Containerizing Applications
aiskillstore/marketplace
Containerizes applications with Docker, docker-compose, and Helm charts.
Develop the Time Series Analytics microservice itself (FastAPI + Kapacitor) — build and deploy it locally via Docker Compose or Helm, run the mocked unit test suite (tests/runtests.sh) and the…
Install Time Series Analytics Dev
$ npx skills add open-edge-platform/edge-ai-libraries --skill time-series-analytics-dev -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install open-edge-platform/edge-ai-libraries time-series-analytics-dev --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/open-edge-platform/edge-ai-libraries.git skills-src && mkdir -p .claude/skills && cp -r skills-src/microservices/time-series-analytics/.github/skills/time-series-analytics-dev .claude/skills/time-series-analytics-dev && rm -rf skills-srcUse ~/.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/
Install the "time-series-analytics-dev" agent skill from https://github.com/open-edge-platform/edge-ai-libraries/tree/main/microservices/time-series-analytics/.github/skills/time-series-analytics-dev into .claude/skills/time-series-analytics-dev/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "time-series-analytics-dev", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/open-edge-platform/edge-ai-libraries/tree/main/microservices/time-series-analytics/.github/skills/time-series-analytics-devType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add open-edge-platform/edge-ai-libraries --skill time-series-analytics-dev -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install open-edge-platform/edge-ai-libraries time-series-analytics-dev --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/open-edge-platform/edge-ai-libraries.git skills-src && mkdir -p .agents/skills && cp -r skills-src/microservices/time-series-analytics/.github/skills/time-series-analytics-dev .agents/skills/time-series-analytics-dev && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "time-series-analytics-dev" agent skill from https://github.com/open-edge-platform/edge-ai-libraries/tree/main/microservices/time-series-analytics/.github/skills/time-series-analytics-dev into .agents/skills/time-series-analytics-dev/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "time-series-analytics-dev", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add open-edge-platform/edge-ai-libraries --skill time-series-analytics-dev -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install open-edge-platform/edge-ai-libraries time-series-analytics-dev --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/open-edge-platform/edge-ai-libraries.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/microservices/time-series-analytics/.github/skills/time-series-analytics-dev .cursor/skills/time-series-analytics-dev && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "time-series-analytics-dev" agent skill from https://github.com/open-edge-platform/edge-ai-libraries/tree/main/microservices/time-series-analytics/.github/skills/time-series-analytics-dev into .cursor/skills/time-series-analytics-dev/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "time-series-analytics-dev", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/open-edge-platform/edge-ai-libraries.git --path microservices/time-series-analytics/.github/skills/time-series-analytics-dev--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add open-edge-platform/edge-ai-libraries --skill time-series-analytics-dev -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install open-edge-platform/edge-ai-libraries time-series-analytics-dev --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/open-edge-platform/edge-ai-libraries.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/microservices/time-series-analytics/.github/skills/time-series-analytics-dev .gemini/skills/time-series-analytics-dev && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "time-series-analytics-dev" agent skill from https://github.com/open-edge-platform/edge-ai-libraries/tree/main/microservices/time-series-analytics/.github/skills/time-series-analytics-dev into .gemini/skills/time-series-analytics-dev/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "time-series-analytics-dev", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install open-edge-platform/edge-ai-libraries time-series-analytics-devInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add open-edge-platform/edge-ai-libraries --skill time-series-analytics-dev -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/open-edge-platform/edge-ai-libraries.git skills-src && mkdir -p .github/skills && cp -r skills-src/microservices/time-series-analytics/.github/skills/time-series-analytics-dev .github/skills/time-series-analytics-dev && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "time-series-analytics-dev" agent skill from https://github.com/open-edge-platform/edge-ai-libraries/tree/main/microservices/time-series-analytics/.github/skills/time-series-analytics-dev into .github/skills/time-series-analytics-dev/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "time-series-analytics-dev", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add open-edge-platform/edge-ai-libraries --skill time-series-analytics-dev -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install open-edge-platform/edge-ai-libraries time-series-analytics-dev --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/open-edge-platform/edge-ai-libraries.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/microservices/time-series-analytics/.github/skills/time-series-analytics-dev .opencode/skills/time-series-analytics-dev && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "time-series-analytics-dev" agent skill from https://github.com/open-edge-platform/edge-ai-libraries/tree/main/microservices/time-series-analytics/.github/skills/time-series-analytics-dev into .opencode/skills/time-series-analytics-dev/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "time-series-analytics-dev", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
