refactor+feat: Snapshot-Router-Split + generischer ChecklistAgent + AGB-Modul
- Item 2: Snapshot-Doc-Checks (cookie/impressum/dse/agb) in snapshot_check_routes.py (agent_compliance_check_routes.py 464→365 Z.); gleiche Pfade, in main.py registriert. - ChecklistAgent-Basis: DSE-Logik generalisiert (L1/L2, kurze Titel, _severity_ override-Hook). DSEAgent + AGBAgent sind jetzt Thin-Subclasses → künftige Doc-Agenten (widerruf/avv/…) trivial. - Item 4: AGBAgent (§§ 305 ff. BGB, AGB_CHECKLIST) + agb-check + AGB-Tab via AgentModuleTab. Kein Library-Firehose. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
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Benjamin Admin
@@ -1,180 +1,29 @@
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"""DSEAgent — Datenschutzerklärung / Datenschutzinformation (Art. 13/14 DSGVO).
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Kuratiert: läuft die ART13_CHECKLIST (Pflichtangaben L1 „erwähnt?" +
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Detailchecks L2 „vollständig?") deterministisch über den DSE-Text. BEWUSST
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KEIN Library-Firehose (eCall/Gesundheit/Telekom/Data-Act-Lärm aus der 90k-
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Control-Library) — nur die echten Art-13/14-Auskunftspflichten. Output =
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AgentOutput (mc_coverage + Findings + Maßnahmen), gerendert im AgentResultTab
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wie das Impressum-Modul.
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Thin-Subclass von ChecklistAgent über die kuratierte ART13_CHECKLIST (KEIN
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90k-Library-Firehose). Einzige Spezialität: Drittland wird bei dokumentiertem
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Drittlandtransfer (Scan-Kontext) zu HIGH angehoben.
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"""
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from __future__ import annotations
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import re
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from datetime import datetime, timezone
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from compliance.services.doc_checks.dse_checks import ART13_CHECKLIST
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from .._base import (
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AgentInput,
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AgentOutput,
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BaseSpecialistAgent,
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CheckStatus,
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EvidenceSource,
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Finding,
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McCoverage,
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Severity,
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SourceType,
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lint_output,
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)
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from .._rollup import rollup
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_SEV = {"HIGH": Severity.HIGH, "MEDIUM": Severity.MEDIUM,
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"LOW": Severity.LOW, "INFO": Severity.INFO}
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# Coverage-Status bei FAIL spiegelt die Risiko-Achse (severity) der Quelle.
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_COV_FAIL = {"HIGH": "high", "MEDIUM": "medium", "LOW": "low", "INFO": "low"}
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_NORM_RE = re.compile(r"\((Art\.[^)]+)\)")
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from .._base import AgentInput
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from .._checklist_agent import ChecklistAgent
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def _match_value(text: str, start: int, end: int) -> str:
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"""Exakter Treffer-Wert (nicht die umgebende Passage), normalisiert + gekappt."""
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return " ".join(text[start:end].split())[:120]
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def _norm_of(label: str) -> str:
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m = _NORM_RE.search(label or "")
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return m.group(1) if m else "Art. 13/14 DSGVO"
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def _compiled(check: dict) -> list:
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out = []
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for p in check.get("patterns", []):
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try:
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out.append(re.compile(p, re.IGNORECASE | re.MULTILINE))
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except re.error:
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continue
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return out
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def _search(patterns: list, text: str):
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for p in patterns:
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m = p.search(text)
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if m:
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return m
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return None
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class DSEAgent(BaseSpecialistAgent):
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class DSEAgent(ChecklistAgent):
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CHECKLIST = ART13_CHECKLIST
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agent_id = "dse"
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agent_version = "1.0"
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doc_type = "dse"
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owned_mc_ids = tuple(c["id"] for c in ART13_CHECKLIST)
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async def evaluate(self, agent_input: AgentInput) -> AgentOutput:
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start = datetime.now(timezone.utc)
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text = (agent_input.text or "").strip()
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def _severity_override(self, c: dict, agent_input: AgentInput):
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sc = (agent_input.context or {}).get("scan_context") or {}
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tc_applies = str(sc.get("third_country_transfer", "")).lower() in (
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tc = str(sc.get("third_country_transfer", "")).lower() in (
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"yes", "true", "1", "ja")
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coverage: list[McCoverage] = []
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findings: list[Finding] = []
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if len(text) < 100:
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for c in ART13_CHECKLIST:
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coverage.append(McCoverage(
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mc_id=c["id"], status="skipped",
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label=c["label"], reason="Text zu kurz"))
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return self._finalize(start, findings, coverage, 0.0,
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"DSE-Text zu kurz oder leer.")
