Enhance Planning AI Context with Catalog Exercise Hints

- Introduced support for catalog exercise hints in various functions, including `_build_stage_ai_context`, `try_suggest_ai_stage_step`, and `build_gap_fill_goal_text`, improving the contextual information available for AI suggestions.
- Added a new function `catalog_exercise_hints_block` to consolidate hints retrieval from the catalog context.
- Updated the `PlanningIntentContext` to include catalog exercise hints, ensuring they are part of the API output.
- Implemented tests to verify the inclusion of catalog exercise hints in goal text generation and rematch logic.
- Incremented version numbers and updated changelog to reflect these enhancements.

backend/planning_exercise_path_ai_fill.py

@@ -19,6 +19,8 @@ from planning_exercise_form_context import (
     prior_path_steps_before_major,
 )
 from planning_exercise_semantics import PlanningSemanticBrief, brief_to_summary_dict
+from planning_catalog_context import ProgressionPlanningCatalogContext
+from planning_prompt_variables import catalog_exercise_hints_block
 
 _logger = logging.getLogger("shinkan.planning_exercise_path_ai_fill")
 
@@ -54,6 +56,7 @@ def _build_stage_ai_context(
     step_after: Optional[Mapping[str, Any]] = None,
     prior_steps: Optional[Sequence[Mapping[str, Any]]] = None,
     start_situation: Optional[str] = None,
+    catalog_exercise_hints: Optional[str] = None,
 ) -> ExerciseFormAiPromptContext:
     """KI-Kontext für unbesetzte Roadmap-Stufe (keine Brücke zwischen falschen Array-Indizes)."""
     gap = dict(spec.get("gap") or {})
@@ -97,6 +100,9 @@ def _build_stage_ai_context(
     sketch = (spec.get("sketch") or "").strip()
     if sketch and sketch != learning_goal:
         goal_parts.extend(["", f"Kontext: {sketch}"])
+    hints = (catalog_exercise_hints or "").strip()
+    if hints:
+        goal_parts.extend(["", "Katalog-Hinweise Übungsanlage:", hints])
     goal = "\n".join(goal_parts)
 
     focus_hint = topic if brief.topic_type == "technique" else None
@@ -118,6 +124,7 @@ def try_suggest_ai_stage_step(
     brief: PlanningSemanticBrief,
     spec: Mapping[str, Any],
     steps: Sequence[Mapping[str, Any]],
+    catalog: Optional[ProgressionPlanningCatalogContext] = None,
 ) -> Optional[Dict[str, Any]]:
     """KI-Vorschlag für leere Roadmap-Stufe."""
     major_idx = spec.get("roadmap_major_step_index")
@@ -136,6 +143,8 @@ def try_suggest_ai_stage_step(
     if not gap.get("learning_goal"):
         gap["learning_goal"] = spec.get("title_hint") or spec.get("sketch")
 
+    exercise_hints = catalog_exercise_hints_block(cur, catalog) if catalog else ""
+
     ctx = _build_stage_ai_context(
         goal_query=goal_query,
         brief=brief,
@@ -143,6 +152,7 @@ def try_suggest_ai_stage_step(
         step_before=step_before,
         step_after=step_after,
         prior_steps=prior_steps,
+        catalog_exercise_hints=exercise_hints or None,
     )
     try:
         ai_payload = run_exercise_form_ai_suggestion(cur, ctx=ctx)
@@ -189,6 +199,7 @@ def _build_gap_ai_context(
     gap: Mapping[str, Any],
     title_hint: Optional[str] = None,
     sketch_hint: Optional[str] = None,
+    catalog_exercise_hints: Optional[str] = None,
 ) -> ExerciseFormAiPromptContext:
     topic = (brief.primary_topic or "Technik").strip()
     phase = gap.get("expected_phase") or "vertiefung"
@@ -206,6 +217,9 @@ def _build_gap_ai_context(
     ]
     if sketch:
         goal_parts.extend(["", f"Hinweis: {sketch}"])
+    hints = (catalog_exercise_hints or "").strip()
+    if hints:
+        goal_parts.extend(["", "Katalog-Hinweise Übungsanlage:", hints])
     goal = "\n".join(goal_parts)
 
