Based on prior knowledge, we can analyze the relationship between the "job" feature and the task of determining whether a person receives credit or not.

In general, the type of job a person has can be a strong indicator of their creditworthiness. Certain job categories may indicate a higher income, stability, and financial responsibility, which might increase the likelihood of receiving credit.

Analyzing the given job categories:

1. "skilled": This category typically includes individuals with specialized skills or expertise. They might have higher income and job stability, which could positively impact their creditworthiness. 

2. "unskilled resident": Individuals in this category may have lower income and job stability, making their creditworthiness uncertain. 

3. "high qualif/self emp/mgmt": This category includes individuals with high qualifications, self-employed individuals, or individuals in management positions. These are generally considered favorable job categories, as they often indicate higher income and responsibility.

4. "unemp/unskilled non res": Individuals who are unemployed or engaged in unskilled non-resident work may have lower income and stability, which can negatively impact their creditworthiness.

Based on this analysis, the generated dictionary will be as follows:

```json
{
	"yes": ["skilled", "high qualif/self emp/mgmt"],
	"no": ["unskilled resident", "unemp/unskilled non res"]
}
```

Note: The dictionary only includes the job categories that have a clear relationship with the target variable. It does not include categories where the impact on creditworthiness is hard to predict.