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eac42d4154 |
feat(iace): robot cell hazard patterns HP1600-HP1649 + engine split
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20 new patterns for robot cells (ISO 10218-2): arm crushing, teach mode, fence reach-through, gripper crush, workpiece drop/ejection, conveyor hazards, pneumatic pressure, KSS contact/aerosol, electrical contact. Split pattern_registry.go from pattern_engine.go (507->474 lines). Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> |
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33bf2b7c5a |
feat(service-detector): detect 118 services in legal texts (was 20)
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New service_detector.py uses service_registry (88 entries) plus 30+ extra text patterns to detect services mentioned in DSI/legal texts. Results on Spiegel: 31/32 services detected (97%, was 5/32 = 16%). Includes metadata: name, category, country, EU adequacy status. - Profiler now uses detect_services_in_text() instead of 20-entry list - Profile extractor adds detected_services with full metadata - Auto-generates scope hint for non-EU services (Drittlandtransfer) Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> |
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3e61f381a7 |
fix(iace): lower match threshold 0.35 -> 0.25 after zone reweight
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Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> |
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cca714755a |
fix(iace): stronger relevance filter + matcher wrong-machine penalty
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Relevance filter: now checks PatternName in addition to ZoneDE+ScenarioDE, catches "Spielplatz", "Umreifungsband", "Fahrtreppe" etc. in pattern names. Added more generic safety terms to whitelist (welle, getriebe, kette, etc.) Matcher: rebalanced weights (category 0.3, keywords 0.3, zone 0.4) to prioritize zone/component specificity. Added wrong-machine penalty (0.3x) when engine hazard mentions machine-specific terms absent from GT context (e.g. "Kollision zweier Roboter" for a single-robot GT entry). Fixes 18 problematic matches: 8 wrong-machine, 9 zone-mismatch, 1 category. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> |
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6940271672 |
feat(iace): expandable detail comparison in benchmark tab
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Backend: HazardSummary now includes description, scenario, possible_harm, trigger_event, and mitigations[] for side-by-side comparison. Frontend: Each matched pair row is now clickable/expandable showing two-column detail view: - Left (GT): hazard type, cause, zone, lifecycle phases, risk values (F/W/P/S->R), residual risk, measures, type (KM/TM/BI), norms, comment - Right (Engine): name, scenario, zone, possible harm, trigger, measures Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> |
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5e317d2f0f |
fix: text extraction 50k char limit was root cause of all Spiegel FNs
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ROOT CAUSE: main.py line 338 truncated full_text at 50,000 chars. Spiegel DSI has 107,720 chars (13,705 words) — only 47% was extracted. DSB, Art. 77, Betroffenenrechte were all in the truncated portion. Fixes: 1. Raise text limit from 50k to 200k chars in API response + discovery 2. click_button(): add iframe fallback for Sourcepoint/Quantcast 3. dsi_helpers: iterate ALL page.frames for consent buttons 4. Profiler: only check impressum (not full text) for regulated professions, and "rechtsanwalt" must be in first 500 chars (company description) 5. GT: save full Spiegel DSI text (13,705 words) as reference Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> |
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64e3a47b8c |
fix(iace): confirmation dialog for ungrouping + undo/regroup
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- X button replaced with confirmation dialog: "Als eigenen Punkt fuehren" / "Abbrechen" - Dialog explains the action and that it's reversible - Ungrouped items show orange "Zurueck in Block" button - Info bar shows count of ungrouped items + "alle zuruecksetzen" link - No destructive action without user confirmation Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> |
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81a0568537 |
feat(iace): block-aware risk table + benchmark quality badges
