENTICAL shapes in both — because only the detection head's final output channel count differs (e.g. [1, 5, N] = 4 bbox coords + 1 class score, vs [1, 84, N] = 4 bbox coords + 80 class scores). Trusting partial shape matches can pick the WRONG source file — architecturally similar but a different model (different class count / task).
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.
- 10d ago First seen · 56 lines · 10 tokens per session scan A d0271c6c5dab
domain-yolo is a skill published in the GitHub repository AxGord/claude-workflow (5 stars, last pushed 1mo ago), licensed MIT. It adds 10 tokens to every session and 882 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 2026-08-31.
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