time-series-analytics-dev- GitHub stars
- 168
- Token cost
- ~1.5k tokens
- SKILL.md length
- 535 words
- Files
- 8 (incl. references)
- Skills in repo
- 29
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
Develop the Time Series Analytics microservice itself (FastAPI + Kapacitor) — build and deploy it locally via Docker Compose or Helm, run the mocked unit test suite (tests/runtests.sh) and the…
- Works in 3 steps: docker logs -f… → curl -sf http://localhost:5000/health… → Kapacitor-internal errors aren't in the…
- Releasing this services own code
- SKILL.md covers When to Use, Example Prompts, Reference Lookup and The one gotcha to know first, plus 6 more sections
- Calls docker, pip and pytest
What it does
Time Series Analytics Dev is an agent skill from open-edge-platform/edge-ai-libraries. Develop the Time Series Analytics microservice itself (FastAPI + Kapacitor) — build and deploy it locally via Docker Compose or Helm, run the mocked unit test suite (tests/runtests.sh) and the slower Docker/Helm end-to-end functional suite (tests-functional/), navigate and modify src/main.py (routes), src/classifierstartup.py (Kapacitor/UDF lifecycle), and src/opcuaalerts.py, and follow this service's release conventions (CHANGELOG.md, image-tag bump locations, Dockerfile build args). Use when modifying, testing…
Its SKILL.md is about 1.5k tokens, which your agent loads only when the skill is triggered. The skill folder holds 10 other files, including reference files (for example `evals/evals.json`, `example-prompts/add-udf-list-endpoint.md` and `example-prompts/debug-udf-not-starting.md`).
It sits in DevOps & Cloud, covering Forecasting and time series, Containers and Microservices. It works with Docker and FastAPI. The repository describes itself as: Libraries, microservices, tools, and other reference software, supporting development of performance-optimized Edge AI applications. The licence is Apache-2.0.
When your agent uses it
- Releasing this services own code
- Tasks that involve Forecasting and time series
- Tasks that involve Containers
Example prompts
- “/time-series-analytics-dev”
Requirements
- Python 3
- Docker
Workflow steps
3 steps, taken from the first numbered list in SKILL.md.
- docker logs -f ia-time-series-analytics-microservice — startup,
- curl -sf http://localhost:5000/health (503 = Kapacitor daemon not
- Kapacitor-internal errors aren't in the container's top-level log
What it can do on your machine
Read from SKILL.md and the folder at commit 960d2e4. 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
Shell commands in SKILL.md call:
dockerpippytestpython3curl
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md. Its commands use docker, pip and curl, 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.
Context cost
Time Series Analytics Dev loads about 1.5k tokens when it runs, and up to ~5k if it reads all its reference files. Until then it costs about 181 tokens; SKILL.md has 535 words of instructions outside code blocks.
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:120
| A version bump touches `docker/.env`, `helm/values.yaml`, and `README-dockerhub.md` together | see `references/release
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); files beside SKILL.md are not scanned.
SKILL.md
The full file from open-edge-platform/edge-ai-libraries at commit 960d2e4, republished under its Apache-2.0 licence (© open-edge-platform). 535 words, ~1,523 tokens.
Download SKILL.mdSave it as
.claude/skills/time-series-analytics-dev/SKILL.md (or your agent's skills folder). This skill also uses 7 other files; get the full folder from GitHub.- name
- time-series-analytics-dev
- description
- Develop the Time Series Analytics microservice itself (FastAPI + Kapacitor) — build and deploy it locally via Docker Compose or Helm, run the mocked unit test suite (tests/run_tests.sh) and the slower Docker/Helm end-to-end functional suite (tests-functional/), navigate and modify src/main.py (routes), src/classifier_startup.py (Kapacitor/UDF lifecycle), and src/opcua_alerts.py, and follow this service's release conventions (CHANGELOG.md, image-tag bump locations, Dockerfile build args). Use when modifying, testing, debugging, or releasing this service's own code. Not for merely deploying the prebuilt image to build a new UDF-based use case on top of it — that is time-series-analytics-user.
Time Series Analytics — Dev
Work on the service's source. This skill assumes a repo clone of
edge-ai-libraries with this microservice at
microservices/time-series-analytics/; if there is no clone, clone the
repo first.