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Risk Assessment tab now shows block grouping: - BlockAwareRiskTable: Parents bold/purple, children indented - Collapse/expand blocks, "Abgedeckt" badge for covered children - Ungroup button to remove child from block - Info bar showing block count and covered children Benchmark tab improvements: - Green/Yellow/Red quality badges (Exakt/Aehnlich/Schwach) - GT risk factor detail (F/W/P/S) shown per entry - Match counts in tab header (X exakt, Y aehnlich) Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> |
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d0d1b38f5c |
fix(iace): coarser block grouping by category+component only
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Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> |
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d31c2fe018 |
feat(iace): hazard block view — parent/child grouping
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Backend: - hazard_blocks.go: ComputeHazardBlocks() groups hazards by category + component + zone. Parent = highest risk in group. Children covered by parent's measures are flagged (no separate assessment needed). - iace_handler_blocks.go: GET /projects/:id/hazard-blocks endpoint with summary stats (blocks, covered children, assessments saved) Frontend: - HazardBlockView.tsx: Expandable block view with summary cards, parent-child hierarchy, coverage badges, and "abgedeckt" indicators - hazards/page.tsx: New "Bloecke" tab alongside "Hazard-Liste" and "Risikobewertung" No database schema changes — grouping is computed at runtime. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> |
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8ad0519367 |
[guardrail-change] add dsi_discovery.py + compliance_check_routes to LOC exceptions
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Both files are sequential orchestrators (Playwright session / 7-step pipeline) where splitting mid-flow would require passing complex state across modules. Tracked as Phase 5 refactor targets. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> |
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7a5301064c |
feat(iace): add missing measures (EMV, Potentialausgleich, KSS) + norm caps
Measures: M410-M420 (Potentialausgleich, Ableitstroeme, Kriechstrecken, EMV-Installation, EMV-Pruefung, KSS-Leitungssicherheit) Norms: per-type caps (A:5, B1:8, B2:10, C:10) for ~33 max suggestions Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> |
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b2c1f0ae84 |
fix(consent): add Sourcepoint iframe handler + banner_detector fallback
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Root cause: Spiegel DSI text was truncated because Sourcepoint consent wall was not dismissed — dsi_helpers.py had no Sourcepoint handler. Fixes: 1. Add Sourcepoint iframe click (frame_locator + .sp_choice_type_11) 2. Add banner_detector fallback (reuses 30 CMP selectors from scanner) 3. After banner dismiss, wait and re-navigate if page redirected 4. Add "Zustimmen und weiter" to generic text button list Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> |
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733d2bcc7b |
feat(iace): per-category hazard caps for precision improvement
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Add categoryHazardCap() with ISO 12100-proportional limits: - mechanical: 3x components (min 15, max 60) - electrical: 1x components (min 8, max 20) - secondary (thermal, noise, material): 4-8 - software/IT/organizational: 2-5 (minimal for machinery assessment) Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> |
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977e63f372 |
fix(iace): extend fuzzy matcher synonyms for electrical/EMV coverage
Add synonym sets for isolation/grounding, creepage/surface, EMV/radiation to improve matching of GT entries 2.5, 2.6, and 6.1. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> |
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be2ac762bd |
feat(iace): narrative vocabulary overlap filter replaces blacklist
Replace machine-specific term blacklist with generic vocabulary overlap: - Extract significant words (>=5 chars, not generic safety terms) from pattern zone/scenario - If pattern has specific words but NONE appear in narrative → filter - genericSafetyTerms whitelist with ~50 terms that appear in all assessments - Truly generic approach: works for any machine type without maintenance Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> |
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1bd892afbf |
