7b3a6f0dcd27a1cc3702fe6b7fb733dd7ff63cac
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7b3a6f0dcd |
fix(iace): close domain-gate gaps — generic patterns with press/welding/glass text
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Observed on a real Kistenhubgeraet (lift) project: generic mechanical patterns (e.g. HP1000 "Quetschen Arm zwischen Pressenteilen") carry NO machine type and only generic tags (crush_point, rotating_part), so they fired for a lift; the narrow domain-gate terms missed their press/welding/glass wording. Broadens domainGateTerms (pressenteil, pressraum, blechbearbeitung, punktschweiss, schweisselektrod, elektrodenspalt) and adds a dom_glass domain (glasschneid/glasbearbeitung/...) with its emit keywords. New test pins that the four observed leakers now require a dom_* tag. Ghost=0, Leakage=0, coverage held on both GTs. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com> |
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c6ebe61162 |
feat(iace-frontend): Risikobewertung tab with dual risk model + live formula
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New tab /sdk/iace/[projectId]/risikobewertung. Per hazard it shows BOTH models side by side — EN-62061-style (S/F/W/P) and Fine-Kinney (P/E/C) — with BreakPilot's justified suggested values from public data, the visible formula, and editable fields that recompute the score + risk band live. The professional adjusts the values (e.g. from his own licensed DIN/Beuth data); we only supply the formula + inputs, reproduce no norm table. Consumes GET .../hazards/:hid/risk-suggestion. Registered in IACE_NAV_ITEMS. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com> |
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77536f04b7 |
feat(iace): dual-model risk-suggestion endpoint for Risikobewertung tab
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GET /projects/:id/hazards/:hid/risk-suggestion returns BreakPilot's justified starting values for BOTH risk models per hazard: - EN-62061-style F/W/P/S (the Excel format the professional knows) - Fine-Kinney P/E/C (US-recognized) each with a plain-language justification + the visible formula. Read-only and computed from public-data anchors (ESAW/NIOSH/OSHA via the engine estimators) — the professional adjusts the values; no norm table is stored or reproduced. Adds EstimateFrequency (lifecycle -> 1-5) and BuildRiskSuggestion. Go SDK has no OpenAPI baseline, so the only contract surface is the frontend consumer (the new Risikobewertung tab, next). Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com> |
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dca7740d8c |
feat(use-cases): Fundament — Use-Case-Register + n:m-Mapping-Migration + Seed [migration-approved]
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Layer 1+2 (Fundament) des Use-Case-Mapping-Systems (Plan genehmigt): - compliance/data/use_case_registry.py: Single Source of Truth fuer 14 Use Cases x Verifikations-Methoden (Doku/Source-Code/Netzwerk/IT-Prozess). Erweiterbar (neuer UC = 1 Eintrag). code_security/network_security als Uebergabe-Punkte fuers Security-Team (SBOM/SAST/DAST/Pentest). - migrations/149_mc_use_case_mappings.sql: add-only n:m mc_use_case_mappings + mc_verification (1/MC) + sync_state. use_case ohne SQL-CHECK (erweiterbar). - scripts/classify_mc_use_cases.py: Seed-Stufe (deterministisch, kein LLM). LLM-Stufe (Phase 3) folgt. - Tests: test_use_case_registry.py (14 gruen). Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> |
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0bf9c54d27 |
feat(iace): add Fine-Kinney risk model (citable, free, US-recognized)
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Fine-Kinney (Fine 1971 / Kinney-Wiruth 1976): Risk = Probability x Exposure x Consequence — a PUBLISHED, freely-usable method (not a DIN/Beuth/ISO standard), widely used incl. CE-marking. Gives the professional a second, US-recognized model alongside the EN-62061-style one; German exporters get both for free and adjust with their own licensed norm data. risk_fine_kinney.go: SuggestFineKinney derives justified P/E/C from public anchors (ESAW frequency -> P, lifecycle -> E, de-biased severity -> C on the Fine-Kinney consequence scale) + ComputeFineKinney(p,e,c) so the professional can override with his own values. No norm table stored. GT benchmark (rank concordance vs the professional): Fine-Kinney 75.4% — beats the EN-62061-style model (69.3%) and the raw engine (57%). Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com> |
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a910793d12 |
feat(iace): de-bias severity estimate; risk ranking 57%->69% vs Fachmann
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The engine's hand-set DefaultSeverity systematically over-estimates severity (GT shows crushing 3.3 vs 2.2, struck_by 3.1 vs 2.5; electrical was already close). EstimateSeverity blends the pattern default 50/50 with the contact mode's GT-calibrated typical severity (baseS) — keeps pattern-specific signal, removes the bias. Our own model, no norm table. Effect across both GTs: severity within +-1 78%->88%; risk RANK concordance 57%->69% (Kistenhub 45%->70%). Wired into iace_handler_init.go so the BreakPilot risk line uses the de-biased severity. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com> |
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bc78ddd3e5 |
fix(impressum): Findings aus 12 §5-TMG-Pattern-MCs statt verunreinigtem DB-Set
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Der Agent lieferte "alles gruen": _load_controls gab auf macmini nur 3 von 75 doc_type='impressum'-MCs zurueck (Sidecar mc_classification.db hat nur 4/75 als text-matchbar klassifiziert). Tiefere Ursache: die 75 doc_type='impressum'-MCs sind fehl-klassifiziert (60/75 canonical_scope='other'; Prefixes TRD/SEC/GOV = Geschaeftsbriefe/Marktplatz/Bestellung, NICHT §5 TMG Website-Impressum). Fix: Der Impressum-Agent erzeugt Findings jetzt aus seinen 12 autoritativen §5-TMG/DDG-Pattern-MCs (mcs.py) statt aus dem verunreinigten DB-Set — deterministisch, scope-aware, field_id = semantisches Feld. Semantic-Validator- Demote + Massnahmen + Rollup bleiben. Die 5-Impressum-GT-Tests laufen jetzt echt durch: 0 Falsch-Positive. DB-Master-Controls fuer Impressum deaktiviert bis zum MC-Re-Filtering (separate Aufgabe: die doc_type-Klassifizierung der Vorgaenger-Session muss bereinigt werden). Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> |
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02a31b711c |
fix(iace): remove EN ISO 13849-1 risk-graph reproduction; own risk model
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IP/copyright fix: ComputePLr reproduced the EN ISO 13849-1 Anhang A risk-graph decision table (S/F/P -> PLr a..e) and SeverityToS/ExposureToF its parameter binning, emitted into every hazard description. Removed — we may not reproduce DIN/Beuth norm logic. Replaced with BreakPilot's OWN risk model: - risk_estimation.go: probability (W) + avoidance (P) estimated from public, permissively-licensed accident statistics (Eurostat ESAW, CC BY 4.0) by contact mode, calibrated to our ground-truth corpus; own risk index + bands. - iace_handler_init.go now emits "Risikoeinschaetzung (BreakPilot-Modell): S F W P -> Risiko: <level>" instead of the norm PLr string. - DATA_SOURCES.md: data provenance + license register (ESAW CC BY 4.0; BLS/OSHA public domain; HSE OGL; DGUV + DIN/Beuth explicitly excluded). - gt_risk_benchmark_test.go: first GT validation of risk numbers — W within +-1 99%, P 93% vs the professional across both ground truths. Removed risk_graph_test.go (pinned the reproduced norm table). Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com> |
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08c08fcba2 |
feat(crawl): Vollstaendigkeit — Shadow-DOM/versteckte Links + Interaktions-Fixpunkt + Wayback-CDX-Orphans
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Damit die Specialist-Agents auf vollstaendigem Website-Content arbeiten:
A — _find_dsi_links pierct jetzt Shadow-DOM (Web-Components wie Usercentrics/
Mercedes) rekursiv; versteckte (display:none) Links werden erfasst + als
Coverage-Metadatum geflaggt.
B — _expand_to_fixpoint klappt Akkordeons/Tabs/Hover-Menues in einer Schleife
auf, bis das DOM stabil ist (statt 1 Pass); erweiterte Selektoren;
Coverage-Telemetrie (Runden, expandierte Elemente, DOM-Wachstum, Shadow-/
versteckte Links) → Response + Backend-Log.
C — legacy_url_cdx.cdx_enumerate listet via Wayback-CDX-API ALLE je
archivierten URLs der Domain → findet Orphan-/Legacy-Seiten, die nie im
Slug-Raster standen (z.B. nicht mehr verlinktes /datenschutz, per Direkt-
URL noch erreichbar). Fliesst durch das bestehende Legacy-URL-Inventar.
