diff --git a/.claude/docs/working/PLANNING_EXERCISE_SUGGEST_CONTEXT.md b/.claude/docs/working/PLANNING_EXERCISE_SUGGEST_CONTEXT.md
index f0309d2..5d337d1 100644
--- a/.claude/docs/working/PLANNING_EXERCISE_SUGGEST_CONTEXT.md
+++ b/.claude/docs/working/PLANNING_EXERCISE_SUGGEST_CONTEXT.md
@@ -2,7 +2,7 @@
 
 **Version:** 0.1  
 **Datum:** 2026-05-22  
-**Status:** P0 in Umsetzung (Hybrid-Retrieval ohne LLM-Intent)  
+**Status:** P0.1 — Hybrid-Retrieval + Phase-1-Profil-Score (`profile_v1`); LLM-Rerank P2  
 **Bezüge:** `AI_PLANNING_KI_MULTISTAGE_FORECAST.md` · `AI_PROMPT_TARGET_ARCHITECTURE.md` · `SKILL_SCORING_SPEC.md` · `TRAINING_FRAMEWORK_SPEC.md` §3 (Progressionsgraph)
 
 ---
@@ -26,7 +26,8 @@ Trainer in der **Trainingsplanung** sollen Übungen finden oder anlegen können
 |-------|------|---------|-----|
 | **S0** | Kontext-Pack | SQL/API, deterministisch | ✅ |
 | **S1a** | Intent strukturieren | Optional LLM `planning_exercise_search_intent` | Heuristik |
-| **S1b** | Hybrid-Retrieval | Score: Volltext + Graph + Skills + Plan | ✅ |
+| **S1b** | Hybrid-Retrieval | Score: Volltext + Graph + Skills + Plan + **Profil** | ✅ |
+| **S1b+** | Profil-Vorselektion | `ExerciseMatchProfile` × `PlanningTargetProfile` | ✅ `profile_v1` |
 | **S1c** | Rerank + Begründung | Optional LLM `planning_exercise_search_rank` | Regelbasierte `reasons[]` |
 | **S2** | Neu-Anlage | Bestehende `suggestExerciseAi` + Pack als Zusatzkontext | Später |
 
@@ -81,11 +82,14 @@ score = w_ft * fulltext_rank
       + w_prog * progression_hit
       + w_skill * skill_jaccard(anchor, candidate)
       + w_plan * plan_affinity
+      + w_profile * profile_match(exercise, target)
       + w_repeat * (candidate in unit_plan ? -1 : 0)
       + w_group_repeat * (candidate in group_recent ? -0.5 : 0)
 ```
 
-**`reasons[]`** (regelbasiert, Deutsch): z. B. „Nachfolger im Progressionsgraph“, „Fähigkeiten passen zur Anker-Übung“, „Volltext-Treffer“.
+**`profile_match`** (0–1): siehe §12–§13 — Katalog-Dimensionen + Skill-Gewichte + Skill-Gap.
+
+**`reasons[]`** (regelbasiert, Deutsch): z. B. „Nachfolger im Progressionsgraph“, „Fähigkeiten passen zur Anker-Übung“, „Fokusbereich passend zum Planungsziel“, „Deckt Skill-Lücke im bisherigen Plan“, „Volltext-Treffer“.
 
 ---
 
@@ -121,6 +125,13 @@ score = w_ft * fulltext_rank
     "planned_count": 4,
     "anchor_title": "Partner-Fangspiel"
   },
+  "target_profile_summary": {
+    "sources": ["framework_catalog", "current_unit_plan", "anchor_exercise"],
+    "focus_areas": ["Reaktion & Abwehr"],
+    "top_skills": [{ "skill_id": 12, "name": "Reaktionsgeschwindigkeit", "weight": 1.0 }],
+    "has_skill_gap": true
+  },
+  "retrieval_phase": "profile_v1",
   "intent_resolved": "suggest_next",
   "hits": [
     {
@@ -128,14 +139,14 @@ score = w_ft * fulltext_rank
       "title": "…",
       "summary": "…",
       "score": 0.78,
-      "reasons": ["Nachfolger im Progressionsgraph", "3 gemeinsame Fähigkeiten mit Anker-Übung"],
+      "reasons": ["Nachfolger im Progressionsgraph", "Fokusbereich passend zum Planungsziel"],
       "focus_area": "…"
     }
   ]
 }
 ```
 
-**Modul:** `backend/planning_exercise_suggest.py` · Router `backend/routers/planning_exercise_suggest.py`
+**Modul:** `backend/planning_exercise_suggest.py` · `backend/planning_exercise_profiles.py` · Router `backend/routers/planning_exercise_suggest.py`
 
 ---
 
@@ -166,6 +177,7 @@ Wenn `hits` leer oder Trainer wählt „Mit KI anlegen“:
 | Phase | Inhalt |
 |-------|--------|
 | **P0** ✅ | Context-Pack, Hybrid-Score, API, Picker in Planung |
+| **P0.1** ✅ | `ExerciseMatchProfile` / `PlanningTargetProfile`, `profile_v1`, `target_profile_summary` |
 | **P1** | LLM Intent-JSON; Neu-Anlage mit Pack |
 | **P2** | LLM-Rerank + Kurzbegründung |
 | **P3** | Skill-Discovery / Framework-Ziele im Pack |
@@ -176,6 +188,7 @@ Wenn `hits` leer oder Trainer wählt „Mit KI anlegen“:
 
 - **2026-05-22:** Erstfassung; P0 API + Planungs-Picker.
 - **2026-05-22:** P0 implementiert (`planning_exercise_suggest.py`, Router, Picker); unsaved Formular-Plan noch nicht an API (nur persistierte Einheit).
+- **2026-05-22:** P0.1 — `planning_exercise_profiles.py`, Profil-Score in Hybrid-Retrieval, `retrieval_phase: profile_v1`, `target_profile_summary`.
 