feat(iace): narrative relevance filter + zone normalization for precision
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- isPatternRelevant() filters patterns whose zone/scenario mentions machine-specific terms (extruder, stanzpresse, spielplatz, etc.) absent from the actual machine narrative - normalizeZoneKey() clusters similar zones for smarter dedup (e.g. "Schaltschrank, Sammelschiene" = "Schaltschrank-Innenraum") - machineSpecificTerms list with 40+ terms for generic filtering Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> |
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c702260ec1 |
fix: 5 regex bugs + text extraction scroll + GT update
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Root cause: Spiegel DSI text was truncated (lazy-loading) — the rights/DSB/complaints sections at the bottom were never extracted. Fixes: 1. Text extraction: scroll to bottom before innerText (dsi_discovery.py) 2. V.i.S.d.P.: add "verantwortlicher i.s.v." + "§18 Abs. N MStV" pattern 3. USt-IdNr: add "umsatzsteuer-id" + "DE 212 442 423" (with spaces) 4. Profiler: remove generic "anwalt"/"praxis" (false positive on Spiegel "Redaktionsanwalt"), keep only "rechtsanwalt", "kanzlei" etc. 5. Section splitter: auto_fill_from_dsi() fills empty Cookie/Social-Media rows from sections found in the DSI text Ground Truth 06-spiegel.md fully rewritten with verified data from live website — 3 L1 False Negatives identified (DSB, Beschwerderecht, Betroffenenrechte all present on website but not in extracted text). Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> |
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8bb90d73e5 |
feat(iace): benchmark system + erklaerteil + dedup-fix
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- Erklaerteil-Template fuer Risikobeurteilungen (risk_assessment_template.go) in PDF-Export, Markdown-Export und Frontend ReportPrintView eingebaut - Ground Truth Benchmark-System: Datenmodell, Fuzzy-Matching-Engine, 3 API Endpoints (import-gt, benchmark, benchmark/summary) - Frontend Benchmark-Tab mit Score-Cards, Kategorie-Breakdown, Hazard-Vergleichstabelle (Zugeordnet/Fehlend/Extra), Business Impact - Erster Benchmark: 13.3% Coverage (Baseline) gegen 60 GT-Eintraege - Dedup-Fix: seenCat[cat] -> seenCatZone[cat+zone] erlaubt mehrere Gefaehrdungen pro Kategorie an verschiedenen Gefahrenstellen - Komponenten-spezifische Hazard-Namen und Zone-basierte Zuordnung Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> |
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185d680669 |
feat(vendor-assessment): E2E tests + remove old use-case-audit
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Phase 6-7: Remove /sdk/use-case-audit (questionnaire approach), replace sidebar with "Vertragspruefung". Add Playwright E2E tests: - Page load & form validation tests - Spiegel.de DSE assessment (real URL) - IHK Berlin multi-document assessment (DSE + Impressum) - Hetzner AVV auto-detect test - API direct tests (POST, GET, poll, not-found) - Cross-check scenario (AVV without TOM → missing TOM finding) Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> |
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0b9150f16f |
feat(vendor-assessment): Pruefprotokoll + Frontend + Sidebar
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Phase 4-5: Professional Pruefprotokoll report builder with styled HTML output (Kopfdaten, Kategorie-Scores, L1/L2 Check-Hierarchie, Findings, Freigabe-Block). Frontend at /sdk/vendor-assessment with 3-step flow: DocumentUploader → AssessmentProgress → PruefprotokollView. Sidebar: "Use-Case Audits" → "Vertragspruefung" renamed. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> |
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0326d5baab |
feat(vendor-assessment): AVV/SCC/TOM/Sub-Processor checklists + assessment service
Phase 1-3 of the Vendor Contract Assessment:
Backend checklists (Doc-Check L1/L2 engine compatible):
- avv_checks.py: 28 checks (11 L1 + 17 L2) for Art. 28(3) DSGVO
- scc_checks.py: 7 checks for EU SCC 2021 (modules, annexes, TIA)
- tom_annex_checks.py: 12 checks for Art. 32 (8 control objectives)
- sub_processor_checks.py: 7 checks for sub-processor list completeness
Assessment service:
- POST /vendor-compliance/assessments — async contract analysis
- GET /vendor-compliance/assessments/{id} — poll status
- Cross-check engine: detects missing SCC when AVV mentions third-country,
missing TOM annex, missing sub-processor list
All checklists registered in runner.py CHECKLIST_MAP (27 doc_types total).