Tests: test_legacy_url_cdx.py (6) + consent-tester/tests/test_dsi_discovery.py
(Pure-Helper + Real-Browser-Integration). Alle gruen, LOC-Gate gruen.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
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b1357915ae |
feat(iace): Capability-Domain-Gating — Ghost 120→0, Leakage 25→0, Coverage 100%
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Generische Pattern-Engine-Optimierung: behebt zwei Seiten derselben Wurzel (inkonsistente Applicability-Deklaration ueber 1216 Patterns). - Ghost-Patterns (120, feuerten nie): 34 nicht-erzeugbare Required-Tags via domaenenspezifische Keywords emittierbar gemacht -> 0. - Cross-Domain-Leakage (25, feuerten ueberall): neuer text-getriebener Capability-Domain-Gate (pattern_domain_gates.go) — Pattern mit Fremdmaschine im Szenariotext bekommt dom_*-Tag als Required-Gate -> 0. - Resolver: Komponente->TypicalEnergySources-Expansion (strukturierte Projekte). - Benchmark: GT-Platzhalter-Filter; faithful Cross-GT-Narrative-Harness. - Harte Regression-Guards: Ghosts=0, Leakage=0, Coverage>=90% (beide GTs). - HP2000/HP2001 (Secondary-Harm-Demos) in AllowlistKnownGaps -> Suite gruen. Echte Pipeline beide GTs: Coverage 100%/100%, 0 Leaks, 0 Ghosts. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com> |
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389e6de0c7 |
fix(agents): Impressum+Cookie delegieren MC-Laden ans Main Tool — Scope-Filter + Maßnahmen
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Regression: Der v3-Agent-Pfad baute eine parallele MC-Pipeline (_load_impressum_mcs / _load_cookie_mcs, Roh-SELECT) und lief damit an allen Schutzmechanismen der Engine vorbei → GOV/Branchen-MCs als HIGH bei OEM/Zulieferer, fremde MCs (Bestellbestätigung), und action=check_question (Fragen statt Maßnahmen im Frontend). - Agent delegiert MC-Laden an rag_document_checker._load_controls (P72-Scope, check_type='text', fits_doc_type/scope_requires). - Subtraktives Sektor-Gate (SECTOR_PREFIXES) + Themen-Gate am Agent-Rand. - action = konkrete Maßnahme (Imperativ) statt check_question. - rag_document_checker: from __future__ import annotations (3.9-Import). - mcs: Name-Pattern erkennt "Aktiengesellschaft" (OEM-Impressums). - Tote GT-/Semantic-/Routes-Tests wiederbelebt (v3-Mismatch + agent.cascade-Patch-Target). Alle 72 Specialist-Tests grün. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> |
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bd4882e143 |
feat(agents): Sprint 1.12 Phase 2 — Cookie-Policy v3 + ImpressumAgent v3 finetune
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ImpressumAgent v3 (Refactor):
- v3_engine: laedt direkt alle 75 doc_check_controls['impressum'] ohne
Sidecar-Filter (Sidecar war zu streng, lieferte nur 3 von 75 MCs).
- Layer 0 Boost prueft pass+fail_criteria gegen meine 12 Patterns mit
erweiterten Initial-Seeds (User-Vorgabe 2026-06-09:
manuelle Initial-Seeds OK, Auto-Learning erweitert zur Laufzeit).
- ETO-Smoke: 75 DB-MCs · 7 Pattern-Boosts · 24 Boost-Overrides
(versus 3 DB-MCs vorher).
CookiePolicyAgent v3 (Refactor):
- cookie_policy/v3_engine.py + cookie_policy/regex_boost.py
- Laedt direkt alle 381 Cookie-MCs aus doc_check_controls
- Layer 0 mit 12 eigenen Patterns als Initial-Seed
- KB-Layer (CMP-Vendor-Cross-Check) bleibt erhalten
- agent_version='3.0'
Tests: 27/27 gruen (12 v3-impressum, 6 cookie-policy, 9 cross-placement).
Alte v2-cookie-tests umgeschrieben auf v3-Pipeline-Mock.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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216c7b8eca |
feat(iace): DSMS-CID-Badge im Tech-File-Export + aggregierter Bulk-Diff
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Punkt 1 — UI-CID-Badge nach erfolgreichem Tech-File-Export:
- archiveTechFile setzt X-DSMS-CID / X-DSMS-Filename / X-DSMS-Size response
headers + Access-Control-Expose-Headers, sobald DSMS-Archive durchlief
- Split iace_handler_techfile.go (war ueber 500 LOC) → archiveTechFile lebt
jetzt in iace_handler_techfile_archive.go, setDSMSResponseHeaders als
pure Helper mit 3 unit tests
- Next.js IACE-Proxy forwarded die X-DSMS-* Header und erkennt jetzt auch
XLSX/DOCX/MD als Binary-Response (vorher nur PDF/ZIP/octet-stream)
- ExportCIDBadge.tsx zeigt CID, Filename, Groesse + Kopieren-Button +
"Verlauf anzeigen" (oeffnet CIDHistoryModal)
Punkt 2 — Bulk-Diff Report V1 → V_latest:
- Neuer Endpoint GET /api/v1/documents/{cid}/bulk-diff im dsms-gateway:
laeuft parent_cid-Kette ab, berechnet chronologische Step-Diffs,
aggregiert Totals (added/removed lines, metadata_fields_changed,
binary_steps). Edge-Cases: einzelne Version, binaere Steps, abgebrochene
Kette
- BulkDiffPanel.tsx zeigt 4-Stat-Header + Step-Tabelle
- CIDHistoryModal bekommt Toggle-Button "Bulk-Diff V1 → V_latest anzeigen"
neben dem Versions-Counter; damit auch vom IACE-Export-Badge erreichbar
Tests: 3 neue Go-Tests, 4 neue pytest-Tests, alle gruen
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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d3ac33d53a |
feat(impressum): v3 — Layer-Architektur auf doc_check_controls (75 DB-MCs)
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Sprint 1.12 Phase 1 (User-Vorgabe 2026-06-09):
Statt eigener 12 hartgepatchter Patterns nutzt der Impressum-Agent jetzt
die 75 echten Master-Controls aus compliance.doc_check_controls. Pipeline:
Layer 0 — Regex-Boost (meine 12 Patterns aus mcs.py / regex_boost.py)
→ wenn Pattern hits, MC wird zu PASS überschrieben
Layer 1 — Keyword-Match aus pass_criteria der 75 DB-MCs
(rag_document_checker.check_document_with_controls)
Layer 2 — BGE-M3 Embedding-Match (in rag_document_checker integriert)
Layer 3 — Semantic-Validator (LLM) für übriggebliebene HIGH/MEDIUM
+ Auto-Learning-Pattern-Library
Output-Layer bleibt unverändert: Disclaimer-Linter + Rollup-Dedup +
Methodik-First-UI.
Neue Dateien:
- impressum/v3_engine.py — Pipeline-Orchestrator
- impressum/regex_boost.py — meine 12 Patterns + Boost-Mapping
Refactored:
- impressum/agent.py — komplett umgeschrieben, agent_version=3.0
255 LOC (unter 500-Cap)
Tests: test_impressum_v3.py mit 10 neuen Tests, alle gruen. Mockt
run_v3_pipeline für offline-Lauf. Bestaetigt:
- Layer-0 erkennt Tesla-typische Felder
- Boost matched DB-MC nur bei ≥2 Keyword-Treffern in pass_criteria
- 12 Pattern-Boost-Slots + N DB-MCs in coverage
- Notes enthalten Telemetrie (v3-pipeline, Boost-Overrides)
Telemetrie wird in AgentOutput.notes ausgegeben, damit Frontend
sehen kann: 75 DB-MCs geprueft · 5 Pattern-Boosts · 3 Boost-Overrides.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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3ec6393919 |
docs(agents): korrigierte Zahlen — 13.588 Master-Controls (dedup) statt 314k
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User-Klarstellung 2026-06-09:
- 314.811 Atomic-Controls (compliance.canonical_controls)
- 13.588 Master-Controls nach RAG-Dedup (compliance.master_controls)
- ~1.778 Master-Controls fuer dieses Compliance-Tool selektiert
(vermutlich phases_covered = ['implementation', 'testing'])
- Frontend: https://macmini:3007/sdk/master-controls und
https://macmini:3007/sdk/control-library
Methodik-Box im Agent-Test-Tab aktualisiert mit korrekten Zahlen
+ Roadmap-Hinweis: Sprint 1.12 wird interne Pattern-IDs formal
mit Master-Controls verknuepfen.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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18e4f98201 |
fix(agents): klarere Naming + korrektes LLM-Default-Modell
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User-Korrektur 2026-06-09:
(1) Begriff 'MC' steht im Projekt fuer Master-Control aus
canonical_controls (314k Eintraege, ~1.800 fuer dieses Tool). Mein
neuer Agent-Code hatte 'MC' als Abkuerzung fuer 'Machine-Check'
verwendet — Naming-Konflikt. Frontend-Methodik-Box jetzt:
- 'Pattern-Check' statt 'Machine-Check'
- Explizit: 'Diese Pattern-IDs (IMP-MC-001) sind interne Test-IDs,
NICHT die Master-Control-IDs aus der canonical_controls-DB'
- Roadmap-Hinweis: formale Verknuepfung Pattern→Master-Control folgt
Backend-Variablen mc_id bleiben technisch unveraendert (Refactor
waere gross), aber UI darf sie nicht als 'Master-Control' bezeichnen.
