GreptimeDB is an observability database that stores metrics, logs, and traces in a columnar engine over object storage, using a shared table model for the data. It is for operating telemetry backends, correlating related signals with SQL, and querying metrics with PromQL.
Borrowing it
Nothing to install: this file belongs to GreptimeTeam/greptimedb. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/GreptimeTeam/greptimedb/main/.agents/skills/greptimedb-development-docker-image/SKILL.mdgit clone --depth 1 https://github.com/GreptimeTeam/greptimedbWrote 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/greptimeteam/greptimedb/greptimedb-development-docker-image)<a href="https://agentmods.dev/skills/greptimeteam/greptimedb/greptimedb-development-docker-image"><img src="https://agentmods.dev/badge/skills/greptimeteam/greptimedb/greptimedb-development-docker-image/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/greptimeteam/greptimedb/greptimedb-development-docker-image"><img src="https://agentmods.dev/badge/skills/greptimeteam/greptimedb/greptimedb-development-docker-image.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 6 findings, up to high
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 →
- high Privilege Escalation · line 191 Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
- high Privilege Escalation · line 214 Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
- medium MCP Rug Pull · line 340 Docker image references without a specific tag (:latest is implicit) or digest (@sha256:...) can be silently replaced by a malicious image.Fix: Pin the image: image:tag or image@sha256:abc123
- medium Excessive Agency · line 61 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
- medium Excessive Agency · line 87 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
- medium Excessive Agency · line 363 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00071 | $0.03547 |
| Opus 5 | $0.00036 | $0.01774 |
| Sonnet 5 | $0.00014 | $0.00709 |
| Haiku 4.5 | $0.00007 | $0.00355 |
Grade A, and why
greptimedb-development-docker-image 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 13d 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 — 369 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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
greptimewith Cargo'snightlyprofile. - 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 thegreptimebinary as its entrypoint.
Inputs
Collect or discover the following before any build or push:
| Input | Discovery and rule |
|---|---|
| Source repository | Treat 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 binary | Default 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 source | Include 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 platform | Preselect 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 mode | Ask whether the user wants a locally loadable debug image or a registry push. |
| Registry/repository | Read 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. |
| Tag | Read 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. |
What ships with it
11 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.
- assets/Dockerfile 315 B
- scripts/binary_platform.py 2.8 KB runs code
- scripts/build_binary.sh 1.5 KB runs code
- scripts/build_image.sh 4.1 KB runs code
- scripts/collect_context.py 9.4 KB runs code
- scripts/image_config.py 5.8 KB runs code
- scripts/next_image_tag.py 1.4 KB runs code
- scripts/prepare_context.sh 1.6 KB runs code
- scripts/update_image_env.py 1.8 KB runs code
- tests/test_binary_platform.py 4.2 KB runs code
- tests/test_image_config.py 4.7 KB runs code
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.
- 13d ago First seen · 369 lines · 71 tokens per session scan A 0106923e80e0
greptimedb-development-docker-image is a skill published in the GitHub repository GreptimeTeam/greptimedb (6,660 stars, last pushed yesterday), licensed Apache-2.0. It adds 71 tokens to every session and 3,547 once invoked, about $0.0004 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 2026-08-30.
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