Agent skill

GreptimeDB Dev Docker Image

by GreptimeTeam in GreptimeTeam/greptimedb

Packages a locally built GreptimeDB debug binary into a development-only Docker image for local-cluster testing, with an optional push to a dev registry.

Apache-2.0Auto-check: notesDevOps & Cloud

Install GreptimeDB Dev Docker Image

skills CLI
$ npx skills add GreptimeTeam/greptimedb --skill greptimedb-development-docker-image -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install GreptimeTeam/greptimedb greptimedb-development-docker-image --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/GreptimeTeam/greptimedb.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.agents/skills/greptimedb-development-docker-image .claude/skills/greptimedb-development-docker-image && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
greptimedb-development-docker-image
GitHub stars
6.7k
Token cost
~4k tokens
SKILL.md length
1,867 words
Files
12 (incl. scripts, assets)
Skills in repo
4
Repo updated
First seen
Licence
Apache-2.0

At a glance

Packages a locally built GreptimeDB debug binary into a development-only Docker image for local-cluster testing, with an optional push to a dev registry.

  • Works in 4 steps: Inspect the host and container tooling → Build the executable for the image… → Build or push the image → …
  • Packaging a locally built GreptimeDB binary for local-cluster debugging
  • SKILL.md covers Goal, Inputs, Interactive Workflow and Environment State, plus 2 more sections
  • Runs Python and Shell scripts from its folder; calls docker, python3 and podman

What it does

The skill follows GreptimeDB's documented development-image procedure: build greptime with Cargo's nightly profile, copy the binary into the Docker context as ./greptime, and build the supplied Dockerfile, which uses Ubuntu 24.04 only as the runtime base and exposes the binary as its entrypoint. It is explicitly not a release workflow and must not be used to publish production or release artifacts.

Before building, the agent gathers inputs in a single batch configuration: the source repository, the edition and binary name (open-source or Enterprise), the Cargo profile, a target platform preselected as linux/amd64 unless Docker runs on arm64, the build mode (a loadable debug image or a registry push), and the registry, repository and tag read from the workspace's .env, with the tag incremented if it already exists. Non-secret image settings are kept in .env for the next build.

Python and shell scripts handle platform detection, binary builds, context preparation, tag selection and image builds, and tests cover platform and config handling. Docker with Buildx is required for cross-platform builds, while Podman works only for native-platform builds.

When your agent uses it

  • Packaging a locally built GreptimeDB binary for local-cluster debugging
  • Cross-building a development image for another platform
  • Pushing a non-release GreptimeDB or Enterprise image to a development registry

Example prompts

  • “Build a dev Docker image from my debug GreptimeDB binary and load it locally.”
  • “Cross-build an arm64 image of the Enterprise edition and push it to our dev registry.”
  • “Tag and push the next development image version to the registry in my .env.”

Requirements

  • Docker with Buildx for cross-platform builds, or Podman for native builds
  • Cargo and a GreptimeDB source checkout
  • Python, for the helper scripts
  • Compatibility (from SKILL.md): Requires Docker with Buildx for cross-platform builds; Podman is supported for native-platform builds.

Workflow steps

4 steps, taken from the step headings in SKILL.md.

  1. Inspect the host and container tooling
  2. Build the executable for the image platform
  3. Build or push the image
  4. Verify the result

What it can do on your machine

Read from SKILL.md and the folder at commit a8e293f. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 8 files in scripts/ (Python and Shell), which the agent can run.

    Shell commands in SKILL.md call:

    • docker
    • python3
    • podman
    • cargo

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use docker, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

  • Compatibility

    Requires Docker with Buildx for cross-platform builds; Podman is supported for native-platform builds.

    From compatibility in the SKILL.md frontmatter.

Context cost

GreptimeDB Dev Docker Image loads about 4k tokens when it runs. Until then it costs about 83 tokens; SKILL.md has 1,867 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~83
When it runs · the whole SKILL.md, loaded when a task matches
~4k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check: notes

The automated check noted patterns worth knowing about, such as sudo or a known installer.

