feat(analysis): make resource budgets recoverable across CLI and MCP (#9706)

* refactor(analysis): centralize resource budgets and recovery facts

* feat(analysis): expose ASAR and Ghidra runtime admission controls

* feat(artifacts): expose reader admission and preserve limited recognition

* feat(javascript): expose recoverable semantic precision budgets

* feat(web): add recoverable source-map budgets and compact retained views

* feat(web): make historical capture decoding budgets recoverable

* fix(desktop): enforce selected view policies across capture and export

* feat(runtime): make Frida and LLDB retention budgets selectable

* feat(native): expose diagnostic budgets and preserve exhaustion causes

* feat(managed): expose recoverable snapshot and representation budgets

* feat(metadata): make Go and Rust inspection budgets recoverable

* feat(wasm): expose representation and producer admissions

* feat(evm): expose per-call worker resource budgets

* feat(android): make resource and string-pool omissions recoverable

* feat(producers): expose command and recorded-capture budgets

* docs(analysis): record recovery contracts and integrated validation

* fix(docs): link calibration oracle from published verification pages

* fix(docs): restore desktop task anchors and receipt links

* test(analysis): align fixtures with selectable budget contracts

* fix(analysis): remove source-location projector dependency cycle

* test(analysis): refresh budget recovery and verifier regressions

* test(analysis): preserve format and publication recovery expectations

* test(windows): align conformance with owned runtime admission

* fix(desktop): preserve selected alias kinds in exported state

* test(analysis): align provider fixtures with recoverable budgets

* test(desktop): validate emitted alias metadata structurally

* fix(ghidra): classify bounded Windows endpoint refusals

* test(cli): assert selectable artifact admission recovery

* fix(cli): preserve capture provenance in environment builds

scripts/verify/analysis/javascript-precision-calibration/aggregate.py

@@ -0,0 +1,59 @@
+"""Aggregate retained observer receipts and complete CLI records; needs tiktoken."""
+
+import argparse
+import json
+from pathlib import Path
+import statistics
+
+import tiktoken
+
+
+def main():
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument('inputs', type=Path)
+    parser.add_argument('output', type=Path)
+    options = parser.parse_args()
+    encoder = tiktoken.get_encoding('cl100k_base')
+    def compact(value):
+        return json.dumps(value, ensure_ascii=False, separators=(',', ':'))
+    rows = []
+    for path in sorted(options.inputs.glob('*.metrics.json')):
+        metrics = json.loads(path.read_text())
+        record = json.loads(path.with_name(path.name.replace('.metrics.json', '.replies.json')).read_text())
+        summary = json.loads(path.with_suffix('.stdout').read_text())
+        tokens = {
+            'arguments': len(encoder.encode(compact(record['argv']))),
+            'reply': len(encoder.encode(compact(json.loads(record['stdout'])))),
+            'progress_stderr': len(encoder.encode(record['stderr'])),
+        }
+        rows.append({
+            'name': path.stem.removesuffix('.metrics'),
+            'task': summary['case'], 'budget': summary['budget'],
+            'verified_facts': summary['verified_facts'], 'unknowns': summary['unknowns'],
+            'wall_s': metrics['elapsed_seconds'],
+            'cpu_s': metrics['cpu_seconds_waited_subtree'],
+            'host_group_rss_bytes': metrics['peak_process_group_rss_bytes_sampled'],
+            'tokens': tokens, 'reply_bytes': summary['reply_bytes'],
+            'exit_code': metrics['exit_code'], 'cleanup': metrics['cleanup'],
+            'stop_reason': metrics['stop_reason'],
+        })
+    medians = []
+    for task in ['theme', 'ms']:
+        for budget in ['default', 'double']:
+            selected = [row for row in rows if row['task'] == task and row['budget'] == budget]
+            if not selected:
+                continue
+            medians.append({
+                'task': task, 'budget': budget, 'trials': len(selected),
+                'facts': selected[0]['verified_facts'], 'unknowns': selected[0]['unknowns'],
+                'wall_s': statistics.median(row['wall_s'] for row in selected),
+                'cpu_s': statistics.median(row['cpu_s'] for row in selected),
+                'host_group_rss_mib': statistics.median(row['host_group_rss_bytes'] / 1048576 for row in selected),
+                'tokens': statistics.median(sum(row['tokens'].values()) for row in selected),
+                'reply_bytes': selected[0]['reply_bytes'],
+            })
+    options.output.write_text(json.dumps({'rows': rows, 'medians': medians}, indent=2) + '\n')
+
+
+if __name__ == '__main__':
+    main()