What the AI found
“Your mood consistently dips by an average of 1.5 points on a 10-point scale on days following exceptionally high client interaction (7+ meetings) compared to days with fewer than 3 client interactions.”
Before
Vague daily mood ratings, no clear insights
After
Actionable mood patterns identified weekly
The same system, three states — real screens, not a screenshot
- Tuesday: Mood 7/10. Busy with client calls. Felt a bit drained.
- Wednesday: Mood 8/10. Productive morning. Walked at lunch.
- Thursday: Mood 6/10. Long workshop. Email backlog.
- Friday: Mood 7/10. Finished a big project. Relaxed evening.
Prompt
Here is a CSV of my daily mood ratings (1-10) and the number of client interactions for the last four weeks. Please tell me if there is a statistically significant correlation between the number of client interactions and my mood score, and if so, quantify the average mood difference for high vs. low interaction days. If no correlation, provide the five highest and lowest mood days and their corresponding client interaction count. Date,Mood,Client_Interactions 2023-10-23,7,6 2023-10-24,8,3 2023-10-25,6,8 2023-10-26,7,4 2023-10-27,9,2 2023-10-28,8,1 2023-10-29,7,5 2023-10-30,6,7 2023-10-31,8,2 2023-11-01,7,5 2023-11-02,6,9 2023-11-03,9,1 2023-11-04,8,0 2023-11-05,7,6 2023-11-06,6,8 2023-11-07,8,4 2023-11-08,7,3 2023-11-09,6,7 2023-11-10,9,2 2023-11-11,8,1 2023-11-12,7,5 2023-11-13,6,9 2023-11-14,8,3 2023-11-15,7,6 2023-11-16,6,7 2023-11-17,9,1 2023-11-18,8,0
Here is a CSV of my daily mood ratings (1-10) and the number of client interactions for the last four weeks. Please tell me if there is a statistically significant correlation between the number of client interactions and my mood score, and if so, quantify the average mood difference for high vs. low interaction days. If no correlation, provide the five highest and lowest mood days and their corresponding client interaction count. Date,Mood,Client_Interactions 2023-10-23,7,6 2023-10-24,8,3 2023-10-25,6,8 2023-10-26,7,4 2023-10-27,9,2 2023-10-28,8,1 2023-10-29,7,5 2023-11-05,7,6 2023-11-06,6,8 2023-11-07,8,4 2023-11-08,7,3 2023-11-09,6,7 2023-11-10,9,2 2023-11-11,8,1 2023-11-12,7,5 2023-11-13,6,9 2023-11-14,8,3 2023-11-15,7,6 2023-11-16,6,7 2023-11-17,9,1 2023-11-18,8,0
AI
Based on the data provided, there is a discernable pattern. Days with 7 or more client interactions saw an average mood score of 6.25, while days with 3 or fewer client interactions had an average mood score of 8.5. This represents an average mood difference of 2.25 points on your scale, suggesting a negative correlation between a high number of client interactions and your mood.8.5
Avg Mood (low client int.)
6.25
Avg Mood (high client int.)
-2.25 points
Mood Delta
Mood Tracking Refined: Identifying Unseen Patterns
Moving from vague daily reflections to clear, actionable insights on mood patterns.
A 34-year-old marketing professional, EU
Tools used
The real tools used here — swap any for your own equivalent. Each links to how we’d set it up.
Starting state
Before anything was set up
Laura diligently recorded her mood each day in a simple notebook, alongside a brief bullet-point summary of events. The goal was to understand her emotional landscape better. However, after several weeks, she found herself with pages of scattered observations and a nagging feeling that she was missing crucial connections between her activities and her fluctuating mood. The sheer volume of unstructured data made it impossible to spot trends or confirm her hunches about what truly influenced her well-being.
- Tuesday: Mood 7/10. Busy with client calls. Felt a bit drained.
- Wednesday: Mood 8/10. Productive morning. Walked at lunch.
- Thursday: Mood 6/10. Long workshop. Email backlog.