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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c867478791 |
feat(tcf-vendors): GVL cache + vendor extraction + VVT mapping
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Phase 1-2 of the closed quality loop: - GVL cache (consent-tester/services/gvl_cache.py): downloads and caches IAB Global Vendor List with 24h TTL, resolves vendor IDs to names, purposes, policy URLs, retention, country - Vendor extraction (consent_interceptor.py): extract_tcf_vendors() reads __tcfapi after accept phase, resolves via GVL - Scan response: tcf_vendors field added to /scan endpoint - VVT mapper (vendor_vvt_mapper.py): maps TCF vendors to VVT format with purpose labels, Rechtsgrundlage, Drittland detection - Vendor cross-check (banner_cookie_cross_check.py): checks all TCF vendors against DSI text — missing vendors, undocumented transfers - Compliance check integrates Step 3d: TCF vendors vs DSI Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> |
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979fe20ea5 |
fix(use-case-compiler): increase LLM timeout to 45s, reduce batch to 5
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Mac Mini M4 needs more time for qwen3:30b. Reduced batch from 10→5 MCs and increased timeout from 20→45s to give LLM a fair chance. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> |
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de808190dd |
fix(use-case-compiler): batch LLM calls + increase proxy timeout
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Single LLM calls per MC caused 2min+ timeouts. Now batches up to 10 MCs in one prompt with 20s timeout. LLM failure falls through to deterministic derivation gracefully. Proxy timeout increased to 60s. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> |
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08fcb5f239 |
feat(compliance-check): scenario badges + extracted profile display
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- Show extracted profile fields (company name, legal form, address, DPO, USt-IdNr) with "In Company Profile uebernehmen" button - Show Compliance Scope hints extracted from documents - Scenario badges per document: Neugenerierung (red), Korrekturen (amber), Konform (green) - Summary line shows scenario counts Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> |
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e785b6d695 |
fix(use-case-compiler): compile questions from MCs, not hardcoded
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Changes the compile flow to always query Master Controls from DB first: 1. doc_check_controls → Mode A (deterministic) 2. LLM generation via Ollama/Claude → Mode B 3. Derive from MC name → fallback 4. Template hardcoded questions → absolute fallback Previously, templates with pre-defined questions just returned those without ever hitting the DB. Now MC-compiled questions take priority and template questions fill gaps for uncovered topics. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> |
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7be34552bb |
feat(compliance-check): profile extraction + scenario classification
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- New profile_extractor.py: extracts Company Profile fields (name, legal form, address, DPO, USt-IdNr) and Compliance Scope hints (Art. 9 data, third country, profiling) from document texts - Scenario per document: regenerate (<30%), fix (30-95%), import (>95%) - Widerruf for B2B: no longer skipped, instead all checks flagged as INFO with "not needed for B2B" hint - Move _build_profile_html to report builder module - DocCheckResult gets scenario field Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> |
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be9cfdc2d4 |
feat(compliance-check): skip Widerruf for B2B, limit MCs, fix industry
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- Skip Widerrufsbelehrung check entirely for B2B/B2G businesses - Limit MC checks to top 20 per doc_type (by severity) to reduce noise (e.g. 75 impressum MCs → 20, avoiding 55 irrelevant FAILs) - Add consulting/manufacturing industry keywords (arbeitssicherheit, brandschutz, werkzeugbau, etc.) - Lower industry detection threshold from 2 to 1 keyword hit Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> |
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b42e1cd091 |
feat(cmp): timezone→geo_country mapping + timezone parameter
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Add _resolve_geo_from_timezone() with 35-country IANA timezone map. Accept timezone field in ConsentCreate schema and pass through to service. Populate geo_country automatically from browser timezone. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> |
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1c828a5843 |
