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.
git clone --depth 1 https://github.com/AMDResearch/ai4science-studioWrote 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/rules/amdresearch/ai4science-studio/apptainer-overlay-build)<a href="https://agentmods.dev/rules/amdresearch/ai4science-studio/apptainer-overlay-build"><img src="https://agentmods.dev/badge/rules/amdresearch/ai4science-studio/apptainer-overlay-build.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 5.1 | $0.00000 | $0.00383 |
| Opus 5 | $0.00000 | $0.00192 |
| Sonnet 5 | $0.00000 | $0.00077 |
| Haiku 4.5 | $0.00000 | $0.00038 |
Grade A, and why
apptainer-overlay-build scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 7d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
Apptainer overlay: use the SIF venv instead of --target
When building overlay images for Apptainer/Singularity containers that already
ship a PyTorch venv (e.g. /opt/venv in rocm/pytorch images), prefer
installing into the activated venv rather than using pip install --target.
Problem
pip install --target <dir> creates a fully isolated install that does not
see packages in the container's venv. When a dependency like
pytorch-lightning declares torch as a requirement, pip resolves and
downloads the full ROCm torch wheel (~6 GB), even though the identical version
is already present in the SIF.
Preferred approach
- Mount the overlay read-write and activate the SIF's venv.
- Use plain
pip install(no--target). pip sees the existing torch and skips it; new packages are written into the overlay's upper layer. - No post-install strip step is needed because torch was never downloaded.
apptainer exec --rocm --overlay "$OVERLAY:rw" "$SIF" bash -c '
source /opt/venv/bin/activate
pip install --no-cache-dir mpi4py huggingface-hub pytorch-lightning ...
'
When --target is still acceptable
- If the overlay must keep packages strictly isolated from the venv (e.g. multiple overlays with conflicting dep versions).
- In that case, keep the existing NFS-staging + strip-torch pattern, but document the ~6 GB download overhead in a comment.
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.
- 7d ago First seen · 41 lines · 0 tokens per session scan A 4cbee3fc0278
apptainer-overlay-build is a cursor rule published in the GitHub repository AMDResearch/ai4science-studio (4 stars, last pushed 1mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 383 tokens. 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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