Enhance Planning Exercise Profiles and Context Handling

- Introduced new functions to generate skill profiles from exercise IDs, improving the ability to summarize skills for both units and sections.
- Updated the planning target profile to incorporate section-specific exercise IDs, allowing for more granular skill tracking and context.
- Enhanced the ExercisePickerModal and related pages to support section context, including titles, guidance notes, and exercise counts.
- Implemented expectation mode handling in the planning target pipeline to differentiate between planning references and query-only scenarios.
- Incremented version to 0.8.174 and updated changelog to reflect these enhancements in planning AI capabilities.

backend/planning_exercise_profiles.py

@@ -293,11 +293,50 @@ def _profile_from_unit_occurrences(cur, unit_id: int) -> Dict[int, float]:
     return _skill_weights_from_profile(prof.get("skills") or [])
 
 
+def _profile_from_exercise_ids(cur, exercise_ids: Sequence[int]) -> Dict[int, float]:
+    ids = [int(x) for x in exercise_ids if int(x) > 0]
+    if not ids:
+        return {}
+    occ = [ExerciseOccurrence(exercise_id=eid) for eid in ids]
+    prof = profile_for_occurrences(cur, occ, reference_max_by_skill=None)
+    return _skill_weights_from_profile(prof.get("skills") or [])
+
+
+def skill_profile_summary_from_exercise_ids(
+    cur,
+    exercise_ids: Sequence[int],
+    *,
+    limit_skills: int = 8,
+) -> Dict[str, Any]:
+    """Kompaktes Fähigkeitenprofil für LLM-Kontext und UI."""
+    ids = [int(x) for x in exercise_ids if int(x) > 0]
+    if not ids:
+        return {"exercise_count": 0, "skills": []}
+    occ = [ExerciseOccurrence(exercise_id=eid) for eid in ids]
+    prof = profile_for_occurrences(cur, occ, reference_max_by_skill=None)
+    skills_out = prof.get("skills") or []
+    top = sorted(skills_out, key=lambda s: -float(s.get("weight") or s.get("score") or 0))[:limit_skills]
+    names = _load_skill_names(cur, [int(s["skill_id"]) for s in top if s.get("skill_id") is not None])
+    return {
+        "exercise_count": len(ids),
+        "skills": [
+            {
+                "skill_id": int(s["skill_id"]),
+                "name": names.get(int(s["skill_id"]), f"#{s['skill_id']}"),
+                "weight": round(float(s.get("weight") or s.get("score") or 0), 3),
+            }
+            for s in top
+            if s.get("skill_id") is not None
+        ],
+    }
+
+
 def build_planning_target_profile(
     cur,
     *,
     unit: Dict[str, Any],
     planned_exercise_ids: Sequence[int],
+    section_planned_exercise_ids: Optional[Sequence[int]] = None,
     anchor_exercise_id: Optional[int],
     intent: str,
 ) -> PlanningTargetProfile:
@@ -356,6 +395,13 @@ def build_planning_target_profile(
         if skill_plan:
             sources.append("current_unit_plan")
 
+    section_ids = [int(x) for x in (section_planned_exercise_ids or []) if int(x) > 0]
+    if section_ids:
+        section_skills = _profile_from_exercise_ids(cur, section_ids)
+        if section_skills:
+            skill_target = _merge_weight_maps(skill_target, section_skills, scale=1.0)
+            sources.append("current_section_plan")
+
     if anchor_exercise_id:
         anchor_profiles = load_exercise_match_profiles_bulk(cur, [int(anchor_exercise_id)])
         ap = anchor_profiles.get(int(anchor_exercise_id))

backend/planning_exercise_suggest.py

@@ -12,6 +12,7 @@ from fastapi import HTTPException
 from pydantic import BaseModel, Field
 
 from tenant_context import TenantContext, library_content_visibility_sql
+from planning_exercise_profiles import skill_profile_summary_from_exercise_ids
 from planning_exercise_retrieval import run_multistage_planning_retrieval
 from planning_exercise_llm_rank import try_llm_rerank_planning_hits
 from planning_exercise_target_pipeline import (
@@ -56,6 +57,9 @@ class PlanningExerciseSuggestRequest(BaseModel):
     query: Optional[str] = ""
     intent_hint: Optional[str] = None
     planned_exercise_ids: Optional[List[int]] = None
+    section_title: Optional[str] = None
+    section_guidance_notes: Optional[str] = None
+    section_planned_exercise_ids: Optional[List[int]] = None
     include_llm_intent: bool = True
     include_llm_rank: bool = False
     limit: int = Field(default=20, ge=1, le=50)
@@ -241,6 +245,131 @@ def _load_group_recent_exercise_ids(
     return out
 