     focus_hint = topic if brief.topic_type == "technique" else None
@@ -271,8 +285,10 @@ def try_suggest_ai_bridge_step(
     gap: Mapping[str, Any],
     title_hint: Optional[str] = None,
     sketch_hint: Optional[str] = None,
+    catalog: Optional[ProgressionPlanningCatalogContext] = None,
 ) -> Optional[Dict[str, Any]]:
     """Ruft exercise AI suggest auf — kein Speichern in DB."""
+    exercise_hints = catalog_exercise_hints_block(cur, catalog) if catalog else ""
     ctx = _build_gap_ai_context(
         goal_query=goal_query,
         brief=brief,
@@ -281,6 +297,7 @@ def try_suggest_ai_bridge_step(
         gap=gap,
         title_hint=title_hint,
         sketch_hint=sketch_hint,
+        catalog_exercise_hints=exercise_hints or None,
     )
     g_plain = strip_html_to_plain(ctx.goal)
     if not g_plain.strip() and not (ctx.title or "").strip():
@@ -478,6 +495,7 @@ def build_gap_fill_goal_text(
     step_a: Optional[Mapping[str, Any]] = None,
     step_b: Optional[Mapping[str, Any]] = None,
     roadmap_snapshot: Optional[Mapping[str, Any]] = None,
+    catalog_exercise_hints: Optional[str] = None,
 ) -> str:
     """Ausführlicher Zieltext für KI-Neuanlage aus Pfad-, Roadmap- und Stufen-Kontext."""
     topic = (brief.primary_topic or "Technik").strip()
@@ -555,6 +573,9 @@ def build_gap_fill_goal_text(
         parts.append(f"Qualitätsprüfung: {spec['rationale']}")
     if spec.get("sketch"):
         parts.append(f"Skizze: {spec['sketch']}")
+    hints = (catalog_exercise_hints or "").strip()
+    if hints:
+        parts.append(f"Katalog-Hinweise Übungsanlage:\n{hints}")
     parts.append(
         "Die Übung muss die Stufe didaktisch erfüllen: klare Voraussetzungen, messbares Stufenziel, "
         "Bezug zum Gesamtpfad — keine generische Kraftübung ohne Technikbezug. "
@@ -571,6 +592,7 @@ def build_gap_fill_offer(
     brief: Optional[PlanningSemanticBrief] = None,
     proposal: Optional[Mapping[str, Any]] = None,
     roadmap_snapshot: Optional[Mapping[str, Any]] = None,
+    catalog_exercise_hints: Optional[str] = None,
 ) -> Dict[str, Any]:
     source = spec.get("source")
     idx = int(spec.get("insert_after_index") or 0)
@@ -603,6 +625,7 @@ def build_gap_fill_offer(
             step_a=step_a,
             step_b=step_b,
             roadmap_snapshot=enriched_snapshot or None,
+            catalog_exercise_hints=catalog_exercise_hints,
         )
     ctx_preview = enriched_snapshot or None
     offer: Dict[str, Any] = {
@@ -642,6 +665,7 @@ def apply_gap_fill_after_qa(
     max_ai_proposals: int = 3,
     auto_insert_proposals: bool = False,
     roadmap_snapshot: Optional[Mapping[str, Any]] = None,
+    catalog: Optional[ProgressionPlanningCatalogContext] = None,
 ) -> Tuple[List[Dict[str, Any]], List[Dict[str, Any]], List[Dict[str, Any]]]:
     """
     Erzeugt gap_fill_offers für die UI; optional KI-Vorschläge einfügen.
@@ -653,6 +677,7 @@ def apply_gap_fill_after_qa(
     out = list(steps)
     proposals: List[Dict[str, Any]] = []
     offers: List[Dict[str, Any]] = []
+    exercise_hints = catalog_exercise_hints_block(cur, catalog) if catalog else ""
 