  • NoteMentions a .env fileSKILL.md:16
    retain the non-secret image settings in `.env` for
  • NoteMentions a .env fileSKILL.md:38
    ORY` only from the selected workspace's `.env` and prefill them in the batch configuration. Example: `registry.example.c
  • NoteMentions a .env fileSKILL.md:39
    TAG` only from the selected workspace's `.env` and prefill it in the batch configuration. When the tag exists in the sel
  • NoteMentions a .env fileSKILL.md:72
    reference, build mode, and non-secret `.env` values.
  • NoteMentions a .env fileSKILL.md:84
    | Registry/repository | Use `.env` defaults, enter new values |
  • NoteMentions a .env fileSKILL.md:93
    st changed non-secret image settings to `.env`;
  • NoteMentions a .env fileSKILL.md:121
    selected workspace's `.env`;
  • NoteMentions a .env fileSKILL.md:149
    confirm it before `.env` is updated or an image is built. Never use automatic
  • NoteMentions a .env fileSKILL.md:172
    lives only in the selected workspace's `.env`. The
  • NoteMentions a .env fileSKILL.md:173
    keys and never reads or displays other `.env`

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from GreptimeTeam/greptimedb at commit a8e293f, republished under its Apache-2.0 licence (© GreptimeTeam). 1,867 words, ~4,001 tokens.

Download SKILL.mdSave it as .claude/skills/greptimedb-development-docker-image/SKILL.md (or your agent's skills folder). This skill also uses 11 other files; get the full folder from GitHub.
name
greptimedb-development-docker-image
description
Builds a development-only GreptimeDB Docker image from a local debug binary for local-cluster testing, and optionally pushes it to a development registry. Use when the user asks to package, build, tag, publish, or cross-build a non-release GreptimeDB or GreptimeDB Enterprise image for debugging.
compatibility
Requires Docker with Buildx for cross-platform builds; Podman is supported for native-platform builds.
metadata.protocols
docker buildx podman
metadata.platforms
linux-amd64 linux-arm64

GreptimeDB Development Docker Image

Goal

Package a locally built GreptimeDB binary into a development-only Docker image for debugging and local-cluster testing, optionally push it to a development registry, and retain the non-secret image settings in .env for the next build. It is not a release-image workflow and must not be used to publish a production or release artifact.

This skill follows the documented development-image build procedure:

  • Build greptime with Cargo's nightly profile.
  • Copy the target binary into the Docker build context as ./greptime.
  • Build the supplied Dockerfile, which uses Ubuntu 24.04 only as the runtime base image and exposes the greptime binary as its entrypoint.

Inputs

Collect or discover the following before any build or push:

InputDiscovery and rule
Source repositoryTreat the directory in which this skill is invoked as the default workspace. Include it as an editable field in the batch configuration; do not ask a separate confirmation. Do not assume open-source versus Enterprise.
Edition and binaryDefault to greptime; use the binary name the user requests for Enterprise builds. The copied Docker-context filename must always be greptime.
Cargo profile and binary sourceInclude this in the batch configuration. Default to rebuilding nightly; reuse an existing binary only after the user explicitly accepts that its freshness is unverified.
Target platformPreselect linux/amd64 unless Docker's server is linux/arm64, then preselect linux/arm64. Let the user override it in the batch configuration. Ubuntu 24.04 is the runtime base image, not a target-platform choice. Each image has exactly one target platform, but it may differ from the host platform.
Build modeAsk whether the user wants a locally loadable debug image or a registry push.
Registry/repositoryRead IMAGE_REGISTRY and IMAGE_REPOSITORY only from the selected workspace's .env and prefill them in the batch configuration. Example: registry.example.com/team + greptimedb-dev.
TagRead IMAGE_TAG only from the selected workspace's .env and prefill it in the batch configuration. When the tag exists in the selected registry, preselect an incremented version.

Use the image reference ${IMAGE_REGISTRY}/${IMAGE_REPOSITORY}:${IMAGE_TAG}. If the registry is intentionally empty, omit its slash rather than producing a leading slash.

Interactive Workflow

Use the platform's interactive prompt component for every question, selection, and confirmation. Do not ask an open-ended text question when a single-choice, multi-select, or confirmation component can represent the decision. If the platform does not provide an interactive component, use the equivalent numbered or lettered prompt below and wait for input before continuing.

Batch configuration

Minimize user round trips: run the collector first against the invocation directory, then use one interactive form or batched prompt to collect workspace, edition/binary, profile/reuse-or-rebuild choice, target platform, build mode, registry/repository, tag, and whether to inspect the configured registry tag. Use the invocation directory as the editable workspace default. Prefill collector values and mark recommended defaults. If the user changes workspace, rerun the collector for that workspace without asking another configuration question. Only ask a follow-up if information is missing or invalid, or a safety gate is required. Treat unchanged prefilled values as accepted.

Keep separate confirmation components only for elevated privileges: sudo, package installation, and privileged QEMU setup. Use one final confirmation for all non-privileged selected work.