 
+def _section_for_context(
+    sections: Sequence[Dict[str, Any]],
+    section_order_index: Optional[int],
+) -> Optional[Dict[str, Any]]:
+    if section_order_index is None:
+        return None
+    target = int(section_order_index)
+    for sec in sections:
+        if int(sec.get("order_index") or -1) == target:
+            return sec
+    if 0 <= target < len(sections):
+        return sections[target]
+    return None
+
+
+def _collect_exercise_ids_from_section(sec: Optional[Dict[str, Any]]) -> List[int]:
+    if not sec:
+        return []
+    out: List[int] = []
+    seen: Set[int] = set()
+    for it in sorted(sec.get("items") or [], key=lambda x: int(x.get("order_index") or 0)):
+        if str(it.get("item_type") or "").strip().lower() == "note":
+            continue
+        raw = it.get("exercise_id")
+        if raw is None:
+            continue
+        try:
+            eid = int(raw)
+        except (TypeError, ValueError):
+            continue
+        if eid < 1 or eid in seen:
+            continue
+        seen.add(eid)
+        out.append(eid)
+    return out
+
+
+def _resolve_last_exercise_in_section(sec: Optional[Dict[str, Any]]) -> Tuple[Optional[int], Optional[str]]:
+    if not sec:
+        return None, None
+    last_id: Optional[int] = None
+    last_title: Optional[str] = None
+    for it in sorted(sec.get("items") or [], key=lambda x: int(x.get("order_index") or 0)):
+        if str(it.get("item_type") or "").strip().lower() == "note":
+            continue
+        raw = it.get("exercise_id")
+        if raw is None:
+            continue
+        try:
+            eid = int(raw)
+        except (TypeError, ValueError):
+            continue
+        if eid < 1:
+            continue
+        last_id = eid
+        t = (it.get("exercise_title") or "").strip()
+        last_title = t or None
+    return last_id, last_title
+
+
+def _attach_planning_context_details(
+    cur,
+    pack: Dict[str, Any],
+    *,
+    sections: Optional[Sequence[Dict[str, Any]]] = None,
+    body: Optional[PlanningExerciseSuggestRequest] = None,
+) -> Dict[str, Any]:
+    """Abschnitt, Fähigkeitenprofile und letzte Übung anreichern."""
+    sec: Optional[Dict[str, Any]] = None
+    section_idx = pack.get("section_order_index")
+    if sections is not None and section_idx is not None:
+        sec = _section_for_context(sections, section_idx)
+
+    section_ids = _collect_exercise_ids_from_section(sec)
+    if body and body.section_planned_exercise_ids:
+        section_ids = []
+        seen: Set[int] = set()
+        for raw in body.section_planned_exercise_ids:
+            try:
+                eid = int(raw)
+            except (TypeError, ValueError):
+                continue
+            if eid < 1 or eid in seen:
+                continue
+            seen.add(eid)
+            section_ids.append(eid)
+    elif pack.get("section_planned_exercise_ids"):
+        section_ids = list(pack.get("section_planned_exercise_ids") or [])
+
+    section_title = pack.get("section_title")
+    if body and (body.section_title or "").strip():
+        section_title = (body.section_title or "").strip()
+    elif sec and (sec.get("title") or "").strip():
+        section_title = (sec.get("title") or "").strip()
+
+    guidance = None
+    if body and (body.section_guidance_notes or "").strip():
+        guidance = (body.section_guidance_notes or "").strip()
+    elif sec and (sec.get("guidance_notes") or "").strip():
+        guidance = (sec.get("guidance_notes") or "").strip()
+
+    last_in_section_id, last_in_section_title = _resolve_last_exercise_in_section(sec)
+    if body and not last_in_section_id and pack.get("anchor_exercise_id"):
+        last_in_section_id = pack.get("anchor_exercise_id")
+        last_in_section_title = pack.get("anchor_title")
+
+    unit_ids = list(pack.get("planned_exercise_ids") or [])
+    pack["section_title"] = section_title
+    pack["section_guidance_notes"] = guidance
+    pack["section_planned_exercise_ids"] = section_ids
+    pack["section_exercise_count"] = len(section_ids)
+    pack["last_section_exercise_id"] = last_in_section_id
+    pack["last_section_exercise_title"] = last_in_section_title
+    pack["unit_skill_profile_summary"] = skill_profile_summary_from_exercise_ids(cur, unit_ids)
+    pack["section_skill_profile_summary"] = skill_profile_summary_from_exercise_ids(cur, section_ids)
+    pack["has_planning_reference"] = bool(
+        unit_ids
+        or section_ids
+        or pack.get("anchor_exercise_id")
+        or (pack.get("unit") or {}).get("framework_slot_id")
+        or (pack.get("unit") or {}).get("origin_framework_slot_id")
+    )
+    return pack
+
+
 def _section_title_for_index(sections: Sequence[Dict[str, Any]], section_order_index: Optional[int]) -> Optional[str]:
     if section_order_index is None:
         return None
@@ -348,7 +477,7 @@ def build_planning_exercise_context_pack(
     titles = _load_exercise_titles(cur, [x for x in [anchor_id] if x])
     anchor_title = titles.get(anchor_id) if anchor_id else None
 
