TESS-cont quantifies the flux fraction coming from nearby stars in the TESS photometric aperture of any observed target. The package identifies the main contaminant Gaia DR2/DR3 sources, quantifies their individual and total flux contributions to the aperture, and determines whether any of these stars could be the origin of the observed transit and variability signals. Written in Python, TESS-cont is based on building the pixel response functions (PRFs) of nearby Gaia sources and computing their flux distributions across the TESS Target Pixel Files (TPFs) or Full Frame Images (FFIs).

pycheops analyzes CHEOPS light curve data. The models in the package can also be applied to other types of data. pycheops includes a "cook book" and examples; in addition, it provides a command-line tool that aids in the preparation of observing requests for CHEOPS observers.

The end-to-end SHERLOCK (Searching for Hints of Exoplanets fRom Lightcurves Of spaCe-based seeKers) pipeline allows users to explore data from space-based missions to search for planetary candidates. It can recover alerted candidates by the automatic pipelines such as SPOC and the QLP, Kepler objects of interest (KOIs) and TESS objects of interest (TOIs), and can search for candidates that remain unnoticed due to detection thresholds, lack of data exploration, or poor photometric quality. SHERLOCK has six different modules to perform its tasks; these modules can be executed by filling in an initial YAML file with some basic information and using a few lines of code sequentially to pass from one step to the next. Alternatively, the user may provide with the light curve in a csv file, where the time, normalized flux, and flux error are provided in columns in comma-separated format.

nuance uses linear models and Gaussian processes to simultaneously search for planetary transits while modeling correlated noises (e.g., stellar variability). The code computes the likelihood of a transit being present in some correlated noise without disentangling the two; it searches the transit signal while simultaneously modeling correlated noise, assuming that the light curve can be modeled as a Gaussian process. nuance detects single or periodic transits and can find transits in light curves from multiple instruments, whether space-based or ground-based; it can also run in parallel on CPUs or GPUs.

WATSON (Visual Vetting and Analysis of Transits from Space ObservatioNs) enables a comfortable visual vetting of transiting signal candidates from Kepler, K2, and TESS missions. The code looks for transit-like signals that could be generated by other sources or instrument artifacts and runs simplified tests on scenarios including transit shape model fit, odd-even transits checks, and centroids shifts. It also considers optical ghost effects, transit source offsets, and several other scenarios. WATSON then computes metrics and flags problematic signals.

The injection-recovery MATRIX (Multi-phAse Transits Recovery from Injected eXoplanets) Toolkit creates grids of scenarios with a set of periods, radii, and epochs of synthetic transiting exoplanet signals in a provided light curve. Typical injection-recovery executions consist of 2-dimensional scenarios, where only one epoch (random or hardcoded) was used for each period and radius, which may reduce accuracy. MATRIX performs multi-phase analyses needing only a few parameters in a configuration file and running one line of code.

prose provides pipelines for performing common tasks, such as automated calibration, reduction and photometry, and makes building custom pipelines easy. The prose framework is instrument-agnostic and makes constructing pipelines easy. It offers a wide range of implemented building blocks and also allows users to define their own.