     for spec in specs:
         source = spec.get("source")
@@ -666,6 +691,7 @@ def apply_gap_fill_after_qa(
                     brief=brief,
                     spec=spec,
                     steps=out,
+                    catalog=catalog,
                 )
             offer = build_gap_fill_offer(
                 spec=spec,
@@ -674,6 +700,7 @@ def apply_gap_fill_after_qa(
                 brief=brief,
                 proposal=proposal,
                 roadmap_snapshot=roadmap_snapshot,
+                catalog_exercise_hints=exercise_hints or None,
             )
             offers.append(offer)
             if proposal and auto_insert_proposals:
@@ -700,6 +727,7 @@ def apply_gap_fill_after_qa(
                 brief=brief,
                 proposal=None,
                 roadmap_snapshot=roadmap_snapshot,
+                catalog_exercise_hints=exercise_hints or None,
             )
             offers.append(offer)
             continue
@@ -719,6 +747,7 @@ def apply_gap_fill_after_qa(
                 gap=gap,
                 title_hint=str(spec.get("title_hint") or ""),
                 sketch_hint=str(spec.get("sketch") or ""),
+                catalog=catalog,
             )
 
         offer = build_gap_fill_offer(
@@ -728,6 +757,7 @@ def apply_gap_fill_after_qa(
             brief=brief,
             proposal=proposal,
             roadmap_snapshot=roadmap_snapshot,
+            catalog_exercise_hints=exercise_hints or None,
         )
         offers.append(offer)
 

backend/planning_exercise_path_builder.py

@@ -24,9 +24,11 @@ from planning_catalog_context import (
     load_catalog_context_from_graph_row,
     merge_catalog_context_into_target,
 )
+from catalog_prompt_slots import get_rematch_guard_for_catalog
 from planning_exercise_profiles import PlanningTargetProfile
 from planning_path_qa_pipeline import run_multistage_path_qa
 from planning_path_rematch import (
+    apply_rematch_catalog_guard,
     collect_rematch_slot_indices,
     filter_rematch_slot_indices,
     prune_stripped_after_rematch,
@@ -1737,6 +1739,13 @@ def _run_roadmap_rematch_loop(
             stripped_off_topic=current_stripped if round_idx == 0 else [],
             off_topic_steps=off_topic_before_strip if round_idx == 0 and use_initial_off_topic else [],
         )
+        catalog_context = _resolve_planning_catalog_context(cur, body)
+        rematch_guard = get_rematch_guard_for_catalog(cur, catalog_context)
+        slot_indices = apply_rematch_catalog_guard(
+            slot_indices,
+            rematch_reasons,
+            rematch_guard=rematch_guard,
+        )
         if not slot_indices:
             break
 
@@ -2191,6 +2200,7 @@ def _run_evaluate_only_path_qa(
                 max_ai_proposals=0,
                 auto_insert_proposals=False,
                 roadmap_snapshot=path_roadmap_snapshot,
+                catalog=catalog_context,
             )
 
     multistage_qa = run_multistage_path_qa(
@@ -4294,6 +4304,7 @@ def suggest_progression_path(
                 max_ai_proposals=0,
                 auto_insert_proposals=False,
                 roadmap_snapshot=path_roadmap_snapshot,
+                catalog=catalog_context,
             )
 
     if roadmap_gap_offers:

backend/planning_intent_context.py

@@ -49,9 +49,10 @@ class PlanningIntentContext:
     context_notes: str = ""
     topic_type: str = "general"
     technique_sibling_excludes: List[str] = field(default_factory=list)
+    catalog_exercise_hints: Optional[str] = None
 
     def to_api_dict(self) -> Dict[str, Any]:
-        return {
+        out = {
             "source_query": self.source_query,
             "primary_topic": self.primary_topic,
             "topic_type": self.topic_type,
@@ -61,6 +62,10 @@ class PlanningIntentContext:
             "technique_sibling_excludes": self.technique_sibling_excludes[:16],
             "context_notes": self.context_notes[:1200] or None,
         }
+        hints = (self.catalog_exercise_hints or "").strip()
+        if hints:
+            out["catalog_exercise_hints"] = hints[:2000]
+        return out
 