Always offer Cancel for an action that can write state, build, push, or require elevated privileges. Display the relevant preview before the user confirms it: source/binary path, file architecture result, platform(s), image reference, build mode, and non-secret .env values.

Batch configuration fields

Use single-choice controls inside the one batch form for mutually exclusive choices:

DecisionRequired choices
Cargo outputRebuild nightly (recommended), reuse an existing binary only with a freshness-unverified acknowledgement, choose a profile/binary
Target platformlinux/amd64 (default unless Docker server is arm64), linux/arm64 (default when Docker server is arm64). The selected image may be cross-built with Buildx.
Build modeLoad locally (recommended), push to development registry
Registry/repositoryUse .env defaults, enter new values
TagUse proposed increment when the configured tag exists, enter a version

When push is selected and the image configuration is complete, automatically run the read-only registry-tag check. It may return unknown; do not treat that as a missing tag.

Use one final confirmation for all selected non-privileged operations:

  • persist changed non-secret image settings to .env;
  • run the selected Cargo build or reuse the resolved binary;
  • prepare the Docker context;
  • build, verify, and, when selected, push the image.

The final confirmation must clearly state: “This creates a development and local-cluster test image, not a release or production artifact.”

For a real multi-select only, use lettered choices ([A], [B]) and accept all, none, or cancel; otherwise use single-choice components.

Preflight context collection

Before asking the profile, registry, or tag questions, run the bundled, read-only collector. It works on macOS and Linux, does not invoke cargo build, and emits JSON that can be shown or summarized to the user:

bash
python3 <skill-dir>/scripts/collect_context.py \
  --source <source-checkout> \
  --bin greptime \
  --profile nightly \
  [--target <rust-target>]

Use its report to identify:

  • missing IMAGE_REGISTRY, IMAGE_REPOSITORY, or IMAGE_TAG values in the selected workspace's .env;
  • Cargo binary targets available in the workspace and whether the requested --bin exists;
  • the resolved Cargo target directory, expected profile output path, and whether that binary already exists; and
  • macOS/Linux host architecture plus Docker's server platform when Docker is available.

When push is selected and image configuration is complete, rerun the collector with --check-registry-tag and all selected image values. It uses the selected engine's read-only manifest inspection and returns exists or unknown; unknown includes an unavailable registry, an absent tag, or missing authentication and must not be treated as an absent tag.

bash
python3 <skill-dir>/scripts/collect_context.py \
  --source <source-checkout> \
  --bin <binary> \
  --profile <profile> \
  --engine <docker|podman> \
  --registry <registry> \
  --repository <repository> \
  --tag <tag> \
  --check-registry-tag

When the status is exists and the tag ends in a number, preselect the collector's candidate_tag as the next development tag. The user must still confirm it before .env is updated or an image is built. Never use automatic incrementing as permission to overwrite or push an existing image.

If Cargo metadata or the requested binary target is unavailable, stop before building and ask the user to correct the source path or binary target.

The profile/binary choice is a field in the batch configuration, not a separate question. Its default is the existing nightly output when present:

text
Question: Which Cargo build output should package this image?
Options:
- Rebuild nightly (Recommended): run Cargo with `--profile nightly`, then use its output.
- Reuse nightly output: use the existing output only after showing its path, modification time, size, and matching platform; freshness is unverified.
- Choose profile or binary: provide a different Cargo profile or an explicit binary path.

Do not infer that a binary under target/debug, target/release, or another profile is suitable. For an existing binary, show its resolved path and run file before asking for final build confirmation.

Show full SKILL.md (842 more words)Show less

Environment State

The image configuration lives only in the selected workspace's .env. The collector reads only the following keys and never reads or displays other .env values:

dotenv
IMAGE_REGISTRY=registry.example.com/team
IMAGE_REPOSITORY=greptimedb-dev
IMAGE_TAG=dev-001
  1. Read .env if it exists. Ignore blank lines and # comments.
  2. Show the discovered registry, repository, and proposed tag to the user.
  3. Include registry/repository and tag in the batch configuration even when values already exist; saved values are defaults, not authorization to reuse them.
  4. Compare the selected configuration with the three current managed values. If all match, skip .env confirmation and do not update the file. Otherwise, preview only the changed IMAGE_* values, request confirmation, and update the file while preserving unrelated entries and comments.
  5. Never put registry credentials, access tokens, passwords, or docker login output in .env, build commands, logs, or responses. Ask the user to log in themselves if a push needs authentication.