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# L1 (Pflichtangabe erwähnt?) zuerst — Ergebnis steuert L2.
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l1_present: dict[str, bool] = {}
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for c in ART13_CHECKLIST:
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if c.get("level", 1) != 1:
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continue
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m = _search(_compiled(c), text)
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l1_present[c["id"]] = m is not None
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coverage.append(self._cov(c, m, text, tc_applies))
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if m is None:
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findings.append(self._finding(c, False, tc_applies))
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# L2 (vollständig/korrekt?) — nur wenn die übergeordnete L1 da ist. Fehlt
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# die L1, deckt deren Finding die Lücke ab → KEIN irreführendes 'na'
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# (nicht anwendbar) für das Detail (z.B. Transfermechanismus bei BMW).
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for c in ART13_CHECKLIST:
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if c.get("level", 1) != 2:
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continue
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parent = c.get("parent")
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if parent and not l1_present.get(parent, False):
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continue
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m = _search(_compiled(c), text)
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coverage.append(self._cov(c, m, text, tc_applies))
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if m is None:
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findings.append(self._finding(c, True, tc_applies))
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return self._finalize(start, findings, coverage, 0.7, "")
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@staticmethod
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def _eff_sev(c: dict, tc_applies: bool) -> str:
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"""Drittland ist bei dokumentiertem Drittlandtransfer (Scan-Kontext)
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keine weiche MEDIUM-Empfehlung mehr, sondern HIGH (Konzern/US-Provider)."""
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if tc_applies and c["id"] in ("third_country", "third_country_mechanism"):
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if tc and c["id"] in ("third_country", "third_country_mechanism"):
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return "HIGH"
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return c.get("severity", "MEDIUM")
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def _cov(self, c: dict, m, text: str, tc_applies: bool) -> McCoverage:
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if m is not None:
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return McCoverage(
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mc_id=c["id"], status="ok", label=c["label"],
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reason="Pattern-Treffer",
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found=_match_value(text, m.start(), m.end()))
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sev = self._eff_sev(c, tc_applies)
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return McCoverage(
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mc_id=c["id"], status=_COV_FAIL.get(sev, "medium"),
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label=c["label"],
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reason="fehlt" if c.get("level", 1) == 1 else "Detail unvollständig")
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def _finding(self, c: dict, present: bool, tc_applies: bool) -> Finding:
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sev = self._eff_sev(c, tc_applies)
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# Titel + Maßnahme bewusst KURZ (treibt den Recommendation-Titel); die
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# ausführliche Begründung steht als evidence auf der Finding-Karte.
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title = (f"{c['label']}: Detail unvollständig" if present
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else f"{c['label']} fehlt")
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action = (f"{c['label']} präzisieren." if present
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else f"{c['label']} in der Datenschutzerklärung ergänzen.")
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return Finding(
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check_id=f"DSE-{c['id']}",
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agent=self.agent_id, agent_version=self.agent_version,
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field_id=c["id"], status=CheckStatus.FAIL,
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severity=_SEV.get(sev, Severity.MEDIUM),
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severity_reason=("detail_incomplete" if present
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else "pflichtangabe_missing"),
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title=title, norm=_norm_of(c["label"]),
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action=action, evidence=(c.get("hint") or "")[:280], confidence=0.7,
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sources=[EvidenceSource(
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source_type=SourceType.REGEX, source_id=c["id"],
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detail="kein Pattern-Treffer", confidence=0.7)],
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)
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def _finalize(self, start, findings, coverage, confidence, notes):
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end = datetime.now(timezone.utc)
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recs = rollup([f for f in findings
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if f.status == CheckStatus.FAIL.value])
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out = AgentOutput(
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agent=self.agent_id, agent_version=self.agent_version,
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started_at=start, finished_at=end,
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duration_ms=int((end - start).total_seconds() * 1000),
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findings=findings, recommendations=recs, mc_coverage=coverage,
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confidence=confidence, notes=notes,
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mc_total=len(coverage),
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mc_ok=sum(1 for c in coverage if c.status == "ok"),
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mc_na=sum(1 for c in coverage if c.status == "na"),
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mc_high=sum(1 for c in coverage if c.status == "high"),
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mc_medium=sum(1 for c in coverage if c.status == "medium"),
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mc_low=sum(1 for c in coverage if c.status == "low"),
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mc_insufficient=sum(
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1 for c in coverage if c.status == "insufficient_evidence"),
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mc_possibly=sum(
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1 for c in coverage if c.status == "possibly_applicable"),
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)
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return lint_output(out)
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return None
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