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 NVIDIA-TAO/tao-skill-bank --skill tao-data-iogit clone --depth 1 https://github.com/NVIDIA-TAO/tao-skill-bankWrote 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/nvidia-tao/tao-skill-bank/tao-data-io)<a href="https://agentmods.dev/skills/nvidia-tao/tao-skill-bank/tao-data-io"><img src="https://agentmods.dev/badge/skills/nvidia-tao/tao-skill-bank/tao-data-io/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/nvidia-tao/tao-skill-bank/tao-data-io"><img src="https://agentmods.dev/badge/skills/nvidia-tao/tao-skill-bank/tao-data-io.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 | $0.00159 | $0.01564 |
| Opus 5 | $0.00079 | $0.00782 |
| Sonnet 5 | $0.00032 | $0.00313 |
| Haiku 4.5 | $0.00016 | $0.00156 |
Grade C, and why
tao-data-io scanned grade C 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 8d 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.
Reaches for credential fileshighPrivilege escalation
SSH keys, cloud credentials, git-credentials, .npmrc, /etc/shadow: reading these is how a config file becomes a credential leak.
and **never** write `~/.aws/credentials`: How it starts
The opening of the file, as written. The whole thing — 112 lines — stays where its author put it; the contents beside it link to each section on GitHub.
tao-data-io
Get data to and from the compute container. Decide the storage tier first —
under strategy A (pre-positioned mount) no bytes move at all — and when a
fetch is needed, move it host-side with aws/s5cmd/boto3/huggingface-cli/ngc
directly — no nvidia-tao-sdk, no in-container runtime. Other platform skills
call this skill to stage inputs before launch and sync outputs after. It never
launches a container itself. The chosen tier is stamped into the job-record at
submit.
When NOT to invoke this skill: if the inputs are already readable from the compute frame (a local path on the execution host, an existing Lustre/PVC/bind mount), that IS tier A — record it and skip this skill entirely; there is nothing to move. Air-gapped hosts: tier A is the only tier — never attempt an S3/HF/NGC fetch; anything missing (datasets, checkpoints, and the container images themselves) must be pre-positioned by the operator, and the preflight's readability check is the only data step that runs.
Credentials (env vars; values never written to disk by this skill)
S3 credentials use the officially documented AWS env vars, read from the
session environment: AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY, and (for
S3-compatible stores) AWS_ENDPOINT_URL, AWS_DEFAULT_REGION. The aws
CLI and boto3 pick the variables up natively — never run aws configure
and never write ~/.aws/credentials:
set -a; source /path/to/.env; set +a # omit if already exported
aws s3 ls "s3://$S3_BUCKET_NAME/..." # reads AWS_* from the environment
If a session provides only the legacy TAO names (ACCESS_KEY, SECRET_KEY,
S3_ENDPOINT_URL, CLOUD_REGION), map them once, scoped to the command:
AWS_ACCESS_KEY_ID="$ACCESS_KEY" AWS_SECRET_ACCESS_KEY="$SECRET_KEY" aws s3 ...
HF_TOKEN / NGC_KEY pass through unchanged for PTM pulls. Never pass a
credential as a CLI argument (-p, --token, -e KEY=value); use
--password-stdin or -e VAR (no value).
What ships with it
3 files 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.
- 8d ago First seen · 112 lines · 159 tokens per session scan C f9d003bb4c82
tao-data-io is a skill published in the GitHub repository NVIDIA-TAO/tao-skill-bank (88 stars, last pushed today), licensed Apache-2.0. It adds 159 tokens to every session and 1,564 once invoked, about $0.0008 per session on Opus 5. A static security scan graded it C with 1 finding (reaches for credential files). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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