For a repeated build, offer an incremented tag before asking. Use scripts/next_image_tag.py --tag <existing-tag> to calculate it. It increments the final numeric component while preserving zero padding:

text
weny-2025-0715-01 -> weny-2025-0715-02
v0.1.4 -> v0.1.5
debug-009 -> debug-010

If the saved tag has no trailing number, ask the user for the next tag rather than inventing a versioning convention. Do not overwrite an existing image tag without explicit user confirmation.

Only if a managed value is missing or differs, persist the values with the bundled helper after confirmation instead of hand-editing the file:

bash
python3 <skill-dir>/scripts/update_image_env.py \
  --env <source-checkout>/.env \
  --registry <registry> \
  --repository <repository> \
  --tag <tag>

Build Procedure

1. Inspect the host and container tooling

Run these checks and report the selected path:

bash
uname -s
uname -m
docker version --format '{{.Server.Os}}/{{.Server.Arch}}'
docker buildx version
docker buildx inspect --bootstrap

For a native-platform build, Podman can be used if Docker is unavailable. For any non-native request, require Docker Buildx. Do not silently fall back to a native build when the requested image platform differs.

If the requested target is unavailable, stop and ask the user to configure a Buildx builder and cross-compilation toolchain externally. Do not automate privileged QEMU or binfmt setup, and never run an unpinned privileged image.

2. Build the executable for the image platform

From the source checkout, build the requested binary. For open-source amd64:

bash
<skill-dir>/scripts/build_binary.sh \
  --source <source-checkout> --package cmd --bin greptime --profile nightly

For open-source arm64:

bash
<skill-dir>/scripts/build_binary.sh \
  --source <source-checkout> \
  --package cmd \
  --bin greptime \
  --profile nightly \
  --target aarch64-unknown-linux-gnu

For Enterprise, use the user-provided executable target, for example:

bash
<skill-dir>/scripts/build_binary.sh \
  --source <source-checkout> --bin greptime-ent-cloud --profile nightly

For a user-selected profile, replace nightly in the helper call and resolve the binary under target/<profile>/<binary> (or target/<rust-target>/<profile>/<binary> for cross-compilation). Reuse the existing target file only when the interactive profile question selected reuse; otherwise invoke the helper to rebuild it.

Do not use a binary compiled for the host architecture in an image intended for another architecture. Determine the binary path from the Cargo target and profile, then copy it into the Docker context as greptime:

bash
cp <source-target-binary> <build-context>/greptime

Verify it before building the image with file <build-context>/greptime; its reported architecture must match the requested target platform.

Create a fresh isolated build context (never use the repository root) and use the bundled preparation script. The helper rejects an existing Dockerfile or binary output to prevent accidental context reuse:

bash
<skill-dir>/scripts/prepare_context.sh \
  --context "$(mktemp -d)" \
  --binary <source-target-binary> \
  --dockerfile <skill-dir>/assets/Dockerfile \
  --platform <platform>
3. Build or push the image

Run the command only after the user confirms the final image reference and whether it should be pushed.

Use the bundled build helper rather than spelling out individual Docker or Podman commands. It follows the existing scripts' Docker-first/Podman-fallback behavior, uses Buildx for a non-native Docker request, and accepts exactly one target platform per image:

bash
<skill-dir>/scripts/build_image.sh \
  --context <build-context> \
  --image <image-reference> \
  --platform <platform> \
  --mode local|push

--mode local builds a native image locally or uses docker buildx --load for a non-native image. --mode push pushes the selected single-platform image. The helper never pushes in local mode.

This is intentionally a runtime image built from a precompiled binary, not a multi-stage Dockerfile. Ubuntu 24.04 is the runtime base image only; select one target platform separately as linux/amd64 or linux/arm64. Only introduce a multi-stage Dockerfile if the user asks to compile within Docker or the local Rust toolchain cannot build the required target. Keep the final Ubuntu 24.04 runtime stage and existing entrypoint unless the user asks to change runtime behavior.

At the final confirmation, repeat that the selected reference is a development/local-cluster test image, not a release artifact. If the user requests a release or production image, stop and direct them to the release process instead.

4. Verify the result

For a local image, use the selected engine to inspect its architecture and start it with --help:

bash
docker image inspect <image-reference> --format '{{.Os}}/{{.Architecture}}'
docker run --rm <image-reference> --help

For Podman, use podman image inspect <image-reference> and podman run --rm <image-reference> --help. For a pushed Podman image, use podman manifest inspect <image-reference>.

For a pushed image, inspect its remote manifest:

bash
docker buildx imagetools inspect <image-reference>

Report the final image reference, target platform, source binary path, and whether the image was loaded locally or pushed.

