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://docs.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://docs.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 sensor on my team and writes them to a
sheet called "CO2 nightly". One row per sensor 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://docs.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 /sensors and /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://docs.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://docs.1st.app/llms-full.txt.
In Make.com, build a scenario that runs every 10 minutes, calls GET
/v1/sensors, filters to sensors with status != "online", and emails
the [email protected] address listing each offline sensor 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://docs.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://docs.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.