 
 def build_planning_intent_context(
@@ -70,6 +75,8 @@ def build_planning_intent_context(
     goal_analysis: Optional[Mapping[str, Any]] = None,
     extra_context: Optional[str] = None,
     primary_topic: Optional[str] = None,
+    cur=None,
+    catalog=None,
 ) -> PlanningIntentContext:
     """Intent aus Anfrage, Zielanalyse und optionalem Kontext — ohne Sonderregeln pro Thema."""
     ga = dict(goal_analysis or {})
@@ -124,6 +131,12 @@ def build_planning_intent_context(
         if line not in path_success:
             path_success.insert(0, line)
 
+    catalog_hints = ""
+    if cur is not None and catalog is not None:
+        from planning_prompt_variables import catalog_exercise_hints_block
+
+        catalog_hints = catalog_exercise_hints_block(cur, catalog)
+
     return PlanningIntentContext(
         source_query=(goal_query or "").strip(),
         primary_topic=topic,
@@ -133,6 +146,7 @@ def build_planning_intent_context(
         explicit_exclusions=explicit,
         technique_sibling_excludes=siblings[:16],
         context_notes=combined_notes[:1200],
+        catalog_exercise_hints=catalog_hints or None,
     )
 
 

backend/planning_path_rematch.py

@@ -7,6 +7,8 @@ from typing import Any, Dict, List, Mapping, Optional, Sequence, Set, Tuple
 
 from planning_progression_roadmap import ProgressionRoadmapContext, StageSpecArtifact
 
+_UNFILLED_REMATCH_REASON = "Keine passende Übung für Roadmap-Stufe"
+
 
 def _slot_priority_for_rematch(
     body,
@@ -103,7 +105,7 @@ def collect_rematch_slot_indices(
         if isinstance(item, (list, tuple)) and len(item) >= 2:
             idx, spec = item[0], item[1]
             midx = getattr(spec, "major_step_index", idx)
-            _register(int(midx), "Keine passende Übung für Roadmap-Stufe")
+            _register(int(midx), _UNFILLED_REMATCH_REASON)
         elif isinstance(item, dict):
             midx = _resolve_major(item)
             if midx is not None:
@@ -114,6 +116,25 @@ def collect_rematch_slot_indices(
     return indices, reasons
 
 
+def apply_rematch_catalog_guard(
+    slot_indices: Set[int],
+    rematch_reasons: Mapping[int, str],
+    *,
+    rematch_guard: Optional[str],
+) -> Set[int]:
+    """
+    Bei gesetztem rematch_guard (Katalog-Slot): kein Auto-Rematch nur wegen leerer Roadmap-Stufe.
+    Off-Topic, explizite QS-Hinweise und Stufen-Verfeinerung bleiben aktiv.
+    """
+    if not (rematch_guard or "").strip():
+        return set(slot_indices)
+    return {
+        idx
+        for idx in slot_indices
+        if str(rematch_reasons.get(idx) or "").strip() != _UNFILLED_REMATCH_REASON
+    }
+
+
 def filter_rematch_slot_indices(
     steps: Sequence[Mapping[str, Any]],
     slot_indices: Set[int],
@@ -372,6 +393,7 @@ def prune_stripped_after_rematch(
 
 
 __all__ = [
+    "apply_rematch_catalog_guard",
     "collect_rematch_slot_indices",
     "filter_rematch_slot_indices",
     "prune_stripped_after_rematch",

backend/planning_progression_roadmap.py

@@ -1244,6 +1244,8 @@ def run_progression_roadmap_pipeline(
             resolved.target_state,
         ),
         primary_topic=goal_analysis.primary_topic,
+        cur=cur,
+        catalog=catalog,
     )
 
     heuristic_specs = build_stage_specs(

backend/planning_prompt_variables.py

@@ -34,6 +34,27 @@ _PLANNING_PROMPT_VARIABLE_PROVIDERS: tuple[PlanningPromptVariableProvider, ...]
 )
 