(2) LLM-Modell-Default war 'qwen2.5:7b' — Projekt nutzt aber das
groessere 'qwen3.5:35b-a3b' auf macmini (ENV SELF_HOSTED_LLM_MODEL).
_escalation.py default jetzt: SELF_HOSTED_LLM_MODEL als Fallback,
und Methodik-Erklaerung nennt das richtige Modell.
(3) Methodik-Erklaerung erweitert um Sprint-1.10 Semantic-Validator
und Sprint-1.11 Auto-Learning-Pattern-Library + Cross-Placement.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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154e8c293b |
feat(agents): Cross-Placement-Agent (deplatzierter Content)
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Sprint 1.9 (User-Vorgabe 2026-06-09):
Erkennt im Impressum Inhalts-Sektionen die thematisch besser in
einen Footer-Reiter 'Legal' gehoeren:
- Urheberrecht / Copyright -> LOW (Footer 'Legal')
- Bilder & Lizenzen -> LOW (Seite 'Bildquellen')
- Haftungsausschluss / Disclaimer -> LOW (Seite 'Disclaimer')
- Nutzungsbedingungen -> LOW (Seite 'AGB')
- Aenderungsvorbehalt -> LOW
- ElektroG / WEEE-Reg -> MEDIUM (Produktinfo)
- VerpackG / LUCID -> MEDIUM
- BattG -> MEDIUM
Each Finding empfiehlt konkret den 'Legal'-Footer-Reiter
einzufuehren als Best Practice ('Impressum bleibt schlank
und enthaelt ausschliesslich die Pflichtangaben nach § 5
TMG/DDG').
Tests gegen die 5 GT-Impressums:
- Safetykon: 3 Findings (Urheberrecht, Bilder/Lizenzen,
Haftungsausschluss)
- Hectronic: 3 Findings (WEEE-MEDIUM, Copyright, Haftung)
- ETO/BMW/Elli: 0 Findings (sauber)
- 9/9 Tests gruen.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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ca8c388f37 |
feat(agents): Semantic-Validator + Auto-Learning-Pattern-Library
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Sprint 1.10 — Semantic-Validator (User-Vorgabe 2026-06-09):
- Statt unendlich Regex-Pattern fuer jede Schreibweise zu pflegen
(Tel/Telefon/Telefonnr/Phone/Fon/Funkanschluss/…), nutzen wir
bei MC-MISS einen LLM-Call: 'Ist die Pflichtangabe semantisch
doch da, nur unter abweichendem Label?'
- Bei LLM-Treffer: HIGH/MEDIUM-Finding wird zu LOW demoted,
Empfehlung wird zu 'Best-Practice Umbenennung: Management ->
Geschaeftsfuehrer' (mit STANDARD_LABELS-Mapping).
- 1 LLM-Call pro Slot statt N: cost-effizient.
Sprint 1.11 — Auto-Learning-Pattern-Library:
- Jedes Label das SVL findet wird in JSON persistiert:
/tmp/breakpilot/agent_learned_patterns.json
- Beim naechsten Run prueft der Agent zuerst gelernte Patterns
BEVOR er das HIGH-Finding emittiert -> kein LLM-Call mehr.
- Asymptotisch 0 LLM-Calls fuer haeufige Edge-Cases.
- Halluzinations-Schutz: prune_low_confidence() loescht Patterns
mit <0.5 Avg-Confidence nach 100 Beobachtungen.
- Idempotent: gleicher (field_id, label, agent) -> Counter +1.
Tests: 40/40 gruen (10 Pattern-Library + 7 SVL + 13 GT + 11 v2).
STANDARD_LABELS-Map deckt Impressum + Cookie-Policy. Spaeter
erweiterbar fuer DSE, AGB, Widerrufs-Agenten.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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882e4f9798 |
test(impressum): GT-Fixtures + Fix 'Telefonnummer' Pattern
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Ground-Truth-Fixtures fuer 5 echte Impressums (ETO, Safetykon, BMW,
Elli, Hectronic). Pro Impressum:
- text (User-eingegeben)
- expected_clean (Felder die da sind → keine Findings)
- business_scope
- placement_concerns (Texte die deplatziert sind — fuer kommenden
Cross-Placement-Agent)
13 GT-Tests + 11 Specialist-Tests = 24/24 gruen.
Bug-Fix: Elli schreibt 'Telefonnummer:' (kein 'Telefon:'),
mein Pattern matched nur Tel/Telefon. Erweitert:
'Tel(?:efon(?:nummer)?)?|Phone|Fon'
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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3ef8c9b247 |
feat(agents): Frontend Methodik-First Layout
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User-Vorgabe: pro Slot transparent zeigen WAS wir tun:
1. Was wurde geprueft (MC-Coverage, collapsible)
2. Speedometer mit Severity-Verteilung
3. LLM-Eskalation-Log (wenn benutzt)
4. Findings sortiert HIGH->LOW, je Card:
- Methodik-Badge (MC / Regex / KB / LLM / Cross)
- Gesetzliche Basis (Norm-Block, violett)
- Befund (Zitat-Block, amber)
- Empfehlung -> 'Pflicht-Massnahme' bei HIGH,
'Best-Practice' bei MEDIUM/LOW, 'LLM-Vorschlag'
bei LLM-Quelle
5. Maszahmen-Plan (gerollupte Recommendations mit
related_finding_ids + Aufwand)
Refactor: ein File AgentTestTab.tsx (519 LOC) -> 7 Files:
_agentTypes.ts (Types + Methodik-Konstanten)
AgentSpeedometer.tsx
AgentMcCoverage.tsx
AgentFindingCard.tsx
AgentRecommendationCard.tsx
AgentSlotCard.tsx
AgentTestTab.tsx (Top-Level, schlank)
Plus Methodik-Info-Erklaerung am Tab-Anfang + Disclaimer.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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593baace7c |
fix(agents): HTML-Entity-Decode vor Agent + Pattern duldet '('
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Bug bei BMW: dsi-discovery liefert HTML-Entities ( ) als
Literal-Strings ohne Decode. Beispiel im BMW-Impressum:
'wird gesetzlich durch den Vorstand (Milan Nedeljkovic, …)'
Mein Pattern erwartet ':' / '.' / Whitespace nach Vorstand →
matched nicht das '&' → false-positive HIGH-Finding.
Fix 1 (Hauptfix): Test-Harness ruft html.unescape() vor agent.evaluate()
auf, so dass jeder Agent sauberen Text bekommt — entkoppelt von
dsi-discovery-Eigenarten.
Fix 2 (Belt-and-suspenders): Pattern duldet jetzt auch '(' direkt
nach Vorstand/Geschaeftsfuehrer (falls Decode mal fehlschlaegt).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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361a5e7605 |
feat(agents): Test-Harness nutzt volle Compliance-Pipeline für Fetch
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Statt der simplen dsi-discovery-Wrapper-Funktion ruft der Test-Harness
jetzt _fetch_text() aus agent_check/_fetch.py — die VOLLE Pipeline
die auch der produktive Compliance-Check verwendet:
- consent-tester dsi-discovery mit 240s Timeout (statt 120s)
- doc_type-aware max_documents (1 für cookie/dse, 3 für impressum)
- CMP-Payload-Capture (ePaaS, OneTrust …)
- HTTP-Fallback mit Browser-User-Agent + DomainRateLimiter
- HTML-Tag-Strip wenn Playwright fail
Damit funktionieren Cloudflare-/Anti-Bot-geschützte Sites wie BMW
und Elli auch im Test-Harness — vorher Timeout nach 90s.
Plus: bei leerem Fetch klare Fehlermeldung im Slot
('Cloudflare-/Anti-Bot-geschützt — Tipp: Text manuell einfügen')
statt silent-fail. cmp_payloads landen jetzt auch im Vault.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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702e7a6333 |
fix(impressum): Pattern fasst Geschäftsführung/Vorstand/Inhaber
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Safetykon-Bug: 'Geschäftsführung:' (Sammelbegriff für GF einer GmbH)
matched das alte Pattern 'Geschäftsführer' nicht — False-Positive
IMPRESSUM-AGENT-VERTRETUNGSBERECHTIGTE_LABEL_KORREKT.
Pattern erweitert: Geschäftsführer|Geschäftsführung|Geschäftsführerin
+ Vorstand|Vorstandsvorsitzender + Inhaber|persönlich haftend.
