We present “Did I find it?”, difi, a well-tested and user-friendly Python QA framework designed to provide informative data products and summary statistics for evaluating the performance of small body linking algorithms. Discovering Solar System small bodies involves linking observations over multiple nights into orbits. Examples of algorithms that complete this task include the Vera C. Rubin Observatory’s HelioLinC3D (Eggl et al. 2020, in prep), Pan-STARRS’ Moving Object Processing System (MOPS) (Denneau et al. 2013), and novel algorithms such as HelioLinC (Holman et al. 2018), and Tracklet-less Heliocentric Orbit Recovery (Moeyens et al. 2020, reviewed). Given a set of observations containing detections of Solar System objects, each algorithm aims to produce a set of possible linkages that enable the discovery of these Solar System objects. For example, MOPS may produce intra-night linkages known as tracklets which are linked into larger inter-night linkages known as tracks. HelioLinC builds possible linkages called arrows and then links arrows into another set of possible linkages called clusters. While developing and deploying these small body linking algorithms it is important to understand how well they perform in terms of overall completeness and completeness at the population level: how many small bodies were found? How many NEOs were recovered of those deemed discoverable? How many MBAs were found or not found? For computational efficiency and feasibility it is important to understand the quality of the possible linkages produced: how many linkages contain sufficient observations that can be used for discovery? How many linkages are erroneous? Understanding how individual small bodies affect these algorithms can also be important: how many times does a particular small body appear in erroneous linkages? Were all of this small body’s observations recovered in a linkage? Given a set of observations and a set of possible linkages produced by any small body linking algorithm, difi is designed to answer these questions with concise and easy to understand data products and summary statistics. The difi code with installation instructions and usage examples can be found on GitHub at https://github.com/moeyensj/difi.