-    return {
+    pack = {
         "unit_id": int(body.unit_id),
         "unit": {
             "id": int(body.unit_id),
@@ -369,6 +498,7 @@ def build_planning_exercise_context_pack(
         "progression_edge_notes": progression_notes,
         "group_recent_exercise_ids": sorted(group_recent),
     }
+    return _attach_planning_context_details(cur, pack, sections=sections, body=body)
 
 
 def build_client_planning_context_pack(
@@ -413,7 +543,7 @@ def build_client_planning_context_pack(
     titles = _load_exercise_titles(cur, [x for x in [anchor_id] if x])
     anchor_title = titles.get(anchor_id) if anchor_id else None
 
-    return {
+    pack = {
         "unit_id": None,
         "unit": {
             "id": None,
@@ -424,7 +554,7 @@ def build_client_planning_context_pack(
         "group_id": group_id,
         "group_name": group_name,
         "section_order_index": body.section_order_index,
-        "section_title": None,
+        "section_title": (body.section_title or "").strip() or None,
         "planned_exercise_ids": planned_ids,
         "anchor_exercise_id": anchor_id,
         "anchor_title": anchor_title,
@@ -435,6 +565,7 @@ def build_client_planning_context_pack(
         "group_recent_exercise_ids": sorted(group_recent),
         "context_mode": "client_free",
     }
+    return _attach_planning_context_details(cur, pack, sections=None, body=body)
 
 
 def suggest_planning_exercises(
@@ -448,27 +579,40 @@ def suggest_planning_exercises(
     else:
         pack = build_client_planning_context_pack(cur, tenant=tenant, body=body)
     pack = _apply_client_planned_override(cur, pack, body)
+    pack = _attach_planning_context_details(cur, pack, body=body)
     query = _normalize_query(body.query)
     heuristic_intent = resolve_planning_exercise_intent(query, body.intent_hint)
 