 
+def catalog_exercise_hints_block(
+    cur,
+    catalog: Optional[ProgressionPlanningCatalogContext] = None,
+    *,
+    slug: Optional[str] = None,
+) -> str:
+    """Kombinierte hints_on_exercise aus aktivem Katalog-Kontext (Gap-Fill / Intent)."""
+    if cur is None or catalog is None:
+        return ""
+    from catalog_prompt_slots import CATALOG_KINDS, placeholder_key
+
+    merged = merge_planning_prompt_variables(cur, {}, catalog=catalog, slug=slug)
+    lines: list[str] = []
+    for cfg in CATALOG_KINDS:
+        key = placeholder_key(cfg.kind, "hints_on_exercise")
+        text = (merged.get(key) or "").strip()
+        if text:
+            lines.append(f"{cfg.label_de}: {text}")
+    return "\n".join(lines)
+
+
 def merge_planning_prompt_variables(
     cur,
     base_variables: Mapping[str, str],
@@ -113,6 +134,7 @@ def planning_prompt_placeholder_catalog() -> dict:
 
 
 __all__ = [
+    "catalog_exercise_hints_block",
     "merge_planning_prompt_variables",
     "planning_prompt_placeholder_catalog",
 ]

backend/tests/test_planning_exercise_path_ai_fill.py

@@ -83,6 +83,20 @@ def test_strip_off_topic_steps_from_path():
     assert [s["exercise_id"] for s in out] == [1, 2, 4]
 
 
+def test_build_gap_fill_goal_text_includes_catalog_exercise_hints():
+    from planning_exercise_semantics import build_semantic_brief
+
+    brief = build_semantic_brief("Mae Geri")
+    text = build_gap_fill_goal_text(
+        goal_query="Mae Geri lernen",
+        brief=brief,
+        spec={"source": "roadmap_unfilled", "phase": "grundlage", "title_hint": "Stand"},
+        catalog_exercise_hints="Primärfokus: Kihon und Partnerübungen mit Technikbezug.",
+    )
+    assert "Katalog-Hinweise Übungsanlage" in text
+    assert "Kihon" in text
+
+
 def test_build_gap_fill_goal_text_includes_topic():
     brief = build_semantic_brief("Mae Geri Perfektion")
     text = build_gap_fill_goal_text(

backend/tests/test_planning_path_rematch.py

@@ -336,3 +336,29 @@ def test_filter_rematch_skips_preserved_slots():
         off_topic_steps=[],
     )
     assert filtered == {1}
+
+
+def test_apply_rematch_catalog_guard_skips_unfilled_only():
+    from planning_path_rematch import apply_rematch_catalog_guard
+
+    indices = {0, 1, 2}
+    reasons = {
+        0: "QS-Tier-1",
+        1: "Keine passende Übung für Roadmap-Stufe",
+        2: "Passt nicht zur Haupttechnik",
+    }
+    filtered = apply_rematch_catalog_guard(
+        indices,
+        reasons,
+        rematch_guard="Keine leeren Slots erzwingen.",
+    )
+    assert filtered == {0, 2}
+
+
+def test_apply_rematch_catalog_guard_inactive_without_guard():
+    from planning_path_rematch import apply_rematch_catalog_guard
+
+    indices = {1}
+    reasons = {1: "Keine passende Übung für Roadmap-Stufe"}
+    assert apply_rematch_catalog_guard(indices, reasons, rematch_guard=None) == {1}
+    assert apply_rematch_catalog_guard(indices, reasons, rematch_guard="") == {1}

backend/version.py

@@ -1,8 +1,8 @@
 # Shinkan Jinkendo Version Information
 
-APP_VERSION = "0.8.237"
+APP_VERSION = "0.8.238"
 BUILD_DATE = "2026-05-22"
-DB_SCHEMA_VERSION = "20260607094"
+DB_SCHEMA_VERSION = "20260607095"
 
