  We introduce swordfish, a Monte-Carlo-free Python package to predict expected
exclusion limits, the discovery reach and expected confidence contours for a
large class of experiments relevant for particle- and astrophysics. The tool is
applicable to any counting experiment, supports general correlated background
uncertainties, and gives exact results in both the signal- and
systematics-limited regimes. Instead of time-intensive Monte Carlo simulations
and likelihood maximization, it internally utilizes new approximation methods
that are built on information geometry. Out of the box, swordfish provides
straightforward methods for accurately deriving many of the common sensitivity
measures. In addition, it allows one to examine experimental abilities in great
detail by employing the notion of information flux. This new concept
generalizes signal-to-noise ratios to situations where background uncertainties
and component mixing cannot be neglected. The user interface of swordfish is
designed with ease-of-use in mind, which we demonstrate by providing typical
examples from indirect and direct dark matter searches as jupyter notebooks.