+    has_plan_ref = bool(pack.get("has_planning_reference"))
+    expectation_mode = "planning_hybrid" if has_plan_ref else "query_only"
+
     pipeline_context = {
         "unit_title": pack.get("unit_title"),
         "group_name": pack.get("group_name"),
         "section_title": pack.get("section_title"),
+        "section_guidance_notes": pack.get("section_guidance_notes"),
+        "section_exercise_count": pack.get("section_exercise_count"),
         "planned_count": len(pack.get("planned_exercise_ids") or []),
         "anchor_title": pack.get("anchor_title"),
         "anchor_exercise_id": pack.get("anchor_exercise_id"),
+        "last_section_exercise_title": pack.get("last_section_exercise_title"),
         "progression_graph_id": pack.get("progression_graph_id"),
+        "unit_skill_profile": pack.get("unit_skill_profile_summary"),
+        "section_skill_profile": pack.get("section_skill_profile_summary"),
+        "has_planning_reference": has_plan_ref,
+        "expectation_mode": expectation_mode,
     }
     target_profile, intent, scenario_kind, query_intent_summary = build_planning_target_with_query_pipeline(
         cur,
         unit=pack["unit"],
         planned_exercise_ids=pack["planned_exercise_ids"],
+        section_planned_exercise_ids=pack.get("section_planned_exercise_ids") or [],
         anchor_exercise_id=pack.get("anchor_exercise_id"),
         query=query,
         heuristic_intent=heuristic_intent,
         include_llm_intent=body.include_llm_intent,
         context_summary=pipeline_context,
+        has_planning_reference=has_plan_ref,
     )
     weights = _intent_weights(intent)
     target_profile_summary = target_profile.to_summary_dict(cur)
@@ -549,11 +693,18 @@ def suggest_planning_exercises(
         "unit_title": pack.get("unit_title"),
         "group_name": pack.get("group_name"),
         "section_title": pack.get("section_title"),
+        "section_guidance_notes": pack.get("section_guidance_notes"),
+        "section_exercise_count": pack.get("section_exercise_count"),
         "planned_count": len(planned_set),
         "anchor_title": pack.get("anchor_title"),
         "anchor_exercise_id": pack.get("anchor_exercise_id"),
+        "last_section_exercise_title": pack.get("last_section_exercise_title"),
         "progression_graph_id": pack.get("progression_graph_id"),
         "context_mode": pack.get("context_mode") or ("unit" if pack.get("unit_id") else "client_free"),
+        "unit_skill_profile": pack.get("unit_skill_profile_summary"),
+        "section_skill_profile": pack.get("section_skill_profile_summary"),
+        "has_planning_reference": pack.get("has_planning_reference"),
+        "expectation_mode": expectation_mode,
     }
 
     return {
@@ -568,5 +719,6 @@ def suggest_planning_exercises(
         "intent_resolved": intent,
         "intent_heuristic": heuristic_intent,
         "query_normalized": query or None,
+        "expectation_mode": expectation_mode,
         "hits": hits,
     }

backend/planning_exercise_target_pipeline.py

@@ -224,14 +224,19 @@ def build_planning_target_with_query_pipeline(
     *,
     unit: Dict[str, Any],
     planned_exercise_ids: List[int],
+    section_planned_exercise_ids: Optional[List[int]] = None,
     anchor_exercise_id: Optional[int],
     query: Optional[str],
     heuristic_intent: str,
     include_llm_intent: bool,
     context_summary: Mapping[str, Any],
+    has_planning_reference: bool = True,
 ) -> Tuple[PlanningTargetProfile, str, str, Dict[str, Any]]:
     """
     Returns: target_profile, resolved_intent, scenario_kind, query_intent_summary dict
+
+    Ohne Planungsbezug (keine Übungen/Anker/Rahmen): Erwartungsprofil primär aus Suchtext (query_only).
+    Mit Planungsbezug: hybrid aus Plan + optional Query-Overlay.
     """
     scenario = classify_planning_scenario(query, heuristic_intent)
     resolved_intent = heuristic_intent
@@ -239,13 +244,18 @@ def build_planning_target_with_query_pipeline(
     parsed: Optional[PlanningQueryIntentParsed] = None
     resolved_skills: List[Dict[str, Any]] = []
 
-    base = build_planning_target_profile(
-        cur,
-        unit=unit,
-        planned_exercise_ids=planned_exercise_ids,
-        anchor_exercise_id=anchor_exercise_id,
-        intent=heuristic_intent,
-    )
+    if has_planning_reference:
+        base = build_planning_target_profile(
+            cur,
+            unit=unit,
+            planned_exercise_ids=planned_exercise_ids,
+            section_planned_exercise_ids=section_planned_exercise_ids or [],
+            anchor_exercise_id=anchor_exercise_id,
+            intent=heuristic_intent,
+        )
+    else:
+        base = PlanningTargetProfile(sources=["query_only"])
+
     base_summary = base.to_summary_dict(cur)
 
     if should_run_llm_intent_pipeline(query, scenario, include_llm_intent=include_llm_intent):
@@ -273,6 +283,11 @@ def build_planning_target_with_query_pipeline(
 
