Time Series Analytics User

What it does

When your agent uses it

• The user describes a sensor/metric monitoring
• Anomaly-detection scenario
• Wants to plug their own analytics logic
• A trained scikit-learn model into a streaming

Example prompts

• “/time-series-analytics-user”

Requirements

• Python 3
• A Bash shell
• Docker

Workflow steps

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

1. 1Get the service running
2. 2Pick a pattern
3. 3Write the UDF and tick script
4. 4Package and deploy
5. 5Feed data and verify
6. 6Optional: alerting

What it can do on your machine

Read from SKILL.md and the folder at commit cdf860c. 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 1 file in scripts/ (Python and Shell), which the agent can run.

  Shell commands in SKILL.md call:

  • docker

  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.

Context cost

Time Series Analytics User loads about 3.1k tokens when it runs, and up to ~12k if it reads all its reference files. Until then it costs about 245 tokens; SKILL.md has 1,071 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it

~245

When it runs · the whole SKILL.md, loaded when a task matches

~3.1k

With references · SKILL.md plus every file in references/, read only if the agent opens them

~12k

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

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 open-edge-platform/edge-ai-libraries at commit cdf860c, republished under its Apache-2.0 licence (© open-edge-platform). 1,071 words, ~3,075 tokens.

name

time-series-analytics-user

description

Build a new time-series analytics use case on top of the deployed Time Series Analytics microservice — bring it up with Docker Compose (from a repo clone, or by fetching the compose files from GitHub when no clone exists) using the prebuilt intel/ia-time-series-analytics-microservice image, then author a UDF (Python) + TICKscript pair for the use case (threshold alerting, rate-of-change/spike detection, rolling-window anomaly detection, pretrained-model inference per point, or batch windowed inference over a time window), package it as a tar, deploy it via the REST API, and feed it data. Use when the user describes a sensor/metric monitoring or anomaly-detection scenario, wants to plug their own analytics logic or a trained scikit-learn model into a streaming or windowed-batch pipeline, or asks to wire up MQTT/OPC UA alerting on top of this service. Not for modifying the microservice's own source code — that is time-series-analytics-dev.

examples/<use-case-name>/
├── README.md                           ← Quick start + customization guide
├── deploy.sh                           ← Setup automation script
├── test.sh                             ← Validation script
├── test_data.json                      ← Sample input for testing
├── config.json                         ← UDF configuration (upload to microservice)
├── <use-case-name>.tar                 ← Packaged UDF (upload to microservice)
├── DEPLOYMENT_VALIDATION_REPORT.md     ← Test results and diagnostics
│
├── udfs/
│   └── <udf-name>.py                   ← Kapacitor Python UDF handler
├── tick_scripts/
│   └── <udf-name>.tick                 ← TICKscript wiring
└── models/
    └── <model-name>.pkl (or .xml/.bin) ← Pre-trained model file(s) (if applicable)

cd examples/<use-case-name>
chmod +x deploy.sh test.sh
./deploy.sh    # prints next steps
./test.sh      # validates deployment

[ -f docker/docker-compose.yml ] && echo REPO || echo STANDALONE

mkdir -p udfs tick_scripts   # standalone: these won't exist yet
cp .github/skills/time-series-analytics-user/assets/udf_stream_template.py udfs/<name>.py
cp .github/skills/time-series-analytics-user/assets/tick_template.tick tick_scripts/<name>.tick

.github/skills/time-series-analytics-user/scripts/package_udf.sh <name> .

docker logs -f ia-time-series-analytics-microservice

docker logs ia-time-series-analytics-microservice 2>&1 | grep -F "Flagged anomalous point"
docker logs ia-time-series-analytics-microservice 2>&1 | grep -F "Converted line protocol"

docker exec <broker_container> sh -lc \
  "timeout 8 mosquitto_sub -h localhost -t '<topic>' -v"

docker compose down -v   # from docker/

© 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 13 other files (scripts, references, assets) in microservices/time-series-analytics/.github/skills/time-series-analytics-user of open-edge-platform/edge-ai-libraries.

• SKILL.md
• assets/tick_template.tick
• assets/udf_stream_template.py
• benchmark/benchmark.json
• benchmark/benchmark.md
• evals/evals.json
• example-prompts/pressure-threshold-alert.md
• example-prompts/vibration-spike-mqtt-alert.md
• example-prompts/windturbine-anomaly-model.md
• references/api-workflow.md
• references/patterns.md
• references/tickscript-basics.md
• references/udf-authoring.md
• scripts/package_udf.sh

Open the folder on GitHubat commit cdf860c