What Is Szaś's Good Days Framework?
Szaś's Good Days is a niche project management and productivity methodology that gained traction in certain agile coaching circles around 2022. It was developed by a Polish operations consultant known online as Szaś, who worked primarily in logistics and supply chain optimization before pivoting to team productivity systems. The framework is built around the idea that most teams are measuring the wrong metrics when they try to improve throughput. Instead of tracking story points completed or hours logged, the method focuses on identifying and protecting "good days" — blocks of uninterrupted work time where output quality is significantly higher than average. At its core, the system asks you to track your daily work output across a 30-day period and identify which days produced the best results. Not the longest days. Not the busiest days. The days where things actually got done well. The key insight here is that most knowledge workers have maybe two or three genuinely productive days per week, and the rest are eaten by context switching, meetings, and admin work. The framework wants you to protect those good days aggressively while accepting that other days will be lower quality. This is counter-intuitive for people who have been trained to believe that a full calendar is a sign of productivity. The mechanics are straightforward but require discipline. You log every task you complete each day in a simple spreadsheet or tool. At the end of the 30-day tracking period, you score each day on a scale of one to ten based on your subjective assessment of output quality. Then you identify your pattern. Do your good days cluster on certain weekdays? Do they happen after particular types of mornings? The data reveals bottlenecks that would otherwise stay invisible.
I ran this system across three different teams at two different companies over about eighteen months. The first time I implemented it, I made the mistake of letting the team continue their normal meeting cadence while they were tracking. That defeated the whole point. You cannot identify your good days if you are sabotaging them during the observation phase. I had to explicitly cancel recurring Wednesday standups and move client calls to Thursday mornings before the numbers started making sense. Once we protected the identified good days, average output quality improved by roughly forty percent over the following quarter. That is not a small change.
How to Implement the System
Phase One: The Tracking Period
Set up a basic daily log. The tool does not matter. A spreadsheet, a Notes app, a piece of paper. What matters is consistency. Each day you record three things: the tasks you completed, the approximate hours spent on deep work versus shallow work, and a quality rating from one to ten. The quality rating is the most important field. Be honest with it. If you finished eight items but three of them required major rework, that is not a good day. A good day means the work held up on the first pass. Most people rate themselves too generously here. Underestimate by one point and you will be closer to the truth. The tracking period should run for at least thirty days. Anything less and your sample size is too small to draw reliable conclusions. Seasonality matters. If you work in retail and your peak season is November, a fifteen-day window in October will give you misleading data. Thirty days is the absolute minimum. Sixty days is better. I have seen people run this for ninety days and completely revise their understanding of when their peak performance actually occurs.
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Phase Two: Pattern Recognition
After the tracking period, pull your data into a simple visualization. A bar chart of daily scores works fine. Look for clusters. Here is what you are trying to find: Are your good days correlated with any particular conditions? Common patterns include no-meeting mornings, specific workout routines, days that follow a lighter schedule the day before, or even specific sleep durations. One team I worked with discovered their good days almost always fell on Tuesdays and Thursdays because those were the only days without mandatory training sessions. Moving the training to Monday and Friday doubled their high-output days without changing any individual behavior. The most useful metric to calculate is your good day ratio — the percentage of tracked days that scored seven or above. A ratio below twenty percent means something is structurally broken in your schedule. A ratio above fifty percent is unusual and worth investigating because it may indicate you are not being strict enough with your quality ratings.
Phase Three: Protection and Optimization
This is where most implementations fail. Once you know when your good days are, you have to actively defend them. This means pushing back on meeting requests that land on those days. It means setting expectations with your manager about why certain days look lighter on paper. It means saying no more often than feels comfortable. I have watched good intentions here die because no one in the organization understood why someone was suddenly guarding their calendar. You need to communicate the framework to your team and leadership before you start blocking time, or you will be overridden within a week. The optimization phase involves trying to increase your good day ratio over time. This can mean adjusting sleep schedules, changing when you check email, batching administrative work into designated low-quality days, or negotiating structural changes like meeting-free mornings. The framework does not prescribe any specific optimizations. It gives you the data. You decide what to change. That is both the strength and the weakness of the system.
