Summarize Ably message latency data
Get a readable view of latency distribution from the data available to your workflow.
Trigger
Webhook received
Notis starts this workflow when an external tool or custom backend sends an HTTP request.
Action
Execute Code
Execute python code in a sandbox and return any result, stdout, stderr, and error. use /home/user folder to write/read files. try to not use plt.show() as the code is executed remotely. use files for image/chart output instead.
Why this helps
Latency samples are difficult to interpret quickly without consistent summary statistics and a clear reporting window.
- Calculate summary statistics from supplied latency data.
- Save a compact report for review.
- Repeat the same analysis from a webhook request.
Setup
Build it in a few focused steps.
- 1Connect Ably and Codeinterpreter once in the Notis portal.
- 2Create an automation in the portal or tell Notis in plain language from any channel.
- 3Describe the latency data source, reporting window, and statistics to include.
- 4Choose notis_webhook as the trigger and select where run reports should go.
- 5Test with one real sample and verify the report figures.
Questions about this workflow
Does Ably expose latency values automatically here?
No trigger events are listed. Provide accessible latency values or a data source in the instruction.
Which statistics should the report include?
Specify the measures you care about, such as median or percentile values. The workflow cannot infer a reporting standard for your team.
Can the report identify a cause for high latency?
It can describe patterns in the data provided, but causal diagnosis requires additional telemetry and context.
When this happens · Trigger
Do this · Action
Supported Triggers and Actions
Notis builds workflows that link Ably to Codeinterpreter. A trigger fires from one place; an action lands in another.
Ably triggers
Codeinterpreter actions
Recurring trigger
Notis starts this workflow on a schedule, such as daily, weekly, or during business hours.
Create Sandbox
Create a sandbox to execute python code in a jupyter notebook cell. this is useful for agents to communicate, execute code, see output, read files, write files, etc. it's like you own personal computer, but in the cloud. use /home/user folder to write/read files.
Webhook trigger
Notis starts this workflow when an external tool or custom backend sends an HTTP request.
Execute Code
Execute python code in a sandbox and return any result, stdout, stderr, and error. use /home/user folder to write/read files. try to not use plt.show() as the code is executed remotely. use files for image/chart output instead.
Get File
Get a file from the sandbox and returns the file. the files should be read from /home/user folder.
Run Terminal Command
Run a command in the terminal and returns the stdout, stderr, and error code. use /home/user folder to write/read files.
Upload File
Upload a file to the sandbox environment. the files should be uploaded to the /home/user folder.
Connect any two apps with Notis in the middle.
Ably and Codeinterpreter, or any other combination from 1,000+ integrations.
When this happens · Trigger
Do this · Action
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