Spot Workplace Discrimination Patterns with AI
38 nodes · manual, lmChatOpenAi, httpRequest, html, informationExtractor, quickChart
- Engine
- n8n
- Nodes
- 38
- Trigger
- scheduled/triggered
- Source file
- 1994_Code_Manual_Automation_Webhook.json
Workflow flow
When clicking ‘Test workflow’
Manual
OpenAI Chat Model1
@n8n/n8n-nodes-langchain.lmChatOpenAi
OpenAI Chat Model2
@n8n/n8n-nodes-langchain.lmChatOpenAi
OpenAI Chat Model
@n8n/n8n-nodes-langchain.lmChatOpenAi
SET company_name
Set
Define dictionary of demographic keys
Set
ScrapingBee Search Glassdoor
HTTP
Extract company url path
Html
ScrapingBee GET company page contents
HTTP
Extract reviews page url path
Html
ScrapingBee GET Glassdoor Reviews Content
HTTP
Extract Overall Review Summary
Html
Extract Demographics Module
Html
Extract overall ratings and distribution percentages
@n8n/n8n-nodes-langchain.informationExtractor
Extract demographic distributions
@n8n/n8n-nodes-langchain.informationExtractor
Define contributions to variance
Set
Set variance and std_dev
Set
Merge
Merge
Calculate Z-Scores and Effect Sizes
Set
Calculate P-Scores
Code
Format dataset for scatterplot
Code
Sort Effect Sizes
Set
Specify additional parameters for scatterplot
Set
Quickchart Scatterplot
HTTP
QuickChart Bar Chart
QuickChart
Text Analysis of Bias Data
@n8n/n8n-nodes-langchain.chainLlm
26 steps · flows top to bottom, branches left to right
Browse by integration
Browse all integrations →Import into n8n
- Download the n8n JSON file (full or sanitized).
- In n8n: Workflows → Import from file.
- Open each node and reconnect credentials to your accounts.
- Test with manual execution before enabling triggers.
Sanitized JSON removes credential IDs — safer for sharing. Flow graph JSON is OpenRuna's lightweight format for diagrams and other tools, not n8n import.
Overview
Spot Workplace Discrimination Patterns with AI is a free n8n workflow on OpenRuna — 38 nodes connecting manual, lmChatOpenAi, httpRequest, html, informationExtractor, quickChart, chainLlm. Download the JSON, view the flow diagram, and import it into your n8n instance.
What this workflow does
Built in n8n, "Spot Workplace Discrimination Patterns with AI" chains 38 nodes to move data between manual, lmChatOpenAi, httpRequest, html, informationExtractor, quickChart, chainLlm. 38 nodes · manual, lmChatOpenAi, httpRequest, html, informationExtractor, quickChart It starts from a scheduled/triggered trigger and routes data through every node in order. OpenRuna renders the node graph and offers the JSON export so you can import and adapt it in minutes. Use it to learn integration patterns, fork it for your own use case, or deploy it quickly after reconnecting credentials.
Use cases
- Study how data moves from manual to lmChatOpenAi before building your own variant.
- Share it with your team as a documented reference for scheduled/triggered automations in the manual stack.
- Import "Spot Workplace Discrimination Patterns with AI" into n8n, reconnect credentials for manual, lmChatOpenAi, httpRequest, html, informationExtractor, quickChart, chainLlm, and run the same automation in your own workspace.
- Use it as a starting template when wiring manual, lmChatOpenAi, httpRequest, html, informationExtractor, quickChart, chainLlm — adjust node parameters to your accounts and record IDs.
Example output
Imported into n8n, "Spot Workplace Discrimination Patterns with AI" exposes 38 configured nodes. Trigger type: scheduled/triggered. Integrations: manual, lmChatOpenAi, httpRequest, html, informationExtractor, quickChart, chainLlm. Reconnect the manual credential first, then walk the remaining nodes top to bottom. Run it once in test mode to verify any branch logic (Switch/IF nodes) before enabling production triggers.
Tips by platform
Claude
Ask Claude to turn the "Spot Workplace Discrimination Patterns with AI" JSON into a plain-English runbook, node by node. It can flag where manual rate limits or missing retries might bite before you go live.
ChatGPT
Drop the workflow JSON or this OpenRuna page URL into ChatGPT and ask for a step-by-step runbook: triggers, branches, and which fields to customise for manual, lmChatOpenAi, httpRequest, html, informationExtractor, quickChart, chainLlm. Handy for onboarding teammates new to n8n.
Cursor
In Cursor, ask Agent mode to parameterise "Spot Workplace Discrimination Patterns with AI" — extract manual IDs and secrets into environment variables without changing the 38-node structure.
Frequently asked questions
- How do I import "Spot Workplace Discrimination Patterns with AI" into n8n?
- Click Download n8n JSON on this page, then in n8n choose Workflows → Import from file and select the downloaded file. Reconnect credentials on each node — the export carries credential IDs from the original author's instance, not working secrets.
- Which integrations does "Spot Workplace Discrimination Patterns with AI" use?
- It uses: manual, lmChatOpenAi, httpRequest, html, informationExtractor, quickChart, chainLlm. The flow diagram above shows how each service connects across the 38 nodes. Filter OpenRuna by integration to find related workflows and tools.
- Can I import a sanitized export without credentials?
- Yes. Use "Download sanitized JSON" to strip credential references before sharing internally. You will still add your own manual and lmChatOpenAi credentials in n8n after import.
- What trigger type does "Spot Workplace Discrimination Patterns with AI" use?
- Trigger type: scheduled/triggered. After import, confirm the trigger node (webhook, schedule, or app event) matches your environment, then activate the workflow when you are ready to run it.
- Is "Spot Workplace Discrimination Patterns with AI" free to use?
- OpenRuna catalogs community n8n workflows under open licenses. Check the source corpus link for the original license; commercial use may require verifying the third-party API terms for manual, lmChatOpenAi, httpRequest, html, informationExtractor, quickChart, chainLlm.
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