         focus, style, tt, tg, skills, resolved_skills = resolve_query_intent_catalog_ids(cur, parsed)
         if focus or style or tt or tg or skills:
+            overlay_scenario = scenario
+            overlay_emphasis = parsed.emphasis
+            if not has_planning_reference:
+                overlay_scenario = SCENARIO_FREE_SEARCH
+                overlay_emphasis = "replace"
             target = merge_query_overlay_into_target(
                 base,
                 focus=focus,
@@ -280,9 +295,12 @@ def build_planning_target_with_query_pipeline(
                 tt=tt,
                 tg=tg,
                 skills=skills,
-                emphasis=parsed.emphasis,
-                scenario=scenario,
+                emphasis=overlay_emphasis,
+                scenario=overlay_scenario,
             )
+    elif not has_planning_reference and _normalize_query(query):
+        # Kein LLM, aber Freitext: leichtes Profil bleibt leer — Retrieval nutzt Volltext
+        target = PlanningTargetProfile(sources=["query_only"])
 
     query_intent_summary: Dict[str, Any] = {
         "scenario": scenario,
@@ -293,6 +311,7 @@ def build_planning_target_with_query_pipeline(
         "rationale": (parsed.rationale if parsed else None),
         "skill_hints_resolved": resolved_skills,
         "requires_partner": parsed.requires_partner if parsed else None,
+        "expectation_mode": "planning_hybrid" if has_planning_reference else "query_only",
     }
 
     return target, resolved_intent, scenario, query_intent_summary

backend/tests/test_planning_exercise_suggest.py

@@ -78,6 +78,30 @@ def test_compose_retrieval_phase():
     )
 
 
+def test_query_only_expectation_without_planning_reference():
+    from planning_exercise_profiles import PlanningTargetProfile
+    from planning_exercise_target_pipeline import build_planning_target_with_query_pipeline
+
+    class _Cur:
+        pass
+
+    target, intent, scenario, summary = build_planning_target_with_query_pipeline(
+        _Cur(),
+        unit={"id": None, "framework_slot_id": None, "origin_framework_slot_id": None},
+        planned_exercise_ids=[],
+        section_planned_exercise_ids=[],
+        anchor_exercise_id=None,
+        query="Partnerübung Reaktion",
+        heuristic_intent="free_search",
+        include_llm_intent=False,
+        context_summary={"expectation_mode": "query_only"},
+        has_planning_reference=False,
+    )
+    assert intent == "free_search"
+    assert summary.get("expectation_mode") == "query_only"
+    assert target.sources == ["query_only"] or "query_only" in target.sources
+
+
 def test_parse_planning_query_intent_response():
     parsed = parse_planning_query_intent_response(
         '{"intent":"continue_plan_goal","scenario":"additive_constraint",'

backend/version.py

@@ -1,6 +1,6 @@
 # Shinkan Jinkendo Version Information
 
-APP_VERSION = "0.8.173"
+APP_VERSION = "0.8.174"
 BUILD_DATE = "2026-05-22"
 DB_SCHEMA_VERSION = "20260531073"
 
@@ -28,7 +28,7 @@ MODULE_VERSIONS = {
     "skill_profiles": "1.0.0",  # Phase 3: gewichtetes Fähigkeiten-Profil + skill-discovery/suggestions
     "methods": "0.1.0",
     "exercises": "2.37.0",  # Planungs-KI P1: Szenario-Pipeline + Query-Intent-Overlay
-    "planning_exercise_suggest": "0.5.0",  # Mehrstufiges Profil-Retrieval; LLM-Gates (max 1 Call)
+    "planning_exercise_suggest": "0.6.0",  # Abschnitts-/Skill-Kontext; expectation_mode hybrid|query_only
     "training_units": "0.4.0",  # POST .../publish-to-framework: Ablauf aus geplanter Einheit → Rahmen-Slot-Blueprint
     "training_programs": "0.1.0",
     "planning": "0.15.0",  # Vorlagen: Strukturvorschau, Bearbeiten inkl. Split-Sessions + Beschreibung
@@ -43,6 +43,14 @@ MODULE_VERSIONS = {
 }
 