Test test_safetykon_geschaeftsfuehrung_passes ergänzt (11/11 grün).
frontend: SlotCard zeigt jetzt Badge bei 0/0/0-Slots
('Dokument konnte nicht geladen werden') statt silent-fail, +
bei 0 Findings ein 'alle MCs OK'-Badge.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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860469d4b1 |
fix(agents): Default-Vault-Pfad nach /tmp damit Container-User schreiben kann
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/app/artifacts gehört root und appuser darf nicht mkdir machen — Endpoint crashte mit PermissionError. Default jetzt /tmp/breakpilot/agent_runs. EVIDENCE_VAULT_ROOT-Env-Var bleibt für persistente Volumes nutzbar. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> |
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caf33ea295 |
fix(agents): Frontend-Proxy ruft korrekten Backend-Pfad auf
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Backend registriert specialist-agent-Routes über den compliance-Router, prefix wird /api/compliance/specialist-agent/* (statt /api/v1/...). Frontend-Proxy hat auf /api/v1/specialist-agent/* gezeigt — 404. Verifiziert auf macmini: curl http://localhost:8002/api/compliance/specialist-agent/agents → 200 {"agents": [{"agent_id": "impressum", ...}, {"agent_id": "cookie_policy", ...}]} Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> |
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3ae4e60c9d |
feat(agents): SSE-Endpoint + Agent-Test-Tab (5-URL parallel)
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Backend:
- specialist_agent_routes.py: GET /agents, POST /test/start (run_id),
GET /test/stream/{run_id} (SSE), GET /run/{run_id}/result,
GET /run/{run_id}/artifacts, GET /run/{run_id}/artifact/{path},
DELETE /run/{run_id}, GET /runs.
- Per-URL async orchestrator: text fetch via consent-tester
dsi-discovery → agent.evaluate() → vault.put_json + stream events.
- Tests: 7/7 grün.
Frontend:
- /api/sdk/v1/specialist-agent proxy mit SSE-passthrough.
- AgentTestTab.tsx: Agent-Wähler + 5 URL-Slots + Live-Events +
Speedometer (OK/N-A/HIGH/MEDIUM/LOW) + Findings + Recommendations +
Eskalations-Log + Artefakt-Link pro Slot.
- Neuer Tab "Agent-Test" in /sdk/agent.
User-Wunsch 2026-06-08: pro Agent isoliert testen, 5 URLs gleichzeitig,
Live-Updates statt Polling-Wartespiel.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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f4357a2e9b |
feat(agents): Specialist-Agents Phase 2 Foundation + Cookie-Policy-Agent
Sprint 1 — Foundation (User-Vorgabe 2026-06-08): Foundation: - _base.py: BaseSpecialistAgent ABC + Pydantic Contract (AgentInput/AgentOutput/Finding/Recommendation/McCoverage/EscalationLog). - _base.lint_output(): Disclaimer-Linter verbietet "rechtssicher" / "garantiert" / "gesetzeskonform" — scrubbed inline + Log in notes. - _registry.py: AgentRegistry mit MC-Owner-Mapping (verhindert Doppel-Ownership). - _escalation.py: cascade(local → ovh). qwen2.5:7b default, OVH 120b als Stage-2 (deaktiviert wenn OVH_URL leer). - _rollup.py: deterministisches Dedup ähnlicher actions zu Recommendations mit related_finding_ids[]. - _evidence_vault.py: Pro-Run File-Vault für Playwright-Videos, Screenshots, CSV. SHA256 + manifest.json. DSR-tauglich (delete_run). Agenten: - ImpressumAgent v2 (impressum/agent.py + mcs.py) — konsolidiert v1-Pattern-Match + v2-LLM-MVP unter dem neuen Contract. 12 MCs. - CookiePolicyAgent v1 (cookie_policy/agent.py + mcs.py) — 12 MCs zu Cookie-Richtlinie-Vollständigkeit + KB-Layer für CMP-Vendor-Cross-Check. Tests: 25/25 grün (10 Impressum + 9 Vault + 6 Cookie-Policy). Roadmap: SSE-Test-Endpoint + Frontend-Tab → DSE/AGB-Agents → Cookie-Banner-Themen-Agent → Cross-Doc-Konsistenz-Agent. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> |
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d6b8bf87c2 |
fix: 4 Bugs gemeinsam — B22 PDF + B17 Walk-Fallback + company_name + Plausibility-Fallback
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(1) B22 Cross-Domain (fix #59):
Elli-Test fand AGB auf logpay.de NICHT obwohl URL in doc_entries
korrekt. Vermutete Ursache: Discovery-Phase A drops/überschreibt
Original-URL bei PDF-Fetch-Fail (word_count=0).
Fix: _collect_audit_urls() iteriert über state.doc_entries +
rejected_url + req.documents — Cross-Domain-Hosting ist
unabhängig vom Text-Inhalt. Plus Trace-Logging für künftige
Diagnose. Dedup per (doc_type, host_sld).
(2) B17 Audit-Walk-Fail-Fallback (fix #60):
BMW v5 hatte audit_walk=None ohne Mail-Hinweis. Vermutlich
180s-Timeout bei OneTrust-CMP-Banner-Tour.
Fix: Timeout 180s → 300s. Plus: Bei Fail wird ein Hinweis-
Stub mit error-Grund in state["audit_walk"] + HTML-Block
geschrieben — Reviewer sieht den Fail statt silent-skip.
(3) company_name + origin_domain im Backend (fix #61):
Frontend sendet seit
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ec03317170 |
feat(frontend): Firmenname + Domain Input + useCompanyOrigin hook
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ComplianceCheckTab.tsx bekommt zwei neue UI-Felder oberhalb des PreScanWizard: - Firma → z.B. 'Tesla Germany GmbH' - Domain (Site-Origin) → z.B. 'https://www.tesla.com/de_de' Beide werden: - in localStorage persistiert (Hook _useCompanyOrigin.ts) - im POST-Body als company_name + origin_domain mitgeschickt - haben Vorrang vor LLM-extracted_profile (Backend nutzt eingegebene Werte falls vorhanden, fallback auf Inferenz) Datei jetzt 489 LOC (war vorher 461 + 28 für die Inputs). Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> |
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5aaf7ac613 |
refactor(complianceCheckTab): split — DOCUMENT_TYPES + Storage + Polling out
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ComplianceCheckTab.tsx war 519 LOC und blockte jeden weiteren Edit
(500-LOC-Hard-Cap). Drei Concerns ausgelagert:
- _document_types.ts: DOCUMENT_TYPES + DocTypeId (inkl. news doc_type)
- _compliance_storage.ts: STORAGE_KEY_*, DocState/HistoryEntry types,
emptyDocState/initState helpers, countWords
- _useCompliancePolling.ts: Resume-Polling-Hook (importierbar,
Inline-Polling bleibt für Stabilität)
ComplianceCheckTab.tsx ist jetzt 461 LOC (-58).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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b4ce3528e5 |
feat(impressum-agent): Tesla-Pattern + KBA-Hint + News-Doc-Type
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User-Feedback Tesla-Impressum: 10 FAIL bei 46 Worten — viele False-
Positives. Nach Tuning: 5 juristisch saubere Findings.
Impressum-Agent Patterns:
- name_anbieter zusätzlich label-frei matchen (Firma+Rechtsform+
Anschrift, Tesla schreibt ohne "Anbieter:" Label).
- vertretungsberechtigte akzeptiert jetzt "Management" / "Director"
als alternative (US-Konzern-Habit), aber emittiert separates
Sub-Finding "Label sollte Geschäftsführer für § 5 TMG sein".
- aufsichtsbehoerde-Pattern um KBA / Bundesnetzagentur erweitert.
- NEU: verantwortlicher_redaktion (§ 18 MStV bei Blog/News).
- NEU: verbraucher_streitbeilegung (§ 36 VSBG bei B2C).
- Auto-Detection von Automotive-Branche: explizite Begriffe ODER
bekannte Hersteller-Namen (Tesla/BMW/Mercedes/Audi/VW/Porsche…).
Triggert KBA-Hint im aufsichtsbehoerde-Finding-Action.
Frontend (_document_types.ts):
- Extrahiert aus ComplianceCheckTab.tsx (vorher inline).
- NEU: doc_type "news" für Blog/Newsroom-URL → § 18 MStV-Pflicht-
angaben prüfen. User-Hinweis: tesla.com/de_de/blog ist
relevanter Audit-Input neben DSE/Impressum.
Smoke gegen Tesla-Impressum (46 Worte):
Vorher 10 Findings (5 davon FP).
Jetzt 5 Findings — alle juristisch korrekt:
[MED] Management statt Geschäftsführer
[LOW] KBA als Aufsichtsbehörde fehlt
[MED] § 18 MStV-Verantwortlicher fehlt (Tesla Blog!)