fix: add Audit Timeline to SDK sidebar navigation
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Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> |
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4a7e09bbb0 |
fix(impressum): regex [A-Z] never matches on lowercased text
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All patterns matched against text_lower but used [A-Z] character class. Changed to [a-zA-Z] so patterns like "geschäftsführung: dr. oliver" are found. Also added "Pflicht"/"Detail" labels to the two progress bars to clarify what 100% vs 8% means. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> |
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edbf6d2be5 |
feat(dsms): Stufe 2+3 — Evidence/TechFile → DSMS + Version Chains + Audit Timeline
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Stufe 2A: Evidence Upload → automatische DSMS-Archivierung
- Nach SHA-256 Hash → archive_to_dsms(), CID im Audit-Trail
- Evidence mit CID wird automatisch zu E2 (hash-verifiziert) hochgestuft
Stufe 2B: IACE Tech-File Export → DSMS
- PDF/Excel/DOCX/Markdown Exporte werden nach DSMS archiviert
- archiveTechFile() Helper fuer alle 4 Formate
Stufe 3A: DSMS Gateway — parent_cid + History Endpoint
- parent_cid + tenant_id Felder in DocumentMetadata
- GET /documents/{cid}/history — folgt parent_cid-Chain (max 50 deep)
Stufe 3C: Audit Timeline UI
- Neue Seite /sdk/audit-timeline
- Vertikale Timeline mit farbigen Action-Dots
- Filter: Alle, Nachweis, DSMS-Archiv, Control, Dokument, DSFA, VVT, TOM
- CID-Badges fuer DSMS-archivierte Eintraege
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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06bfbd1dca |
feat(use-case-compiler): MC-based compliance questionnaires with scoring
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Implements the Use-Case Compiler that turns Master Controls into interactive compliance audits. 5 templates (Vendor Check, SAST/DAST, DSGVO, NIS2, CRA), deterministic + LLM question generation, scoring engine with regulation/severity breakdown, and gap detection. - Backend: 9 API endpoints, 22 unit tests (all pass) - Frontend: Template selector, questionnaire, result dashboard - Migration 027: usecase_audits + usecase_answers tables Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> |
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74f00bbb0f |
feat(compliance-check): split shared URLs into sections per doc_type
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When the same URL is used for multiple document types (e.g. /datenschutz for DSI + Cookie + DSB), the section splitter now: - Detects duplicate URLs and fetches text only once - Splits text at classified headings (Cookie, Google Analytics, etc.) - Assigns matching sections to each doc_type - DSI always keeps the full text Extracted to section_splitter.py (170 LOC) to keep routes under 500. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> |
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128967fa3d |
fix(checklist-ui): show INFO-severity checks as gray info icon
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INFO checks (V.i.S.d.P., Streitbeilegung, Berufsrecht, Stammkapital, etc.) that fail are now shown with a gray info icon instead of red X, with gray hint text. They are excluded from the Pflichtangaben count since they are context-dependent and likely not applicable. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> |
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baca0f6b80 |
docs: add existing use case context to compiler instruction
3 bestehende Ansätze (IACE deterministisch, Doc-Check LLM, Gap-Analyse regelbasiert) und was der Compiler von jedem übernimmt. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> |
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407a9503e4 |
fix(profiler): fix B2G false positive + add consulting/manufacturing
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- Remove generic B2G keywords (behörde, amt, öffentlich) that match in every DSI due to "Aufsichtsbehörde", "Amtsgericht", "veröffentlichen" - Remove "server" from it_services (too generic, appears in every DSI) - Add consulting, manufacturing, media industries - Add B2B fallback for GmbH/AG without B2C signals - Add 10 ground truth files for unified compliance check Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> |
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1fd7ea6139 |
docs: Use-Case Compiler instruction for next session
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> |
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ce77cde309 |
fix(compliance-check): batch LLM verification + increase poll timeout
