AuC is a Python framework for running a single AI agent with an asynchronous, pluggable reasoning loop, language-model adapters, permission levels, and observable events. It is used to build coding and conversational agents with tools, security checks, web interfaces, background jobs, evaluations, and isolated execution. The catalogue entries are skills for extending its agent workflow.
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 ufy2024/AuC --skill ggetgit clone --depth 1 https://github.com/ufy2024/AuCWrote 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/ufy2024/auc/gget)<a href="https://agentmods.dev/skills/ufy2024/auc/gget"><img src="https://agentmods.dev/badge/skills/ufy2024/auc/gget/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/ufy2024/auc/gget"><img src="https://agentmods.dev/badge/skills/ufy2024/auc/gget.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Agent Snooping · line 21 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
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 | $0.00034 | $0.01294 |
| Opus 5 | $0.00017 | $0.00647 |
| Sonnet 5 | $0.00007 | $0.00259 |
| Haiku 4.5 | $0.00003 | $0.00129 |
Grade A, and why
gget 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 9d 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 — 186 lines — stays where its author put it; the contents beside it link to each section on GitHub.
gget
Use this skill when a task needs quick bioinformatics lookup across genomic
reference databases with the gget CLI or Python package.
When to Use
- Finding Ensembl IDs, gene metadata, transcript details, or sequences.
- Running quick BLAST or BLAT lookups without building a full local pipeline.
- Fetching reference genome links and annotations from Ensembl.
- Querying protein structure, pathway, cancer, expression, or disease-association modules through a single interface.
- Creating a reproducible first-pass evidence log before moving to heavier tools such as Biopython, Snakemake, Nextflow, BLAST+, or database-specific clients.
Use a dedicated workflow instead of gget when the task requires regulated
clinical interpretation, high-throughput production pipelines, or fine-grained
control over database versions and local indexes.
Installation
Use a clean Python environment.
python -m venv .venv
. .venv/bin/activate
python -m pip install --upgrade pip
python -m pip install --upgrade gget
gget --help
If uv is available:
uv venv
. .venv/bin/activate
uv pip install gget
Before relying on an older environment, upgrade gget and re-check the module
docs. The upstream databases queried by gget change over time.
Basic Patterns
CLI shape:
gget <module> [arguments] [options]
Python shape:
import gget
result = gget.search(["BRCA1"], species="human")
print(result)
Common workflow:
- Identify the species, assembly, gene ID type, and database needed.
- Check the current module documentation for arguments.
- Run a small query first.
- Save output with an explicit filename and date.
- Record module name, version, arguments, and database assumptions.
Common Modules
Use current upstream docs for exact arguments. These modules are common first choices:
gget search: find Ensembl IDs from search terms.gget info: retrieve metadata for Ensembl, UniProt, or related IDs.gget seq: fetch nucleotide or amino-acid sequences.gget ref: retrieve reference genome download links.gget blast: run a quick BLAST query.gget blat: locate a sequence against supported genome assemblies.gget muscle: run multiple sequence alignment.gget diamond: run local sequence alignment against reference sequences.gget alphafoldandgget pdb: inspect protein-structure references.gget enrichr,gget opentargets,gget archs4,gget bgee,gget cbio, andgget cosmic: explore enrichment, target, expression, cancer, and disease association data.
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.
- 9d ago First seen · 186 lines · 34 tokens per session scan A 2d64cfd6863b
gget is a skill published in the GitHub repository ufy2024/AuC (1,090 stars, last pushed 1mo ago), licensed MIT. It adds 34 tokens to every session and 1,294 once invoked, about $0.0002 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 2026-09-03.
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