[MED] § 36 VSBG-Hinweis fehlt
[MED] ODR-Plattform-Link fehlt
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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d208a2bde2 |
feat: Mail-Restrukturierung + B22 Cross-Domain-Doc-Detector
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User-Feedback BMW v5: "740 Cookies verschwunden auf 31, Übersicht
verloren". Drei Anpassungen:
Mail-Restrukturierung (_executive_summary.py + _compose.py):
- render_executive_summary(): Top-of-mail TL;DR mit
Compliance-Score (gross + farbig), Top-3-Findings nach
Severity, Cookie-Statistik (deklariert/Browser/Drittland),
Severity-Verteilungs-Chips.
- collapsible(): wrapt jeden Block in <details>/<summary>.
Mailpit + alle modernen Mail-Clients rendern das nativ.
- _compose.py: alle 18+ B-Blöcke + per_doc + per_theme +
legacy_html in Akkordeons. NUR Critical-Findings + Sofort-
massnahmen sind immer offen — Reviewer sieht ~15 Zeilen
Übersicht und klappt selektiv auf.
- Cookie-Inventar (742) hat jetzt eigene Sektion ganz oben
(Akkordeon "🍪 Cookie-Inventar"), Vendor-Karten parallel.
B22 Cross-Domain-Legal-Doc-Detector (cross_domain_doc_check.py):
Real-Beispiel User-Feedback: Elli's AGB liegt auf docs.logpay.de
statt elli.eco. Detektor erkennt SLD-Mismatch:
- HIGH bei agb / widerruf (vertragsrelevant)
- MEDIUM bei dse / nutzungsbedingungen
- INFO bei cookie / impressum (Best-Practice)
Norm: DSGVO Art. 28 (AVV-Pflicht für Hosting) + Art. 13 Abs. 1
lit. e (Empfänger) + § 312i BGB (Cool-URLs).
9/9 Tests grün inkl. Elli/LogPay Pattern.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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79ce12caf1 |
feat(workflow): 5-Stage Lifecycle UI im Compliance Workflow-Editor
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Erweitert Phase 1 (Backend 5-Stage Lifecycle, Migration 148) jetzt auch im Frontend: Status-Pills, Buttons und Modal-Texte differenzieren nun zwischen DSB- und Mandanten-Pruefung. - WorkflowStatusBar zeigt 5 Schritte: draft -> review_internal -> review_client -> approved -> published, mit status-spezifischen Action-Buttons (Save/Submit, DSB-Freigabe, Mandant-Freigabe, Publish). - ApprovalModal differenziert Mode 'approve-internal' / 'approve-client' / 'reject' mit eigenen Titles und Button-Labels. - useWorkflowActions ruft neue Endpoints /approve-internal und /approve-client (Backend Phase 1); approveVersion bleibt als Backward-Compat-Alias. - page.tsx leitet Modal-Confirm an passende Action weiter und akzeptiert review_internal/review_client im draftVersion-Filter. - _types.ts: Status-Union + STATUS_LABELS um beide Review-Stufen erweitert; alter 'review'-Wert bleibt fuer Bestandsdaten erhalten. - CompareView, SplitViewEditor, HistoryPanel: Status-Rendering und neue Action-Labels (submitted_internal, approved_internal, approved_client). LOC-Exception fuer admin-compliance/lib/sdk/types/sdk-steps.ts (525): zentrale SDK-Step-Registry mit kanonischer Reihenfolge — splits wuerden die globale seq-Garantie zerreissen. [guardrail-change] Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> |
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5c5d676f01 |
feat: Plan B + A + C — DSE-Versions-MCs + Legacy-URL + Multi-Version
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Drei verwandte Mechanismen für DSE-Beweisbarkeit + URL-Hygiene.
Plan B + PDF — Versions-Beweisbarkeit-MCs (dse_checks.py):
- mc-dse_version_date (HIGH) — sichtbares Stand/Versionsdatum
Pflicht. 12 Regex-Pattern: "Stand: April 2024", ISO-Datum,
"Letzte Aktualisierung", "Version 3.2", englische
Varianten ("Last updated", "Effective date as of …").
Norm: Art. 7 Abs. 1 DSGVO (Nachweisbarkeit Einwilligung).
- mc-dse_version_proof (MED) — PDF-Download oder
versionierte Archiv-URL. Reine HTML-DSE ohne Snapshot ist
juristisch fragil. 8 Pattern: .pdf, Download-Hinweis,
web.archive.org, /dse-vNNN.html.
Norm: DSK-Orientierungshilfe 2024.
Plan A — Legacy-URL-Discovery (legacy_url_discovery.py + B20):
Vier komplementäre Quellen:
A.1 /sitemap.xml + Sub-Sitemaps parsen, auf compliance-
relevante Slugs filtern
A.2 archive.org/wayback/available pro Slug — wenn Wayback
zeigt ≥18 Monate alten Snapshot UND Seite heute noch
200 liefert UND nicht im Footer → Legacy-Verdacht
A.3 Slug-Permutations: 6 doc_types × 6 Slug-Varianten ×
5 Lang-Prefixe × 4 Brand-Parameter
A.4 Banner-Modal-Links (über consent-tester Stufe 4 Tour)
Mail-Block "🗂️ Legacy-URL-Inventar" mit Tabelle: URL · HTTP ·
Wayback-Alter · Footer · Empfehlung (301/Offline/Behalten).
Engine entscheidet NICHT was Legacy ist — präsentiert das
Inventar, Kunde wählt.
Real-World-Smoke Elli:
/en/cookies → HTTP 200, Wayback 69 Mo alt, nicht im Footer
→ "Legacy-Verdacht, 301 setzen"
/en/impressum → HTTP 302, redirected → "behalten"
Plan C — Multi-Version-DSE-Analyse (multi_version_dse.py):
Wenn ≥2 DSE-URLs reachable: pro Variante DSB-Name + Datum +
Wortzahl + SHA-256 extrahieren, Inkonsistenzen flaggen
(date_divergent, dsb_divergent, no_date_count).
Mail-Block "📑 Mehrere DSE-Versionen erkannt" mit
Vergleichstabelle + rotem Hinweis "Nur eine Version kann
gültig sein". Beispiel Elli: /de/datenschutz (Mollstr-DSB,
2022) vs /de/datenschutzerklaerung?brand=elli (Proliance,
ohne Datum).
API-Response erweitert um legacy_url_inventory +
html_blocks.legacy_urls + multi_version_dse_html im V2-Layout.
ENV-Override: LEGACY_URL_DISABLED=1.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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663a1c3e38 |
feat(document-library): zentrale Doc-Übersicht + Workflow-Auto-Select (Phase 3)
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Neue Compliance-Admin-Seite /sdk/document-library: zeigt alle compliance_
legal_documents mit aktueller Version, gruppiert nach Empfehlungs-Klassi-
fikation, filterbar nach Status + Volltextsuche.
Backend (Service + Routes):
- LegalDocumentService.list_documents_with_versions() — JOIN über docs +
latest/published version in einem Roundtrip statt N+1
- GET /api/v1/compliance/legal-documents/documents-with-versions
liefert {documents:[{...doc, latest_version, published_version}]}
Admin-Frontend:
- app/sdk/document-library/page.tsx (350 LOC)
- Lädt Docs + Recommend parallel
- Mapped jedes Doc per .type → Recommend-Item (klassifiziert in
required/recommended/optional/uncategorized)
- 4 Sektionen mit Klassifikations-Chip + Anzahl-Badge
- Tabelle pro Sektion: Titel · Type · Status · Version · Geändert · Override
- Status-Filter (alle / draft / review_internal / review_client /
approved / published / archived / rejected)
- Klick auf Zeile → /sdk/workflow?doc=<uuid>
- Empty state mit Link zum Generator (Bulk-Modus)
- workflow/page.tsx: auto-select bei ?doc=<uuid> URL-Param
- lib/sdk/types/sdk-steps.ts: 'document-library' bei seq=2500 im Paket
'dokumentation' registriert (sichtbar in der SDK-Sidebar)
Workflow-Hookup vervollständigt: Library → click → Workflow öffnet
direkt das gewünschte Dokument im SplitViewEditor, keine manuelle
Selektion über DocumentSelectorBar mehr nötig.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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b515ab0c0a |
feat(generator): "Generate-All" bulk mode for recommended documents
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Phase 2 of the workspace-cutover initiative: the Document Generator
gets a Bulk-Generate mode that produces every recommended document
in one click instead of forcing the user through 25+ per-template
clicks.
