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Release Tag: v1.0 Description: This release provides the first public version of the Dimensional Collapse Echo Search Pipeline, a compact, pedagogical toolkit for exploring gravitational-wave echo signals in binary black hole merger data. The pipeline is designed as a front-end to the Dimensional Collapse Theory (DCT) framework, where the classical black hole interior is replaced by a partially reflective "Ledger" surface. This surface creates a cavity between the photon sphere and the Ledger, leading to delayed echo trains in the post-merger signal. In this release, the detailed interior model is distilled into a single phenomenological parameter $\Phi$, which sets the echo delay as $$ \tau_{\mathrm{echo}} = \Phi,t_M, $$ with $t_M$ the gravitational time scale of the detector-frame remnant mass. The search methods themselves are theory agnostic and can be used with any model that predicts a characteristic echo delay and corresponding frequency comb spacing. The main Jupyter notebook walks the user through the full analysis chain: manually setting DCT-inspired theoretical priors from a GWOSC event summary (final mass, spin, redshift, $\Phi$), loading strain data for H1 and L1 around the event GPS time, bandpass filtering and whitening, gating the primary merger and ringdown, and applying three complementary echo-search strategies, including a frequency-comb based null test with an empirical (p)-value. To keep the workflow reproducible and beginner friendly, this release includes: Echo search notebook – the main step-by-step analysis and visualization interface. User manual (PDF/TeX) – a standalone document explaining the theory priors, data preparation, and interpretation of results, with an appendix on how to compute all notebook inputs directly from GWOSC event tables by hand. Toy echo datasets (examples/toy_echo/noisy_toy_echo_4096Hz.npz) – a small synthetic 4096 Hz time series containing a merger-like ringdown plus a train of echoes with a known delay and comb spacing. This allows users to verify that the pipeline recovers a clear echo signal without downloading any external data. Reference real-event configuration (examples/gw150914/) – a YAML configuration and reference outputs for a standard GW150914-style analysis, including the detector-frame mass scale $t_M$, predicted $\tau_{\mathrm{echo}}$, inferred frequency comb spacing, and summary $p$-values from the three search methods. Environment file – a minimal set of pinned Python package versions (NumPy, SciPy, Matplotlib, and other dependencies) to support reproducible runs. This release is intended primarily as a transparent, educational front-end: all key parameters are set explicitly, intermediate plots are exposed, and no black-box resampling or hidden preprocessing is performed. Future releases may add automated data download and more sophisticated model comparison, but the v1.0 focus is on clarity, manual control, and a clean bridge between DCT-style theoretical priors and practical echo data analysis.