These scripts are development and evaluation tools, not installed CLI commands. Run them from the repository root with uv run python scripts/<name>.py --help. Inputs under .local-eval/ and generated reports remain untracked unless a data README explicitly names a tracked canonical result.

Audits and release checks

Script	Purpose
`corpus_gap_scan.py`	Compare a local image corpus with the library's `identify` results.
`detection_timing.py`	Record per-method metadata and verdict timings.
`detection_timing_report.py`	Aggregate timing records by method and segment.
`fidelity_metrics.py`	Compute objective image-fidelity metrics for paired outputs.
`invisible_quality_audit.py`	Pair originals and invisible-removal outputs for quality review.
`metadata_removal_audit.py`	Check metadata detection/removal parity over a corpus.
`pill_gate_audit.py`	Measure the Jimeng pill detector on the product path.
`real_examples_e2e.py`	Run end-to-end confidence checks over local examples.
`record_parity_audit.py`	Compare record-based and file-based identification.
`resource_ceilings.py`	Measure peak RSS and runtime for fill backends.
`robustness_suite.py`	Exercise CLI failures on adversarial and degenerate inputs.
`sidecar_regression.py`	Compare current identification with recorded sidecars.
`smoke_matrix.py`	Exercise CLI parameter choices on real local data.
`video_fidelity_probe.py`	Compare delivered video fidelity with its source.
`visible_eval.py`	Benchmark registered visible-mark detectors.
`visible_removal_audit.py`	Audit visible-removal results over a local corpus.

Calibration and corpus preparation

Script	Purpose
`contentseal_transforms.py`	Reproduce and hash-check deterministic Content Seal variants.
`detector_response.py`	Measure detector response over mark size, contrast, background, and aspect.
`fill_quality.py`	Measure visible-fill quality against constructed ground truth.
`ladder_headroom.py`	Measure recall cost from the coarse scale ladder.
`registered_mark_calibrate.py`	Measure a registered detector without conflating visual positives, metadata cohorts, adjudicated negatives, and unlabeled controls.
`synthid_corpus.py`	Ingest and inspect the local SynthID reference corpus.
`vendor_cohort_harvest.py`	Partition TC260 carriers by producer code.
`vendor_mark_calibrate.py`	Calibrate a candidate vendor text detector.
`visible_alpha_solve.py`	Rebuild visible-watermark alpha assets from controlled captures.
`visible_groundtruth.py`	Consolidate blinded contact-sheet labels into ground truth.
`visible_positives.py`	List corpus images carrying a registered visible mark.
`visible_recall_sample.py`	Build an unbiased blinded sample for recall measurement.
`visible_sheets.py`	Build blinded contact sheets for relaxation candidates.

Research and diagnostic prototypes

Script	Purpose
`cjk_tail_probe.py`	Test a generic template for otherwise uncovered CJK labels.
`controlnet_sweep.py`	Sweep the historical ControlNet removal prototype.
`infer_text_lines.py`	Draft stable source-text lines without modifying pixels.
`qwen_scrub_prototype.py`	Probe low-strength Qwen regeneration on a GPU.
`selective_text_restoration.py`	Evaluate text restoration over a scrubbed image.
`synthid_pixel_probe.py`	Run the experimental local SynthID carrier probe.
`video_synthid_sweep.py`	Build oracle-gated video regeneration candidates.

Generated assets

Script	Purpose
`render_pill_silhouette.py`	Render the synthetic Jimeng pill silhouette.
`render_vendor_silhouettes.py`	Render synthetic vendor text-mark silhouettes.

Shared helpers

_plain_console.py provides plain-text fallbacks for Rich output, and _text_eval.py contains normalization helpers shared by text-evaluation scripts.