Run all commands from the microservice root.
When to Use
- Add, modify, or remove a REST route in
src/main.py - Change Kapacitor/UDF lifecycle behavior in
src/classifier_startup.py - Run or extend the unit test suite, or the Docker/Helm functional suite
- Build the image from source, debug a running container, or tune GPU/core-pinning behavior
- Cut a release: bump the version consistently across the files that track it
Example Prompts
Sample problem-solving scenarios this skill handles end-to-end:
| Example | Problem it solves |
|---|---|
| add-udf-list-endpoint.md | Add a new REST route with test coverage |
| debug-udf-not-starting.md | Diagnose a deployed UDF that silently isn't processing data |
Reference Lookup
| File | Load when… |
|---|---|
references/source-map.md | locating where a route, config field, or lifecycle step lives before editing |
references/testing.md | writing new tests, running a subset, or avoiding the import-time Kapacitor-startup trap |
references/build-and-deploy.md | building the image, GPU/core-pinning setup, Helm deployment |
references/release-conventions.md | bumping the version, updating CHANGELOG.md, touching Dockerfile build args |
The one gotcha to know first
src/main.py imports classifier_startup at module load, and importing
that for real starts an actual Kapacitor daemon subprocess. Any test that
imports main must mock classifier_startup in sys.modules before
the import — tests/test_main.py already does this; reuse its pattern
rather than re-importing main fresh in a new test module. Details:
references/testing.md.
Environment setup
python3 -m venv env && source env/bin/activate
pip install -r requirements.txt -r tests/requirements.txtTest / verify loop
./tests/run_tests.sh # full unit suite + coverage (see references/testing.md)
PYTHONPATH=./src pytest tests -k <name> -v # fast iteration on one testFunctional (slow — builds the image / stands up Helm):
cd tests-functional && pip install -r requirements.txt
pytest -q -vv --self-contained-html --html=./test_report/report.html .Source map (summary)
src/main.py(~900 lines) — every route: ingestion, config, UDF package upload/validation, OPC UA alerts. The module-levelconfigdict is the single source of truth;POST /configis the only writer at runtime.src/classifier_startup.py(~550 lines) — Kapacitor daemon lifecycle: rewriteskapacitor.conf's[udf.functions.*]/[[mqtt]]sections fromconfig, validates the extracted UDF package's files exist, startskapacitordas a subprocess, enables the Kapacitor task via its CLI.src/opcua_alerts.py(~210 lines) —asyncua-based OPC UA client used by the/opcua_alertsroute.- Full annotated map:
references/source-map.md.
Show full SKILL.md (211 more words)Show less
Build & deploy from source
cd docker && docker compose build && docker compose up -dGPU driver setup, CPU core-pinning (CORE_PINNING env var), Helm chart
values, and the /dev/dri-mount gotcha on GPU-less hosts:
references/build-and-deploy.md.
Debug a running instance
docker logs -f ia-time-series-analytics-microservice— startup, Kapacitor task enable/retry, request logs.curl -sf http://localhost:5000/health(503 = Kapacitor daemon not running, not just "process not ready").- Kapacitor-internal errors aren't in the container's top-level log:
docker exec -it ia-time-series-analytics-microservice bashthencat /tmp/log/kapacitor/kapacitor.log | grep -i error.
Contribution gotchas
| Gotcha | Consequence |
|---|---|
classifier_startup starts a real Kapacitor daemon on import | tests must mock it in sys.modules before importing main (see above) |
The three UDF names (config.json's udfs.name, .py filename, .tick filename, tick script's @name() node) must be identical | a mismatch fails silently at the pipeline level, not loudly — worth checking first when a "deployment succeeded but nothing happens" bug report comes in |
kapacitord runs as a subprocess inside this same container, not a sidecar | don't assume container-to-container networking semantics when tracing a startup failure |
Compose unconditionally mounts /dev/dri | fails container startup on hosts with no Intel iGPU — see references/build-and-deploy.md |
A version bump touches docker/.env, helm/values.yaml, and README-dockerhub.md together | see references/release-conventions.md — don't bump only one |
| Every new source/config/doc file needs the SPDX header | matches the existing files' Apache v2 license / Copyright (C) 2026 Intel Corporation / SPDX-License-Identifier: Apache-2.0 block |
© open-edge-platform, 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 7 other files (references) in microservices/time-series-analytics/.github/skills/time-series-analytics-dev of open-edge-platform/edge-ai-libraries.