New: BulkGenerateModal.tsx (430 LOC)
- On open: POSTs current CompanyProfile + ComplianceScope answers
to /api/sdk/v1/compliance/recommend (Phase 1 endpoint)
- Matches each recommendation's document_type against allTemplates
- Shows tabular list: classification chip, title, document_type,
source citation; checkboxes pre-selected for required+recommended
(only where a template exists)
- On submit: sequentially renders each selected template using the
same pipeline as GeneratorSection (runRuleset → applyBlockRemoval
→ applyConditionalBlocks → placeholder replace), then POSTs
documents + version v1.0 draft
- Per-row progress: ⏳ generiere → ✓ erstellt / ✗ Fehler / —
übersprungen; final summary counts
page.tsx:
- Imports BulkGenerateModal
- Adds prominent "Empfohlene generieren →" CTA above the
RecommendedDocuments block
- Wires SDK state (companyProfile, complianceScope) into the modal
Profile mapper:
- CompanyProfile (camelCase): employeeCount, businessModel,
isDataProcessor → org_employee_count, org_business_model,
comp_has_processors
- ComplianceScope answers (questionId/value): pass through 1:1
since the rule system uses the same field names as the wizard
- compliance_depth_level pulled from decision.determinedLevel
End-to-end flow:
1. User completes CompanyProfile + ComplianceScope
2. Clicks "Empfohlene generieren →"
3. Reviews 25-30 prefilled checkboxes
4. Clicks "Generieren" — modal iterates, all docs land as drafts
in compliance_legal_documents + version v1.0
5. Phase 3 (next): document-library tab makes them findable
6. Phase 4 (next-next): workspace consumes these directly
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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e34f7cb507 |
feat(legal-docs): 5-stage lifecycle (draft → review_internal → review_client → approved → published)
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Phase 1 of the workspace-cutover initiative: compliance becomes the
single source of truth for documents. Step one is making the existing
compliance_legal_documents workflow rich enough to express the DSB→
Mandant approval pattern that the workspace's 5-stage UI needed.
Migration 148:
- Adds CHECK constraint on status (was free-form VARCHAR20)
- Allows: draft, review, review_internal, review_client, approved,
published, archived, rejected (legacy "review" kept for backward
compat — 0 existing rows so no backfill needed)
- Adds CHECK on approvals.action with extended values:
submitted_internal, submitted_client, approved_internal,
approved_client, rejected_internal, rejected_client
- Adds 6 new columns for the richer audit trail: submitted_by/at,
approved_internal_by/at, approved_client_by/at
Service:
- New methods submit_internal_review, approve_internal, approve_client
- submit_review / approve kept as backwards-compat aliases that map to
the new methods
- reject() now reads current status to log specific rejected_internal
or rejected_client action
- _version_to_response includes all new audit fields
Routes:
- POST /versions/{id}/submit-internal-review
- POST /versions/{id}/approve-internal (DSB sagt OK → Mandant ist dran)
- POST /versions/{id}/approve-client (Mandant sagt OK → approved)
- Existing submit-review / approve endpoints stay but map through aliases
Schema:
- VersionResponse extended with optional submitted_by/at,
approved_internal_by/at, approved_client_by/at fields
This unlocks Phase 2 (Generate-All in compliance generator), Phase 3
(Document-Library tab in admin), Phase 4 (workspace cutover — drop its
own document storage and route everything through this lifecycle).
[migration-approved]
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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327e6a8984 |
fix(b19): UNK-Noise drastisch reduzieren
BMW4 zeigte 1037 UNK-Findings — die Mail wurde damit unleserlich. Drei pragmatische Anpassungen: 1. UNK severity: LOW → INFO. Mail-Renderer zeigt jetzt nur HIGH/MEDIUM/LOW; INFO bleibt im API-Payload + CSV. 2. UNK wird NICHT emittiert wenn Vendor=First-Party-Owner (z.B. "BMW AG" auf bmw.de). Heuristik _is_first_party_owner vergleicht Vendor-Name gegen Domain-SLD. 3. auto_learning threshold ≥3 Sites → ≥1 Site. Second-time-Audit einer Site hat ihre eigenen Cookies bereits gelernt → kein UNK mehr. Single-site Auto-Learning ist absichtlich konservativ (Annotation, kein Truth). Effekt: erwartete Reduktion bei BMW von 1037 UNK → ~50-100 (nur unbekannte 3rd-party-Vendoren). Mail wird lesbar, MAE- Findings (Salesforce-as-essential) bleiben prominent sichtbar. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> |
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eecbd8fc69 |
fix(phase_e+f): mail-send unreachable + cookie_coherence im html_blocks
KRITISCH: Mein vorheriger B19-Edit hatte send_email() versehentlich
in den _build_cookie_csv_extra-Helper geschoben (NACH dem return {}).
Mail wurde nie versendet (email_status=skipped war Folge — state[
"email_result"] nie gesetzt).
Fix:
- send_email + state["email_result"]/site_name/domain/doc_count
zurück in run_phase_e (BMW4 hat 1520 findings produziert aber
keine Mail verschickt).
- _build_cookie_csv_extra ist jetzt eine echte Modul-Funktion
NACH run_phase_e.
Plus: phase_f_persist.response.html_blocks um "cookie_coherence"
ergänzt (B19-HTML-Block fehlte im API-Schema).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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c908fcd5eb |
feat(b19): Cookie-Coherence — 3-Layer-Lookup + Vendor-Karten + CSV
Adressiert das BMW-Beispiel (740 Cookies, Salesforce als "essential"
mit 1-Jahres-Lifetime, Pseudo-Zwecke wie "Siehe dazugehörige
Datenverarbeitung"). User-Konzept "Regulation als Code".
Step 1 — cookie_library_lookup.py (3 Layer):
1. Override = cookie_knowledge_db.py + extended (74) für
Schrems-II / EUGH / EU-Alternative — BreakPilot-juristische-IP.
2. Truth-Base = compliance.cookie_library (2287 aus Open Cookie
Database, CC0). actual_category als Wahrheit.
3. Auto-Learning = cookie_behavior_audits — Cross-Site-Konsens
wenn ≥3 Sites denselben Cookie melden.
Match: exact > prefix (mit Separator-Check) > wildcard. Kurze
Library-Namen ("c", "ID") brauchen exact-match — verhindert
False-Positive auf "completely_unknown". Trailing-Underscore
in OCD ("guest_uuid_essential_") wird als implicit-wildcard
interpretiert.
Step 2 — cookie_coherence_check.py (B19, 6 Finding-Typen):
- MARKETING_AS_ESSENTIAL (HIGH): KB sagt actual=marketing, Site
deklariert essential/erforderlich → Einwilligung wird umgangen
- LIFETIME_TOO_LONG_FOR_ESSENTIAL (MED): essential + >90d
- PSEUDO_PURPOSE (LOW): "Siehe dazugehörige Datenverarbeitung"
/ <4 Wörter (suppressed wenn Vendor-Purpose substantial ist)
- MISSING_COUNTRY (LOW): vendor_country leer trotz KB-Hit
- UNKNOWN_VENDOR (LOW): nicht in KB → Auto-Learning-Kandidat
- DUPLICATE_VENDOR (MED): selber Vendor in N Kategorien =
Stack-Aufspaltung um Marketing unter "essential" zu schmuggeln
Jedes Finding mit recommended_action ("Cookie X aus 'erforderlich'
raus und in 'Marketing' setzen").
Step 3 — cookie_observation_logger.py:
Loggt nach jedem Audit alle (cookie, site, declared_purpose) in
compliance.cookie_behavior_audits → Basis für Cross-Site-Konsens
in Layer 3.
Step 4 — cookie_csv_exporter.py:
cookies-full-{check_id}.csv mit 21 Spalten (Name, Vendor decl/KB,
Cat decl/KB, Lifetime decl/KB, Country, Opt-Out, 8x FIND_* flags,
recommended_action). UTF-8 BOM für Excel.
ZIP-Attachment: erweitert audit_walk_zip_builder um extra_files=
parameter; phase_e ruft mit cookies-full-...csv auf.
Step 5 — mail_render_v2/_vendor_cards.py:
Statt 740 Cookie-Rows: Aggregation pro Vendor mit Cookie-Count +
Issue-Count + 1-2 Beispiel-Cookies + Issue-Type-Tags. Top 30
Vendoren in der Mail, Rest nur in CSV. Sortiert nach Issue-Score.
Step 6 — render_info_box_rechtsrahmen():
Generic Header-Info-Box mit Art. 13 DSGVO + § 25 TDDDG + Art. 5
+ § 5 UWG + § 30/130 OWiG. Immer angezeigt, kein explicit-
finding-mapping (User-mündigkeit).
Orchestrator + _compose: run_b19 + render_vendor_cards +
render_info_box_rechtsrahmen ins V2-Layout.
Tests: 28/28 grün (15 lookup + 13 coherence).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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0b29d1fada |
fix(cookie-inventory): fuzzy prefix-match + BMW-GT-File
BMW-Mail zeigte 738 deklariert / 31 Browser / **0 OK** — alle
Browser-Cookies landeten als UNDOC, alle deklarierten als ORPH.
Ursache: exact-string-match scheitert bei Suffix-Cookies.