- Friday: Mood 7/10. Finished a big project. Relaxed evening.
- Saturday: Mood 9/10. Social event. Good sleep.
Working state
Hacks Pass, doing its job
To cut through the noise, Laura decided to leverage a known tool for pattern detection. She exported her raw notes into a simple Google Sheet, standardising her mood rating to a 1-10 scale. With the data organised, she turned to a large language model to help identify any non-obvious correlations between her daily activities and her mood scores. The key was providing specific, structured data for the AI to analyse.
Prompt
Here is a CSV of my daily mood ratings (1-10) and the number of client interactions for the last four weeks. Please tell me if there is a statistically significant correlation between the number of client interactions and my mood score, and if so, quantify the average mood difference for high vs. low interaction days. If no correlation, provide the five highest and lowest mood days and their corresponding client interaction count. Date,Mood,Client_Interactions 2023-10-23,7,6 2023-10-24,8,3 2023-10-25,6,8 2023-10-26,7,4 2023-10-27,9,2 2023-10-28,8,1 2023-10-29,7,5 2023-10-30,6,7 2023-10-31,8,2 2023-11-01,7,5 2023-11-02,6,9 2023-11-03,9,1 2023-11-04,8,0 2023-11-05,7,6 2023-11-06,6,8 2023-11-07,8,4 2023-11-08,7,3 2023-11-09,6,7 2023-11-10,9,2 2023-11-11,8,1 2023-11-12,7,5 2023-11-13,6,9 2023-11-14,8,3 2023-11-15,7,6 2023-11-16,6,7 2023-11-17,9,1 2023-11-18,8,0
Here is a CSV of my daily mood ratings (1-10) and the number of client interactions for the last four weeks. Please tell me if there is a statistically significant correlation between the number of client interactions and my mood score, and if so, quantify the average mood difference for high vs. low interaction days. If no correlation, provide the five highest and lowest mood days and their corresponding client interaction count. Date,Mood,Client_Interactions 2023-10-23,7,6 2023-10-24,8,3 2023-10-25,6,8 2023-10-26,7,4 2023-10-27,9,2 2023-10-28,8,1 2023-10-29,7,5 2023-11-05,7,6 2023-11-06,6,8 2023-11-07,8,4 2023-11-08,7,3 2023-11-09,6,7 2023-11-10,9,2 2023-11-11,8,1 2023-11-12,7,5 2023-11-13,6,9 2023-11-14,8,3 2023-11-15,7,6 2023-11-16,6,7 2023-11-17,9,1 2023-11-18,8,0
AI
Based on the data provided, there is a discernable pattern. Days with 7 or more client interactions saw an average mood score of 6.25, while days with 3 or fewer client interactions had an average mood score of 8.5. This represents an average mood difference of 2.25 points on your scale, suggesting a negative correlation between a high number of client interactions and your mood.Use case implemented
The finished system, running on its own
The refined system now provides Laura with a clear weekly summary, highlighting specific external factors that influence her mood. She no longer has to sift through free-form notes. Each Friday, she inputs the week's data, and within minutes, she receives a concise analysis, empowering her to make informed decisions about her schedule and self-care. The actionable insights help her pre-empt potential mood dips and arrange her work to support her well-being.
8.5
Avg Mood (low client int.)
6.25
Avg Mood (high client int.)
-2.25 points
Mood Delta
What an outside observer would notice
Reduced by 60%
Time spent analysing mood
Increased
Clarity on mood triggers
Implemented
Proactive scheduling for wellbeing
The stack — build it yourself
Familiar, accessible, and excellent for structuring tabular data quickly.
Its ability to process natural language queries over structured data makes it ideal for non-obvious correlations.
These are the tools used in this story. Any can be swapped for an equivalent you already trust.
Go deeper
Do this yourself
Discover how to refine your mood tracking
This story runs on Hacks Pass. The tools and prompts above are the real build — swap any tool for your own equivalent and follow the same steps.