 CHANGELOG = [
+    {
+        "version": "0.8.174",
+        "date": "2026-05-22",
+        "changes": [
+            "Planungs-KI: Abschnitts-Kontext (guidance_notes, Übungszahl, letzte Übung), Fähigkeitenprofil Einheit/Abschnitt an LLM.",
+            "Erwartungsprofil hybrid (Planungsbezug) vs. query_only (nur Suchtext); current_section_plan im Target-Profil.",
+        ],
+    },
     {
         "version": "0.8.173",
         "date": "2026-05-22",

frontend/src/components/ExercisePickerModal.jsx

@@ -104,6 +104,13 @@ export default function ExercisePickerModal({
     const base = {
       groupId: Number.isFinite(groupId) && groupId > 0 ? groupId : null,
       sectionOrderIndex: planningContext?.sectionOrderIndex ?? 0,
+      sectionTitle: planningContext?.sectionTitle ?? null,
+      sectionGuidanceNotes: planningContext?.sectionGuidanceNotes ?? null,
+      sectionPlannedExerciseIds: Array.isArray(planningContext?.sectionPlannedExerciseIds)
+        ? planningContext.sectionPlannedExerciseIds
+        : [],
+      sectionExerciseCount: planningContext?.sectionExerciseCount ?? null,
+      lastExerciseTitle: planningContext?.lastExerciseTitle ?? null,
       phaseOrderIndex: planningContext?.phaseOrderIndex ?? null,
       parallelStreamOrderIndex: planningContext?.parallelStreamOrderIndex ?? null,
       anchorExerciseId: planningContext?.anchorExerciseId ?? null,
@@ -424,10 +431,24 @@ export default function ExercisePickerModal({
       if (resolvedPlanningUnitId) {
         requestBody.unit_id = Number(resolvedPlanningUnitId)
       }
-      if (activePlanningContext.groupId) {
-        requestBody.group_id = Number(activePlanningContext.groupId)
-      }
-      const res = await api.suggestPlanningExercises(requestBody)
+        if (activePlanningContext.groupId) {
+          requestBody.group_id = Number(activePlanningContext.groupId)
+        }
+        if (activePlanningContext.sectionTitle) {
+          requestBody.section_title = String(activePlanningContext.sectionTitle)
+        }
+        if (activePlanningContext.sectionGuidanceNotes) {
+          requestBody.section_guidance_notes = String(activePlanningContext.sectionGuidanceNotes)
+        }
+        if (
+          Array.isArray(activePlanningContext.sectionPlannedExerciseIds) &&
+          activePlanningContext.sectionPlannedExerciseIds.length > 0
+        ) {
+          requestBody.section_planned_exercise_ids = activePlanningContext.sectionPlannedExerciseIds
+            .map((x) => Number(x))
+            .filter((x) => Number.isFinite(x) && x > 0)
+        }
+        const res = await api.suggestPlanningExercises(requestBody)
       setPlanningContextSummary(res?.context_summary || null)
       setPlanningTargetProfileSummary(res?.target_profile_summary || null)
       setPlanningLlmRankApplied(Boolean(res?.llm_rank_applied))
@@ -662,6 +683,16 @@ export default function ExercisePickerModal({
                 {planningContextSummary.section_title ? (
                   <span className="exercise-tag">{planningContextSummary.section_title}</span>
                 ) : null}
+                {planningContextSummary.section_exercise_count != null ? (
+                  <span className="exercise-tag">
+                    {planningContextSummary.section_exercise_count} Übungen im Abschnitt
+                  </span>
+                ) : null}
+                {planningContextSummary.last_section_exercise_title ? (
+                  <span className="exercise-tag">
+                    Letzte: {planningContextSummary.last_section_exercise_title}
+                  </span>
+                ) : null}
                 {planningContextSummary.planned_count != null ? (
                   <span className="exercise-tag">{planningContextSummary.planned_count} Übungen im Plan</span>
                 ) : null}
@@ -687,6 +718,19 @@ export default function ExercisePickerModal({
                     ))
                   : null}
               </div>
+              {planningContextSummary.section_guidance_notes ? (
+                <p style={{ margin: '6px 0 0', fontSize: '11px', color: 'var(--text2)' }}>
+                  Abschnitt: {planningContextSummary.section_guidance_notes}
+                </p>
+              ) : null}
+              {planningContextSummary.expectation_mode ? (
+                <p style={{ margin: '4px 0 0', fontSize: '11px', color: 'var(--text3)' }}>
+                  Erwartungsprofil:{' '}
+                  {planningContextSummary.expectation_mode === 'query_only'
+                    ? 'nur Suchtext'
+                    : 'Planung + optional Suchtext'}
+                </p>
+              ) : null}
               {planningTargetProfileSummary?.has_skill_gap ? (
                 <p style={{ margin: '6px 0 0', fontSize: '11px', color: 'var(--text3)' }}>
                   Skill-Lücke zum bisherigen Plan berücksichtigt