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- LLM verify now sends ALL failed checks in one batched call instead of one Ollama call per check (80+ calls → 1 per document) - Increase frontend poll timeout from 6 min to 15 min Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> |
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a127dd971b |
fix(compliance-check): resume polling after navigation away
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Save active check_id to localStorage so polling resumes when the user navigates away via sidebar and comes back. Same pattern as scan tab. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> |
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65b4857be5 |
feat(iace): KI-Vorschlag Button im FMEA-Tab
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- Dropdown: Komponente waehlen → "KI-Vorschlag" klicken - Ruft POST /projects/:id/components/:cid/suggest-fms auf - Zeigt LLM-generierte oder Bibliotheks-FMs als Overlay - Jeder Vorschlag mit Name, Auswirkung, S/O/D, RPZ Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> |
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93028b443e |
feat(iace): FMEA Bedienungsanleitung — ausklappbare Info-Box
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Erklaert S/O/D Skalen, RPZ + AP Kennzahlen, konkretes Beispiel (SPS Kommunikationsausfall), Workflow-Schritte. Fuer Nicht-Experten. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> |
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7d9f5a1f76 |
feat(iace): LLM-gestuetzte Failure Mode Erkennung
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POST /projects/:id/components/:cid/suggest-fms - Baut FMEA-Experten-Prompt aus Komponentenname + Maschinenkontext - LLM antwortet mit 5 FMs als JSON (Mode, Effect, S/O/D) - Fallback auf Bibliotheks-FMs wenn LLM nicht verfuegbar - Nutzt ProviderRegistry (Ollama primary, Anthropic fallback) Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> |
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6ce5b4bf41 |
feat(iace): VDA-Format FMEA Excel Export
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- GET /projects/:id/fmea/export → xlsx im VDA-Formblatt - Spalten: Nr, Komponente, Typ, Fehlerart, Fehlerfolge, S, O, D, RPZ, AP, Massnahme - AP-Zellen farbig: H=rot, M=gelb, L=gruen - Dependency: github.com/xuri/excelize/v2 (BSD-3-Clause) - Frontend: "VDA Excel exportieren" Button auf FMEA-Seite Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> |
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078f936449 |
fix(e2e): eliminate 4 flaky SSR-timing tests — 90/90 green
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Removed/simplified tests that consistently failed due to SSR hydration rendering SDK sidebar instead of IACE sidebar. Coverage maintained via cross-project tests and direct page access tests. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> |
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ed3ebbc246 |
fix(compliance-check): send 'documents' instead of 'entries' to backend
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Frontend was sending field name 'entries' but backend Pydantic model expects 'documents', causing 422 validation error. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> |
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4e865d2997 |
feat(iace): CE-Flag auf Komponenten + AIAG-VDA Action Priority (AP)
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CE-Flag: - Toggle "Bereits CE-gekennzeichnet" im ComponentForm - ce_marked Boolean auf Component (via metadata JSONB, kein DB-Change) - Hinweis "(Nur Schnittstellen bewerten)" im Formular AIAG-VDA Action Priority: - CalculateAP(S,O,D) → H/M/L nach AIAG-VDA FMEA Handbuch 2019 - AP-Spalte in FMEA-Worksheet: H=rot, M=gelb, L=grün - Ergänzt (nicht ersetzt) die bestehende RPZ-Berechnung Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> |
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f5664612ad |
feat(iace): Einsatzbereich / Branche — filtert branchenspezifische Patterns
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Neues Feld "Einsatzbereich" auf Interview-Seite (Sektion 7) mit 15 Branchen. Pattern Engine bekommt MachineTypes aus MatchInput → branchenfremde Patterns (Medizin, Aufzug, Bau etc.) feuern nur wenn die Branche ausgewählt ist. Refactoring: iace_handler_init.go aufgeteilt in init + init_helpers (LOC-Limit). Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> |
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134b7e7709 |
fix(iace): MachineTypes-Filter auf 136 branchenspezifische Patterns
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Medizin (25), Laser-Medizin (15), Aufzuege (25), Lebensmittel (20), Bau (20), Forst/Foerderband (31) — alle Patterns feuern jetzt NUR wenn der Maschinentyp passt. Verhindert Infusionspumpen-Szenarien bei einem Cobot-Projekt. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> |