Safety Rules

  • This skill is only for development and local-cluster testing. Never present its image as a production, release, or officially published artifact.
  • Never push by default. Local debug builds should use a local tag and load the image unless the user explicitly requests a push.
  • Do not run docker login, privileged QEMU setup, package installation, or overwrite a tag without confirmation.
  • Do not delete existing images, builders, binaries, or .env entries as part of this workflow.
  • Stop and explain if Docker's server is unavailable, Buildx cannot support the requested platform, or the copied binary architecture does not match the image platform.

© GreptimeTeam, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 11 other files (scripts, assets) in .agents/skills/greptimedb-development-docker-image of GreptimeTeam/greptimedb.

  • SKILL.md
  • assets/Dockerfile
  • scripts/binary_platform.py
  • scripts/build_binary.sh
  • scripts/build_image.sh
  • scripts/collect_context.py
  • scripts/image_config.py
  • scripts/next_image_tag.py
  • scripts/prepare_context.sh
  • scripts/update_image_env.py
  • tests/test_binary_platform.py
  • tests/test_image_config.py

Open the folder on GitHubat commit a8e293f

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Works with

Questions about GreptimeDB Dev Docker Image

What does GreptimeDB Dev Docker Image do?

Packages a locally built GreptimeDB debug binary into a development-only Docker image for local-cluster testing, with an optional push to a dev registry. 04 only as the runtime base and exposes the binary as its entrypoint. It is explicitly not a release workflow and must not be used to publish production or release artifacts.

When should I use GreptimeDB Dev Docker Image?

GreptimeDB Dev Docker Image fits situations like: packaging a locally built GreptimeDB binary for local-cluster debugging; cross-building a development image for another platform; pushing a non-release GreptimeDB or Enterprise image to a development registry.

How do I install GreptimeDB Dev Docker Image in Claude Code?

Run `npx skills add GreptimeTeam/greptimedb --skill greptimedb-development-docker-image -a claude-code`. Or copy the skill folder (.agents/skills/greptimedb-development-docker-image in GreptimeTeam/greptimedb) into .claude/skills/greptimedb-development-docker-image in your project. Claude Code loads it when a task matches its description.

How do I install GreptimeDB Dev Docker Image in Codex?

Run `npx skills add GreptimeTeam/greptimedb --skill greptimedb-development-docker-image -a codex`. Or copy the skill folder (.agents/skills/greptimedb-development-docker-image in GreptimeTeam/greptimedb) into .agents/skills/greptimedb-development-docker-image in your project. Codex loads it when a task matches its description.

Can I use GreptimeDB Dev Docker Image in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add GreptimeTeam/greptimedb --skill greptimedb-development-docker-image -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/greptimedb-development-docker-image, .gemini/skills/greptimedb-development-docker-image, .github/skills/greptimedb-development-docker-image and .opencode/skills/greptimedb-development-docker-image in your project.

What does GreptimeDB Dev Docker Image need to run?

Going by SKILL.md and its folder, GreptimeDB Dev Docker Image needs Python and a shell for the scripts in its folder and the command-line tools its instructions call (docker, python3, podman and cargo). Our summary lists: Docker with Buildx for cross-platform builds, or Podman for native builds; Cargo and a GreptimeDB source checkout; Python, for the helper scripts. Compatibility (from SKILL.md): Requires Docker with Buildx for cross-platform builds; Podman is supported for native-platform builds..

Does GreptimeDB Dev Docker Image access the network?

SKILL.md contains no URLs. Its commands use docker, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is GreptimeDB Dev Docker Image safe to install?

Our automated static check of SKILL.md found notes only (mentions a .env file), nothing it rates as a warning. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does GreptimeDB Dev Docker Image use?

GreptimeDB Dev Docker Image is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does GreptimeDB Dev Docker Image use?

About 4k tokens (SKILL.md is roughly 16k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to GreptimeDB Dev Docker Image?

Skills that share tags, products or a category with GreptimeDB Dev Docker Image: Fly Io Deployer (LeoYeAI/openclaw-master-skills, 2.2k stars), Deploy To Temps (gotempsh/temps, 831 stars), Rust Deployable Service (hashgraph-online/awesome-codex-plugins, 1.3k stars) and Monstermq Broker Config (vogler75/monster-mq, 143 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains GreptimeDB Dev Docker Image?

GreptimeTeam (a GitHub organization) maintains it in GreptimeTeam/greptimedb, which has 6,727 GitHub stars. The repository holds 4 skills in this directory. The repository was last updated on October 10, 2026.

Source: GreptimeTeam/greptimedb on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.