- SKILL.md
- evals/evals.json
- example-prompts/add-udf-list-endpoint.md
- example-prompts/debug-udf-not-starting.md
- references/build-and-deploy.md
- references/release-conventions.md
- references/source-map.md
- references/testing.md
Open the folder on GitHubat commit 960d2e4
Compare with similar skills
Time Series Analytics Dev next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Time Series Analytics Dev this skillopen-edge-platform/edge-ai-libraries | 168 | — | ~1.5k | Automated safety check: Notes | Apache-2.0 | |
| Containerizing Applicationsaiskillstore/marketplace | 430 | — | ~1.9k | Automated safety check: Pass | None | |
| Model Deploymentsecondsky/claude-skills | 227 | — | ~2.4k | Automated safety check: Pass | MIT | |
| AI ServerOpentrons/opentrons | 521 | — | ~2.5k | Automated safety check: Notes | Apache-2.0 | |
| Code PatternsAedelon/claude-code-blueprint | 120 | — | ~1.2k | Automated safety check: Pass | Custom licence | |
| Frontmcp Deploymentagentfront/frontmcp | 146 | — | ~9.2k | Automated safety check: Notes | Apache-2.0 |
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Related
Questions about Time Series Analytics Dev
What does Time Series Analytics Dev do?
Develop the Time Series Analytics microservice itself (FastAPI + Kapacitor) — build and deploy it locally via Docker Compose or Helm, run the mocked unit test suite (tests/runtests.sh) and the…. Time Series Analytics Dev is an agent skill from open-edge-platform/edge-ai-libraries.md, image-tag bump locations, Dockerfile build args).
When should I use Time Series Analytics Dev?
Time Series Analytics Dev fits situations like: releasing this services own code; tasks that involve Forecasting and time series; tasks that involve Containers.
How do I install Time Series Analytics Dev in Claude Code?
Run `npx skills add open-edge-platform/edge-ai-libraries --skill time-series-analytics-dev -a claude-code`. Or copy the skill folder (microservices/time-series-analytics/.github/skills/time-series-analytics-dev in open-edge-platform/edge-ai-libraries) into .claude/skills/time-series-analytics-dev in your project. Claude Code loads it when a task matches its description.
How do I install Time Series Analytics Dev in Codex?
Run `npx skills add open-edge-platform/edge-ai-libraries --skill time-series-analytics-dev -a codex`. Or copy the skill folder (microservices/time-series-analytics/.github/skills/time-series-analytics-dev in open-edge-platform/edge-ai-libraries) into .agents/skills/time-series-analytics-dev in your project. Codex loads it when a task matches its description.
Can I use Time Series Analytics Dev 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 open-edge-platform/edge-ai-libraries --skill time-series-analytics-dev -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/time-series-analytics-dev, .gemini/skills/time-series-analytics-dev, .github/skills/time-series-analytics-dev and .opencode/skills/time-series-analytics-dev in your project.
What does Time Series Analytics Dev need to run?
Going by SKILL.md and its folder, Time Series Analytics Dev needs the command-line tools its instructions call (docker, pip, pytest, python3 and curl). Our summary lists: Python 3; Docker.
Does Time Series Analytics Dev access the network?
SKILL.md contains no URLs. Its commands use docker, pip and curl, which can reach the network depending on how they are called. This is read from the text; nothing was executed.
Is Time Series Analytics Dev 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. Review the folder before installing.
What licence does Time Series Analytics Dev use?
Time Series Analytics Dev 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 Time Series Analytics Dev use?
About 1.5k tokens (SKILL.md is roughly 6.1k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 3.5k tokens, read only when the agent opens those files.
What are the alternatives to Time Series Analytics Dev?
Skills that share tags, products or a category with Time Series Analytics Dev: Containerizing Applications (aiskillstore/marketplace, 430 stars), Model Deployment (secondsky/claude-skills, 227 stars), AI Server (Opentrons/opentrons, 521 stars) and Code Patterns (Aedelon/claude-code-blueprint, 120 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Time Series Analytics Dev?
open-edge-platform (a GitHub organization) maintains it in open-edge-platform/edge-ai-libraries, which has 168 GitHub stars. The repository holds 29 skills in this directory. The repository was last updated on October 7, 2026.
Source: open-edge-platform/edge-ai-libraries on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.