SafetyCulture (iAuditor) is an inspection, issue capture and corrective action platform for teams that’s used over 50,000 times a day in over 85 countries.
Write Python and use any of the 350k+ PyPi packages available. Refer to the Pipedream Python docs to learn more.
Create a new inspection in iAuditor by SafetyCulture. See the documentation
Create a new user in iAuditor by SafetyCulture. See the documentation
Retrieve an inspection report formatted as a PDF or Word (docx) document.See the documentation
Retrieve the web report link for the specified inspection. This will return the existing link if one has been generated before, or generate a new one if one does not exist already. See the documentation
iAuditor by SafetyCulture API allows you to tap into a rich reservoir of safety and quality inspection data, enabling you to automate workflows around inspection management. With this API on Pipedream, you can create custom integrations to trigger actions based on audit completions, new issues, or updates in iAuditor. Streamline safety processes, connect with other tools, and manipulate inspection data to fit your needs.
import { axios } from "@pipedream/platform"
export default defineComponent({
props: {
iauditor_by_safetyculture: {
type: "app",
app: "iauditor_by_safetyculture",
}
},
async run({steps, $}) {
return await axios($, {
url: `https://api.safetyculture.io/share/connections`,
headers: {
Authorization: `Bearer ${this.iauditor_by_safetyculture.$auth.api_token}`,
"accept": `application/json`,
"sc-integration-id": `sc-readme`,
},
})
},
})
Develop, run and deploy your Python code in Pipedream workflows. Integrate seamlessly between no-code steps, with connected accounts, or integrate Data Stores and manipulate files within a workflow.
This includes installing PyPI packages, within your code without having to manage a requirements.txt
file or running pip
.
Below is an example of using Python to access data from the trigger of the workflow, and sharing it with subsequent workflow steps:
def handler(pd: "pipedream"):
# Reference data from previous steps
print(pd.steps["trigger"]["context"]["id"])
# Return data for use in future steps
return {"foo": {"test":True}}