Documentation Index
Fetch the complete documentation index at: https://dev.1st.app/llms.txt
Use this file to discover all available pages before exploring further.
This is the headline page. The 1st API is designed for an analyst or ops
lead, not a backend developer. Your superpower is the AI tool sitting next to
you. Claude, ChatGPT, Cursor, Windsurf. Give it the right context and it
writes your integration.
The universal prefix
Every prompt below starts with this line, paste the full docs into the AI
once per conversation:
Read https://dev.1st.app/llms-full.txt, that's the complete reference
for the 1st sensor platform API. I'll then describe what I want to build.
1. Google Sheets: last night’s CO2
Read https://dev.1st.app/llms-full.txt.
Now write a Google Apps Script that, every morning at 6 AM, fetches last
night's CO2 readings for every device on my team and writes them to a
sheet called "CO2 nightly". One row per device per 5-minute bucket. The
script reads the API key from PropertiesService (ST_API_KEY), the key
already scopes the team, no team_id needed.
Include error handling, log each error `code` by name so I can debug.
2. Glide app: 7-day room conditions
Read https://dev.1st.app/llms-full.txt.
I'm building a Glide app for my coaching staff. They want to see room
conditions over the past 7 days, with a graph per room.
Generate (a) a Google Sheet schema (columns + sample row) populated by a
cron job that pulls /devices and /devices/{id}/readings, (b) the Apps
Script code for the cron, and (c) instructions for wiring the sheet into
a Glide app with one screen per room and a 7-day line chart of CO2.
3. Airtable: rooms above 1000 ppm CO2
Read https://dev.1st.app/llms-full.txt.
I have an Airtable base where each row is a player room. Help me add an
Automation that runs every hour, pulls the latest CO2 reading for each
room from the 1st API, and flags the row red if CO2 > 1000 ppm for the
last 15 minutes.
Use the Airtable Scripting block. The API key is in `ST_API_KEY` secret
config (no team_id needed, the key scopes the team).
Read https://dev.1st.app/llms-full.txt.
In Make.com, build a scenario that runs every 10 minutes, calls GET
/v1/devices, filters to devices with status != "online", and emails
the coach@example.com address listing each offline device by
display_name.
Use the HTTP module with Bearer auth from a Make data store. Provide the
exact Make.com module config screenshots... wait, just describe each
module step-by-step, since you can't actually screenshot.
5. Bubble: fleet dashboard
Read https://dev.1st.app/llms-full.txt.
I'm building a Bubble.io dashboard for our Director of Performance. The
landing page shows a card per room with current CO2, temp, humidity. The
detail page shows a 7-day chart for that room with drill-down to 24h.
(a) List the Bubble API Connector setup I need (endpoints to register,
auth headers, data types). (b) For the landing page, show what
expressions / workflow conditions populate the cards from the API. (c) For
the detail page, show the chart-data expression for the 7-day pull.
Why this works
Three properties make the 1st API extra-friendly to AI codegen:
- The OpenAPI spec is the SDK. Every endpoint description tells the AI
when to use it and when not to. Every field has a plain-English
description and a realistic example.
- Error messages contain the fix inline. When the AI’s first attempt
hits a 400, the response body tells it exactly how to fix the request.
no doc round-trip needed.
- Everything is in one llms-full.txt file. Under 80k tokens, fits in
any modern AI context window. No “I need to read more docs” loop.
When the AI gets stuck
- Ask it to re-read
https://dev.1st.app/llms-full.txt from scratch in a
fresh conversation.
- Show it the exact 4xx response body, the
message field is the fix.
- Ask for one part of the integration at a time, not the whole thing in one
prompt.