frontend/src/pages/TrainingFrameworkProgramEditPage.jsx

@@ -286,10 +286,35 @@ export default function TrainingFrameworkProgramEditPage() {
         plannedExerciseIds.push(eid)
       }
     }
+    const sectionPlannedExerciseIds = []
+    const seenSec = new Set()
+    for (const it of sec?.items || []) {
+      if (String(it?.item_type || '').toLowerCase() === 'note') continue
+      const eid = Number(it?.exercise_id)
+      if (!Number.isFinite(eid) || eid < 1 || seenSec.has(eid)) continue
+      seenSec.add(eid)
+      sectionPlannedExerciseIds.push(eid)
+    }
+    let lastExerciseTitle = null
+    if (sec?.items?.length) {
+      for (let i = sec.items.length - 1; i >= 0; i -= 1) {
+        const it = sec.items[i]
+        if (String(it?.item_type || '').toLowerCase() === 'note') continue
+        if (it?.exercise_id) {
+          lastExerciseTitle = (it.exercise_title || '').trim() || null
+          break
+        }
+      }
+    }
     return {
       unitId: null,
       groupId: null,
       sectionOrderIndex: sIdx,
+      sectionTitle: (sec?.title || '').trim() || null,
+      sectionGuidanceNotes: (sec?.guidance_notes || '').trim() || null,
+      sectionPlannedExerciseIds,
+      sectionExerciseCount: sectionPlannedExerciseIds.length,
+      lastExerciseTitle,
       anchorExerciseId: Number.isFinite(anchorExerciseId) && anchorExerciseId > 0 ? anchorExerciseId : null,
       progressionGraphId: null,
       plannedExerciseIds,

frontend/src/pages/TrainingUnitEditPage.jsx

@@ -173,11 +173,36 @@ export default function TrainingUnitEditPage() {
         plannedExerciseIds.push(eid)
       }
     }
+    const sectionPlannedExerciseIds = []
+    const seenSec = new Set()
+    for (const it of sec?.items || []) {
+      if (String(it?.item_type || '').toLowerCase() === 'note') continue
+      const eid = Number(it?.exercise_id)
+      if (!Number.isFinite(eid) || eid < 1 || seenSec.has(eid)) continue
+      seenSec.add(eid)
+      sectionPlannedExerciseIds.push(eid)
+    }
+    let lastExerciseTitle = null
+    if (sec?.items?.length) {
+      for (let i = sec.items.length - 1; i >= 0; i -= 1) {
+        const it = sec.items[i]
+        if (String(it?.item_type || '').toLowerCase() === 'note') continue
+        if (it?.exercise_id) {
+          lastExerciseTitle = (it.exercise_title || '').trim() || null
+          break
+        }
+      }
+    }
     const groupIdRaw = Number(formData.group_id)
     return {
       unitId: resolvedUnitId ? Number(resolvedUnitId) : null,
       groupId: Number.isFinite(groupIdRaw) && groupIdRaw > 0 ? groupIdRaw : null,
       sectionOrderIndex: sIdx,
+      sectionTitle: (sec?.title || '').trim() || null,
+      sectionGuidanceNotes: (sec?.guidance_notes || '').trim() || null,
+      sectionPlannedExerciseIds,
+      sectionExerciseCount: sectionPlannedExerciseIds.length,
+      lastExerciseTitle,
       anchorExerciseId: Number.isFinite(anchorExerciseId) && anchorExerciseId > 0 ? anchorExerciseId : null,
       progressionGraphId: null,
       plannedExerciseIds,