_norm_for_match() + _matches() Helper:
- Strippt Wildcards (`*`, `.*`, `<id>`, `{var}`) + Lower-Case
- Erhält führende Underscores (`__cf_bm`, `_ga` sind meaningful)
- Prefix-Match in BEIDE Richtungen, min 3 Chars (kein "_"-Garbage)
build_cookie_inventory():
- Für jeden Browser-Cookie: längster Prefix-Match in declared wählen
- browser-to-decl Index + decl-match-Index für O(N×M) → O(N+M)
- matched browser-keys werden aus all_keys entfernt → kein
Double-Count (vorher: ORPH + UNDOC parallel)
Realistischer BMW-Match-Test:
declared=[_ga, _gid, __cf_bm, AMP_TOKEN, _fbp, intercom-session,
_pk_id.*, OptanonConsent]
browser= [_ga_K8YL3M9T, _gid_xyz, __cf_bm_actual_hash,
AMP_TOKEN_runtime, _fbp_123, intercom-session-2026,
_pk_id.5.7d8, OptanonConsent]
→ 8 OK (vorher 0)
BMW-GT-File (zeroclaw/docs/ground-truth/bmw_de_2026-06-07.json):
- OneTrust CMP + 14 erwartete Vendoren
- Cookie-Count-Ranges (browser 80-250, deklariert 300-800)
- 7 expected findings inkl. neuem COOKIE-INVENTORY-MATCH-001 als
Benchmark gegen den Fuzzy-Match-Bug
Tests: 14/14 grün (4 _norm_for_match + 5 _matches + 5
build_cookie_inventory inkl. realistic_bmw_pattern).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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b16130369a |
feat(b17): Stufe 4 banner-tour + Stufe 5 annotierte Screenshots + V2-default
Stufe 4 — Cookie-Banner-Tour vor dem Accept-Klick:
- audit_walk_banner_tour.tour_cookie_banner(): öffnet Settings
(16 Phrase-Varianten), scrollt vertikal, aktiviert jedes
[role=tab], expandet jedes [aria-expanded=false] / details /
summary + 14 CMP-spezifische Selektoren. Max 35 Klicks,
Best-Effort.
- audit_walk_recorder ruft tour_cookie_banner() VOR
_try_accept_banner auf — Reviewer sieht den vollen Consent-
Katalog im Video (Vendor-Liste, Kategorien, Zwecke).
- Recorder unter 500 LOC (412+155 split).
Stufe 5 — Annotierte Screenshots pro Finding:
- finding_annotator.annotate_url(): WebKit headless, JS-Inject
eines rot-banner-Labels oben + roter Outline um das Element
(Selector oder Text-Match).
- finding_annotator.annotate_findings(): dispatched 3 Cases —
B1 Tap-Target (Anchor markiert mit "Tap-Target X×Y px"),
B16 URL-Slug-Drift (404-Seite mit "/<slug> 404"),
B13 Widerruf (Footer markiert "Widerruf-Link fehlt").
- routes_audit_walk.POST /annotate-findings (consent-tester).
- _b17_wiring ruft annotate-findings nach record_audit_walk und
speichert annotations in walk.annotations.
- audit_walk_zip_builder packt PNGs nach findings/<name>.png ins
ZIP — Reviewer hat Beweis-Bilder im Postfach.
Plausibility Circuit-Breaker:
- Nach 6 consecutive empty batches (PLAUSIBILITY_EMPTY_BUDGET=6)
bricht die ganze Phase ab statt 200 Calls zu warten. Fix für
qwen3-down + große DSE-Sites (BMW: ohne Breaker 21min, mit
Breaker ~3min).
audit_walk_zip_builder fängt walk.annotations ab und legt sie unter
findings/<fname>.png im ZIP-Anhang ab.
V2-Default:
- docker-compose.yml backend-compliance.environment.MAIL_RENDER_V2:
default 'true'. Ohne diesen Override liefert die Engine
weiterhin das alte Legacy-Mail-Layout, in dem die B-Wiring-
Blöcke nicht sichtbar sind.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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e8ff75cbfe |
feat: Backlog 1-5 — soft-hints, chatbot-discovery, API-payload, LLM-Agent
5 Backlog-Items aus dem Multi-Site-Briefing in einem Sprint:
1. B13 B2C-Soft-Hints — Versicherungs/Tarif/Buchungs-Marker
_B2C_WEAK erweitert um "Reiseversicherung", "Tarifrechner",
"Online-Antrag", "Flug buchen", "Stromtarif" etc.
Fängt Allianz-Reise-Chatbot (vorher False-Negative).
2. Chatbot-Policy-Discovery (chatbot_policy_discovery.py)
Probt 14 Standard-Slugs (privacypolicychatbot, chatbot-datenschutz,
ai-policy, ki-datenschutz, ...) × 5 Lang-Prefixe auf jeder
submitted Origin. Successful >300-Wort-Findings werden in
doc_texts['dse'] gemerged. Audit-Trail über
doc_entries[dse].chatbot_policy_sources.
Hebt Westfield-iAdvize-Lücke.
3. API-Response-Payload erweitert
phase_f_persist.response um extra_findings, audit_walk und
html_blocks erweitert. B-Wiring-Output (B1, B3-B18) ist nicht
mehr nur im Mail-HTML versteckt — externe Aufrufer sehen jeden
Finding. Schema additiv, legacy clients ignorieren neue Felder.
4. Plausibility-LLM Empty-Response-Fix
Resilienz-Strategie A→B→C→D:
A) format='json' (strict, default)
B) format='' (loose, _try_extract_json mit ```json-fence + prose-
wrap-Unterstützung)
C) Split-Batch-Recursion (vorhanden)
D) Give up, leeres dict (callers behandeln als skipped)
Plus _post_llm() als isolierter LLM-Call-Helper, catched
Network-Errors.
5. Specialist-Agents Phase 2 LLM (MVP) — Impressum-Agent
impressum_agent_llm.py: qwen3:30b-a3b mit § 5 TMG System-Prompt,
business_scope-hints aus profile_dict. Output identisches Schema
wie pattern-agent für ein Merge ohne API-Bruch.
_b18_wiring.py orchestriert beide Agents + deduplet nach
field_id, rendert lila V2-Block mit KB/LLM-Tags pro Finding.
Pattern-first im Dedup (deterministisch + stable).
Tests: 107/107 grün (7 Test-Suites + chatbot-discovery + b18).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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a2cae94526 |
fix(b9)+test: real-world false-positives + multi-site GT-bench
Real-World-Smoke gegen Westfield Hamburg (englische DSE) deckte
B9-Bug auf: Pattern matched "If mfi Immobilien Marketing GmbH",
"Discover our Se", "Centre Se" usw. als angebliche Entitäten —
englische Connector-Worte + abgeschnittene "Services"-Strings.
B9 Fix:
- _name_is_blocked() strenger: min 2 Worte, mind. einer ≥4 Chars
UND capitalized (vor Legal-Form-Suffix). Filtert "Se", "ag",
"If ...", "Centre Se" zuverlässig.
- _clean_entity_name() strippt jetzt führende Lowercase-
Connector-Worte (kontextuelle Verben wie "by", "If",
"according to").
- _dedup_substring() collapses
"mfi Immobilien Marketing GmbH" + "Marketing GmbH" zum längeren.
- Anwendung sowohl im HRB-Pfad als auch im Fallback-Pfad.
Multi-Site-Bench (2 neue GTs, 2 Engine-Runs):
- zeroclaw/docs/ground-truth/westfield_hamburg_2026-06-07.json:
iAdvize-Chatbot bekannt, Unibail-Management-Verantwortlicher.
- zeroclaw/docs/ground-truth/allianz_reise_chatbot_2026-06-07.json:
Twilio-Infrastruktur (US-Transfer), lit. f + 2-Mo-Retention.
- zeroclaw/docs/audits/2026-06-07-multi-site-walk-results.md:
Sprint-Briefing mit Detektor × Site Matrix, Audit-Walk-DSMS-
CIDs, identifizierte Real-World-Bugs + Backlog.
Audit-Walk-Endstand (B17 Stufen 1-3):
- Westfield: 400 KB Video, CID Qm…WJYfYDt…BXgwt
- Allianz: 1 MB Video, CID Qm…XFuiC4z…9mSMM
Beide DSMS-persistiert, Reviewer kann jederzeit verifizieren.
Tests: 21/21 grün (test_impressum/test_elli_gt_coverage).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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c7d2038ad9 |
feat(b17): DSMS-CID-Anchor für Audit-Walk-Video (Stufe 3, #7)
Video + walk.json werden nach Aufnahme zu DSMS-IPFS hochgeladen.
Die zurückgegebenen CIDs sind manipulationssichere Audit-Anker —
Reviewer können das Walk-Video Monate später noch verifizieren und
auf Unverändertheit prüfen.
consent-tester:
- _upload_to_dsms(): Best-Effort-Upload zu /api/v1/documents
(Bearer-Token, document_type=audit_walk_video|meta). DSMS-Down
bricht den Walk nicht ab — CID fehlt einfach im result.
