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 agentmods add skills/mitodl/agent-kit/docker-uv-image-buildsnpx skills add mitodl/agent-kit --skill docker-uv-image-buildsgit clone --depth 1 https://github.com/mitodl/agent-kitWrote 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/mitodl/agent-kit/docker-uv-image-builds)<a href="https://agentmods.dev/skills/mitodl/agent-kit/docker-uv-image-builds"><img src="https://agentmods.dev/badge/skills/mitodl/agent-kit/docker-uv-image-builds.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 | $0.00062 | $0.00423 |
| Opus 5 | $0.00031 | $0.00211 |
| Sonnet 5 | $0.00012 | $0.00085 |
| Haiku 4.5 | $0.00006 | $0.00042 |
Grade A, and why
docker-uv-image-builds scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
Docker Image Builds for Python Services
Image naming
Images follow the pattern: mitodl/<service-name>
The service name should match the application or code location name used elsewhere in configuration (Helm values, Pulumi stacks, Concourse pipelines).
Image tags
Tag images with the git short ref (7-character SHA):
GIT_TAG=$(git rev-parse --short HEAD)
docker build -t mitodl/${SERVICE_NAME}:${GIT_TAG} .
Do not use latest as a production tag.
Python environment inside the image
Use a relocatable virtual environment so the venv works after Docker layer assembly:
WORKDIR /app
COPY pyproject.toml uv.lock ./
RUN uv venv --relocatable /app/.venv && \
uv sync --frozen --no-dev
Shared libraries
Install shared internal libraries (e.g. ol-orchestrate-lib) as build-time
dependencies — do not mount them as Docker volumes at runtime. Add them to
pyproject.toml and let uv sync install them during the image build.
Build context & .dockerignore
Exclude development artifacts:
.venv/
__pycache__/
*.pyc
.git/
Concourse CI integration
Concourse pipelines use paths: filters on git resources to trigger image
rebuilds only when relevant files change. When adding a new service, add its
path to the corresponding pipeline's git resource paths list.
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.
- 4d ago First seen · 67 lines · 62 tokens per session scan A 47f6e849e6fc
docker-uv-image-builds is a skill published in the GitHub repository mitodl/agent-kit (2 stars, last pushed today), licensed BSD-3-Clause. It adds 62 tokens to every session and 423 once invoked, about $0.0003 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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