 MODULE_VERSIONS = {
     "legal_documents": "1.4.0",  # Admin: Live-Vorschau pro Abschnitt + modale Vollvorschau (Editor + Dokumentenliste)
@@ -53,11 +53,20 @@ MODULE_VERSIONS = {
 }
 
 CHANGELOG = [
+    {
+        "version": "0.8.238",
+        "date": "2026-05-22",
+        "changes": [
+            "Planungs-KI H1.5: rematch_guard im Auto-Rematch (kein Rematch nur wegen leerer Roadmap-Stufe).",
+            "hints_on_exercise in Gap-Fill, Übungs-KI-Kontext und intent_context_json (Roadmap-Pipeline).",
+            "Migration 095: keine Migrations-Seeds in catalog_prompt_slots (Prod-sichere Katalogstruktur).",
+        ],
+    },
     {
         "version": "0.8.237",
         "date": "2026-05-22",
         "changes": [
-            "Migration 094: catalog_prompt_slots vollständig befüllt (Karate, SV, alle Trainingsstile/Zielgruppen).",
+            "Migration 094/095: Katalog-Prompt-Slots ohne Migrations-Seeds — Inhalte Admin oder Laufzeit-Fallback.",
             "catalog_slot_fallbacks: Namens-Fallback bis Admin-Override — gleiche Qualität wie H1-Registry.",
         ],
     },

docs/HANDOVER.md

@@ -1,7 +1,7 @@
 # Shinkan Jinkendo – Entwicklungsstand & Handover
 
-**Stand:** 2026-05-22 (F15 Graph-Match & getrennte Pfad-QS, lokal nach **0.8.233**)  
-**App-Version / DB-Schema:** App **`0.8.233`** (Planungs-KI F11–F14, Katalog-Kontext); **F15** siehe §2.8 — DB unverändert (`DB_SCHEMA_VERSION`, Migration **088**).
+**Stand:** 2026-05-22 (Katalog-Prompt-Slots H2/H1.5, **0.8.238**)  
+**App-Version / DB-Schema:** App **`0.8.238`**; DB Migration **095** (keine Slot-Seeds; Prod-sichere Katalogstruktur).
 
 Diese Datei ist die **Einstiegs-Doku für neue Chat-Sessions**: Anforderungen im Detail stehen in `.claude/docs/` (siehe unten); hier der **implementierte Stand**, **Medien-Meilenstein** und **sinnvolle nächste Schritte**.
 
@@ -115,7 +115,8 @@ Das Schema ist gegenüber dem Code zurück: Migration **`022_skills_schema_compl
 | **F13** | **`planning_catalog_context`** (Fokus/Stil/TT/ZG) im Match + Graph-Artefakt | ✅ **0.8.233** |
 | **F14** | **`ProgressionGraphEditor`** — Slot-UI + Planungskontext-Dropdowns | ✅ **0.8.233** |
 | **F15** | Unified Slot-Review (Match-Dialog), getrennte Pfad-QS, `findings_stale` | ✅ lokal (nach 0.8.233) |
-| **H1** | Katalog-Prompt-Snippets (modulare LLM-Anweisungen) | 🔲 Spec **`docs/architecture/PLANNING_CATALOG_PROMPT_SNIPPETS.md`** |
+| **H1–H2.1** | Katalog-Prompt-Slots (DB, Resolver, Admin-UI, granulare Prompts) | ✅ **0.8.234–238** — Spec **`docs/architecture/PLANNING_CATALOG_PROMPT_SNIPPETS.md`** |
+| **H1.5** | `rematch_guard`, `hints_on_exercise` in Rematch/Gap-Fill/Intent | ✅ **0.8.238** |
 
 **Architektur (verbindlich):** Drei Schichten — (1) **Katalog-Dimensionen** (DB, jetzt im Match verdrahtet; **H1:** zusätzlich Prompt-Snippets), (2) **Technik-Disambiguierung** (Code, nur bei `topic_type=technique`), (3) **Didaktik** (Roadmap + LLM-QS, nicht im Vokabular). Progressionsgraph = **Roadmap-first**, **keine Gruppenanalyse**. Bestehender Graph = **leichter Nachfolger-Bias** ab Schritt 2. Trainingsplanung = **eigene Pipeline** (Phase G) — Wiederverwendung der Bausteine, siehe Ist-Doku §16.
 