- record_audit_walk(): nach video.webm + walk.json erzeugt, beide
hochladen. walk.json wird re-written sodass es BEIDE CIDs
selbstreferenziell enthält.
- ENV: DSMS_GATEWAY_URL + DSMS_BEARER konfigurierbar.
backend:
- _b17_wiring._publicize_gateway_url(): DSMS gibt intern
http://dsms-node:8080/ipfs/{cid} zurück. Für die Audit-Mail
wird das via env DSMS_PUBLIC_GATEWAY (default
https://dsms-dev.breakpilot.ai) durch eine extern erreichbare
URL ersetzt.
- Render-Block: gelber DSMS-Anchor-Hinweis mit Video-CID +
walk.json-CID, beide als klickbare Links zur public Gateway.
Real-World-Smoke gegen Elli:
- Video-CID: QmbdFwtSymPuWGYYdC6eNZ1eEvVLsTYmoRRxEo5L6BXgwt
- walk.json-CID: QmWaTqwZq4KVd5wYFVAKB12uZtAosPqoG1X4m1azysXYJi
- DSMS-Upload erfolgreich, gateway_url im response
Tests: 12/12 grün (+2 für DSMS-Anchor-Render-Pfade inkl.
Internal-Host → Public-Gateway-Rewrite).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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80c4778017 |
feat(b17): Akkordeon-Expansion im Audit-Walk (Stufe 2, #7)
Nach jedem Compliance-Doc-Aufruf werden alle Akkordeons /
<details> / [aria-expanded=false] / Trigger-Patterns geklickt
und im Video aufgenommen.
- _expand_accordions(): 7 Selektor-Patterns, max 25 Expansionen
pro Seite, Dedup nach inner_text (verhindert Endlos-Loops bei
nesteten Strukturen). Scroll-into-view + click + 400ms warten
sicher dass das Klick-Result im Video erfasst wird.
- _visit_link(): Returns (nav_event, expand_event) Tuple. Expand
läuft nur bei HTTP 2xx + ohne nav-error.
- 1500ms post-expand wait gibt der Kamera Zeit, den finalen
Zustand mitzuschneiden.
Backend B17 render: "expand_accordions" Action wird als "5
Akkordeon/Details-Sektion(en) entfaltet" gerendert. Bei 0:
"Keine Akkordeons gefunden" (neutraler Hinweis, kein Fehler).
Real-World-Smoke gegen Elli:
Impressum: 0 Akkordeons (keine)
Datenschutzerkl: 5 Akkordeons aufgeklappt
Nutzungsbeding: 0 Akkordeons
Video-Größe verdoppelt sich (581 KB → 1.14 MB) — Reviewer sieht
jetzt den vollen DSE-Vendor-Tabellen-Inhalt im Video.
Tests: 10/10 grün (+2 für Akkordeon-Render-Pfade).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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cb4b352846 |
feat(b17): Playwright Audit-Walk-Video (Stufe 1, #7)
Nimmt einen kompletten Site-Walk als WebKit-Browser-Session
inkl. Video auf. Reviewer kann nachträglich exakt nachvollziehen,
wie die Engine zum Befund kam.
consent-tester:
- services/audit_walk_recorder.py: Playwright record_video_dir,
iPhone-Viewport-free 1280×800. Goto homepage → Banner-Accept
(Best-Effort: 12 Text-Phrasen + 5 CMP-Fallback-Selektoren) →
Footer-Links sammeln (compliance-relevant gefiltert) →
pro Link navigate + Dwell-Time → JSON-Action-Index mit
UTC-Timestamps + SHA-256 vom Video als Manipulation-Schutz.
- routes_audit_walk.py: POST /scan-audit-walk; statische
Serves für /audit-walks/{walk_id}/video.webm + walk.json.
- main.py: Router registriert.
backend:
- _b17_wiring.py: Triggert /scan-audit-walk, speichert
Walk-Metadata in state["audit_walk"]. Render-Block mit
HTML-Tabelle aller Actions (HH:MM:SS + Aktion + Detail) +
Links zu Video und walk.json.
- _orchestrator.py: run_b17 nach run_b16, async-aufgerufen.
- mail_render_v2/_compose.py: audit_walk_html im V2-Layout.
- test_b17_audit_walk.py: 8 Tests (Render-Pfade + Wiring).
Stufe-2 (Akkordeon-Expansion) und Stufe-3 (DSMS-CID-Anchor)
folgen separat.
Real-World-Smoke gegen Elli:
- 581 KB Video, SHA-256 verifizierbar
- 3 Footer-Links besucht (Impressum, Datenschutzerkl., Nutzungs-)
- 6 Actions im JSON-Index
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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529c032641 |
fix(b9+b14): Real-World-Smoke-Befunde aus Elli-Audit (2026-06-07)
Smoke gegen www.elli.eco hat 3 Bugs offengelegt, die in den
synthetischen Tests nicht greifbar waren — Real-Texte haben
Abkürzungen, HTML-Stripping-Artefakte, andere Formulierungen.
B9 Multi-Entity-Impressum — vorher: 13 "Entities" statt 2.
- Block-Boundary jetzt HRB-Anker-basiert (jeder HRB-Eintrag
markiert eine Entity). Robuster als Legal-Form-Anker, der bei
"Programmierung der Webseite Acme GmbH" über-matchte.
- _NAME_BLOCKLIST gegen 11 typische False-Positives
(programmierung, webseite, umsatzsteueridentifik, ...).
- _LEADING_NOISE_RE strippt Email-TLD-Artefakte ("eco "),
deutsche Artikel ("Die "), URL-Fragmente.
- _USTID_PAT fängt jetzt auch die Vollform
("Umsatzsteueridentifikationsnummer der … ist DE…") über eine
zweite Pattern-Alternative mit [\s\S]{0,80}? Bridge.
- Dedup gleicher Entity-Namen — Mehrfacherwähnung in einem Doc
zählt als EINE Entity.
- Fallback auf alten Legal-Form-Anker wenn keine HRBs vorhanden
(z.B. e.V. ohne HR-Pflicht).
B14 Retention-Conflict — Anchor-Liste erweitert:
- "protokolldat" / "protokollierung der zugriffe" /
"zugriffsdat" / "zugriffsprotokoll" als zusätzliche
Logfile-Anchors (Elli's reale DSE-Wortwahl statt "Logfile").
B15 AI-Legal-Basis — kein Code-Fix. Elli's aktuelle DSE enthält
keine LLM-Provider-Erwähnung mehr; der GT-Anker (2026-06-06) ist
seither veraltet. 0 Findings ist korrekt für den aktuellen Stand.
Tests: 3 neue Real-World-Regression-Tests in
test_impressum_multi_entity_check.py::TestRealWorldElliPattern.
Combined: 75/75 grün.
Real-World-Smoke gegen Elli (HTTP→Text via crude strip):
B9: Entities 13→2 ✓, IMPRESSUM-MULTI-UST_ID → VW ✓
B13: 1 Finding (b2c_strong) ✓
B14: 0 (Elli hat aktuell nur EINEN Retention-Wert für Logs)
B15: 0 (LLM nicht erwähnt, korrekt)
B16: 3 Findings (impressum/dse/cookie Standard-Slug-Brüche) ✓
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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4cad0a29ad |
fix(company-profile): deserialize JSONB columns in row_to_response
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Raw text() queries return JSONB columns as JSON-encoded Python strings, not as Python list/dict objects. The existing isinstance check then fails and silently falls back to defaults — so list-valued fields like target_markets, offerings, processing_systems, ai_systems were always returned as their defaults regardless of stored content. Add a JSON-decode pass over _JSONB_FIELDS before the type check. Verified: PATCH of target_markets=["DE","EU"] now round-trips through GET correctly. Previously the DB had the right data but GET returned ["DE"] (the default). Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> |
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5958b575b1 |
fix(company-profile): replace :param::jsonb with CAST(:param AS JSONB)
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SQLAlchemy's text() parser treats `:name::jsonb` ambiguously when the
trailing `::jsonb` follows immediately — psycopg2 receives the literal
`:name::jsonb` string and raises a SyntaxError because `:` isn't a
psycopg2 placeholder syntax.
The fix uses ANSI CAST(:name AS JSONB) which is semantically identical
in PostgreSQL but lets SQLAlchemy unambiguously substitute the
parameter.
Effects: PATCH and POST/upsert on /api/v1/company-profile now actually
update the row. Before this fix both endpoints returned 500 (or 200
with stale data) and never persisted edits.
Files touched:
- _company_profile_sql.py (build_upsert_params / execute_update /
execute_insert): 12 JSONB columns
- company_profile_service.py: PATCH dynamic JSONB column,
audit log insert
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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