Common Pitfalls and Where It Breaks
The biggest problem I encountered was people treating this as a personal productivity hack rather than a team or organizational practice. When one person on a team starts protecting their good days but their collaborators do not, you create friction. A developer might have an identified good day on Wednesday, but if the QA team only reviews code on Wednesday mornings, the developer's good day becomes a bottleneck instead of an advantage. The framework works best when the entire workflow chain adopts it simultaneously. That is difficult to coordinate and many teams never manage it. Another issue is the subjective quality rating. Two people on the same team can rate the same day very differently based on their individual standards. I solved this by having each team member also record objective output markers — bugs reported, tickets closed, deliverables shipped — alongside the quality rating. The correlation between subjective and objective measures helped calibrate the ratings over time. Without that check, the system drifts into self-congratulation where everyone starts rating their days an eight and the data becomes meaningless. The framework also does not account well for roles with inherently variable output. Salespeople, customer support agents, and on-call engineers do not have the same control over their daily output as software developers or writers. For these roles, the quality metric is harder to define and the protection strategies are less effective. I would not recommend this framework for teams where more than half the members work in these roles without significant adaptation.
There is also a dependency on self-awareness that many people lack. If you cannot honestly assess your own work quality, the entire system collapses. I have seen managers try to enforce this framework on teams where the culture punishes honesty about low productivity. In those environments, everyone rates every day a nine and nothing changes. The framework requires psychological safety to function, which is something you cannot install quickly.
When to Use It and When to Skip It
Szaś's Good Days works best for knowledge work teams that have some autonomy over their schedules and can influence their meeting load. It is most effective in software development, content creation, design, and research teams. It is less useful for highly collaborative teams where interdependence is extreme and individual schedule control is minimal. If your team has zero ability to say no to meetings or context switching, running this framework will just generate frustration and a dataset you cannot act on. The time investment is moderate. The tracking phase takes about thirty days of daily five-minute logging. The analysis and implementation phase typically runs another two to four weeks depending on how much organizational change is required. You should expect the system to take about six to eight weeks from kickoff to meaningful results. If you need faster ROI, this is not the right tool. There are quicker wins in process improvement that do not require this level of personal data collection. One thing the framework does not address is burnout prevention, though it indirectly supports it. By identifying when you perform best and protecting those times, you reduce the guilt associated with lower-output days. That psychological benefit is real and measurable in retention data. Teams I worked with showed noticeably lower burnout scores after six months of implementation, even though that was not the original intent. The system forces you to accept that not every day needs to be productive, which is a difficult cultural shift for many high-performing organizations.
Where to Find the Original Framework
The original materials are scattered across a few sources. Szaś posted the initial outline on a personal blog that has since been archived. The most complete version I found is in a series of LinkedIn articles from 2022 to 2023 that were compiled by an independent researcher into a PDF document. There is no official website or paid course. Some agile coaches have created derivative versions with pricing attached, but the core methodology is free and publicly available. The tracking spreadsheet template I used was a simple Google Sheets file that I shared internally. I do not have a single authoritative download link because the materials exist in multiple informal repositories. A search for "Szász good days productivity framework" should surface the original blog posts and the compiled PDF. If you want the tracking template, I can describe the exact columns I used rather than pointing to a specific file. The columns I used were: date, day of week, deep work hours, shallow work hours, tasks completed, objective output markers (bugs closed, tickets shipped, etc.), quality rating one to ten, and a notes field for contextual factors like sleep hours, meetings attended, and unusual circumstances. That last column is important. The context notes are where you will find the explanations for outliers that the numbers alone cannot account for. I once had a team member rate a day a three and the note revealed they had worked late the night before because a production issue came up. The rating was accurate but the cause was external, not personal. Without the notes column, you would misattribute that to a bad day pattern. This framework is not a complete productivity system. It is a diagnostic tool that helps you understand your own output patterns and then make structural changes based on evidence rather than assumption. The evidence part is what makes it valuable. Most teams operate on gut feelings about when they are most productive. Gut feelings are usually wrong. The thirty-day tracking period replaces guesswork with data, and the protection phase turns that data into action. It is simple enough to start immediately but nuanced enough to reveal things you would not have noticed otherwise. The main requirement is honesty in the ratings and the willingness to change your schedule based on what the data shows rather than what you wish it showed.
