Background The success of the Community Based Management of Severe Malnutrition (CSAM) programme largely depends on the knowledge and skills of Front-Line Workers (FLWs). A robust supportive supervision system in CSAM should be tailored to individualistic learning needs by distinguishing the FLWs as per their ability and simultaneously identifying the task domains to be emphasized more in supervisory visits. This paper details the ability assessment strategy developed and employed in the selected geographical locations in Madhya Pradesh (Central India) among the 197 Anganwadi workers (FLWs involved in CSAM implementation). Methodology A 25 items tool was developed based on an analytical construct for ability estimation through Rasch Analysis (RA). RA models the probability of right/wrong answers as a function of a person (participants) and item (questions) parameters and calculates the item difficulty in relation to personability on the same unidimensional linear scale. Suitable visualization like item characteristic curve (ICC), person item map (PIM) and quadratic allocation were plotted in RA. The data fitting to the Rasch model (Rasch diagnostic) was tested by numeric (Anderson LR and Wald test) and graphical methods. Results The item easiness parameter (β) value related to Diarrhoeal assessment was lowest (-2.32, -2.91 to -1.73) and related to peer assessment meaningful action (2.009, 1.669- 2.348)) was highest (most difficult). Anderson LR test (LR=31.32, df=24, p=0.079) showed the absence of global outliers. Quadrant analysis using the permutations of ability score and adjusted burden of malnutrition further mapped 41/197 (20.8%) FLWs to low ability -high burden quadrant and 44/197(25%) as low ability low burden quadrant. Conclusion Rasch assessment may address the innate challenges to maintain homogeneity, discrimination capacity and linearity in a raw score-based measurement construct. The monitoring strategy developed on this thus may offer a judicious, pragmatic and thematic approach to supportive supervision in the CSAM program.