@@ -148,7 +149,7 @@ Das Schema ist gegenüber dem Code zurück: Migration **`022_skills_schema_compl
 5. Phase D′ — Auto KI-Gap-Fill bei persistent leeren Slots
 6. **Trainingsplanung Phase G** — Gruppenkontext-Pack, Scopes `training_section` / `framework_slot` (Ist-Doku §16)
 7. Technik-Katalog konfigurierbar (Backlog)
-8. **H1** — Katalog-Prompt-Snippets (modulare LLM-Anweisungen)
+8. **H1–H2.1** — Katalog-Prompt-Slots ✅ · **Prompt-Editor-Überarbeitung** → eigene Session
 
 #### Übungs-KI Formular / Schnellanlage (Stand **0.8.171**)
 

docs/architecture/PLANNING_CATALOG_PROMPT_SNIPPETS.md

@@ -1,7 +1,7 @@
 # Planungs-KI — Katalog-Prompt-Slots (Snippets)
 
 **Stand:** 2026-05-22  
-**Status:** **H2** umgesetzt (0.8.235) · **H2.1** Admin-UI + granulare Prompts (0.8.236)  
+**Status:** **H2.1** umgesetzt (0.8.236) · **H1.5** Rematch-Guard + Gap-Fill-Hints (0.8.238) · **Prompt-Editor-Überarbeitung** bewusst ausgelagert  
 **Bezüge:** `PLANNING_PROGRESSION_GRAPH_KI.md` §4.4 · `AI_PROMPT_TARGET_ARCHITECTURE.md` §2.4 · `planning_catalog_context.py` · `catalog_prompt_slots.py`
 
 ---
@@ -173,12 +173,12 @@ Hardcodierte `SNIPPET_REGISTRY` — Proof of Concept für `catalog_guidance_bloc
 
 - [x] Slot-Editor an Fokusbereich / Trainingsstil / Zielgruppe / Stilrichtung (`CatalogPromptSlotsEditor`, Stammdaten-Katalog)
 - [x] Prompt-Templates mit granularen Platzhaltern (Migration 093)
-- [ ] Platzhalter-Hilfe im KI-Prompt-Editor (erweitert)
+- [ ] **Prompt-Editor komplett überarbeiten** (Platzhalter-Hilfe, UX) — **eigene Session**
 
-### H1.5
+### H1.5 ✓ (0.8.238)
 
-- [ ] `rematch_guard` im Rematch-Loop
-- [ ] Intent-Prompts + Gap-Fill: `hints_on_exercise`
+- [x] `rematch_guard` im Rematch-Loop (`apply_rematch_catalog_guard`)
+- [x] Gap-Fill + Intent: `hints_on_exercise` via `catalog_exercise_hints_block` / `intent_context_json`
 
 ### H3 — Trainingsplanung (Phase G)
 
@@ -211,6 +211,7 @@ Hardcodierte `SNIPPET_REGISTRY` — Proof of Concept für `catalog_guidance_bloc
 
 | Datum | Änderung |
 |-------|----------|
+| 2026-05-22 | **H1.5** (0.8.238): rematch_guard; hints_on_exercise in Gap-Fill/Intent; Migration 095 ohne Seeds |
 | 2026-05-22 | **H2.1** (0.8.236): Admin-UI `CatalogPromptSlotsEditor`; Migration 093 granulare Prompt-Templates |
 | 2026-05-22 | **H2** (0.8.235): Slot-Typ-Register + `catalog_prompt_slots` DB, granulare Platzhalter, Admin-API |
 | 2026-05-22 | Konzept §4–§8: zwei Ebenen Slot-Typ vs. Slot-Wert; Platzhalter `{kind}_{slot_key}` |