 ---
 
@@ -184,3 +197,84 @@ Wenn `hits` leer oder Trainer wählt „Mit KI anlegen“:
 - **Ungespeicherte Plan-Änderungen:** API liest DB-Stand der Einheit — offene Formular-Items folgen in P0.1 (Client übergibt `planned_exercise_ids[]`).
 - **Progressionsgraph-ID:** noch nicht aus UI wählbar (`progression_graph_id` nur per API).
 - **LLM-Intent / Rerank:** P1/P2 laut Roadmap §9.
+
+---
+
+## 12. ExerciseMatchProfile & PlanningTargetProfile (Phase 1)
+
+Ziel: deterministische Vorselektion über **Profil-Dimensionen** statt nur Titel/Jaccard.
+
+### 12.1 ExerciseMatchProfile (pro Übung)
+
+| Feld | Quelle |
+|------|--------|
+| `focus_area_ids` | `exercise_focus_areas` (Primary = 1.0, sonst 0.85) |
+| `style_direction_ids` | `exercise_style_directions` |
+| `training_type_ids` | `exercise_training_types` |
+| `target_group_ids` | `exercise_target_groups` |
+| `skill_weights` | `exercise_skills` × Intensitäts-Multiplikator (`skill_scoring._skill_link_multiplier`) |
+
+Bulk-Lader: `load_exercise_match_profiles_bulk(cur, exercise_ids)`.
+
+### 12.2 PlanningTargetProfile (Planungsziel)
+
+Zusammensetzung aus mehreren Quellen (`sources[]`):
+
+| Quelle | Inhalt |
+|--------|--------|
+| `framework_catalog` | Fokus/Stil/Trainingsstil/Zielgruppe aus `training_framework_program_*` |
+| `framework_slot_skill_profile` | Skill-Profil des Slot-Blueprints (`profile_for_occurrences`) |
+| `framework_overall_skill_profile` | Fallback: alle Blueprint-Einheiten des Rahmens |
+| `current_unit_plan` | Skill-Profil der bereits eingeplanten Übungen dieser Einheit |
+| `anchor_exercise` | Katalog + Skills der Anker-Übung (Intent-abhängig) |
+| `skill_gap_vs_plan` | `target_skills − plan_skills` (normalisiert, Schwelle > 0.08) |
+
+Builder: `build_planning_target_profile(cur, unit=…, planned_exercise_ids=…, anchor_exercise_id=…, intent=…)`.
+
+Rahmen-Anbindung über `unit.framework_slot_id` oder `origin_framework_slot_id`.
+
+---
+
+## 13. Profil-Score (Formeln)
+
+**Gewichtete Überlappung** (Katalog + Skills):
+
+```
+overlap(a, b) = Σ min(a[k], b[k]) / Σ max(a[k], b[k])
+```
+
+**Skill-Gap-Abdeckung:**
+
+```
+gap_coverage(gap, candidate) = Σ min(gap[k], candidate[k]) / Σ gap[k]
+```
+
+**Profil-Score** (intent-gewichtet, Summe Dimensionen = 1.0):
+
+```
+profile_score = w_focus * overlap(focus)
+              + w_style * overlap(style)
+              + w_tt    * overlap(training_type)
+              + w_tg    * overlap(target_group)
+              + w_skill * overlap(skill_weights)
+              + w_gap   * gap_coverage(skill_gap)
+```
+
+Intent-Gewichte (Auszug): `deepen_exercise` → Skill hoch; `continue_plan_goal` → Gap hoch; `free_search` → Gap + Skill moderat.
+
+Scorer: `score_exercise_against_target(exercise_profile, target_profile, intent=…) → (score, reasons[])`.
+
+---
+
+## 14. Hybrid + Profil (P0.1)
+
+Im Hybrid-Score kommt **`w_profile * profile_score`** hinzu (Intent-abhängig ~0.15–0.35). Jaccard auf Anker-Skills bleibt parallel (schneller Anker-Fokus).
+
+**Response-Felder:**
+
+| Feld | Bedeutung |
+|------|-----------|
+| `retrieval_phase` | `"profile_v1"` — Phase-1 aktiv, kein LLM-Rerank |
+| `target_profile_summary` | Lesbare Kurzinfo für UI-Chips (Fokus, Top-Skills, Quellen) |
+
+**Phase 2 (P2):** Top 20–40 Kandidaten nach Hybrid+Profil → LLM `planning_exercise_search_rank` mit **Titel + summary + goal**; nur IDs aus Kandidatenliste.
diff --git a/backend/planning_exercise_profiles.py b/backend/planning_exercise_profiles.py
new file mode 100644
index 0000000..9f503ed
--- /dev/null
+++ b/backend/planning_exercise_profiles.py
@@ -0,0 +1,448 @@
+"""
+ExerciseMatchProfile / PlanningTargetProfile — Phase-1-Vorselektion Planungs-Übungssuche.
+
+Siehe .claude/docs/working/PLANNING_EXERCISE_SUGGEST_CONTEXT.md §12–§14
+"""
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+from typing import Any, Dict, List, Optional, Sequence, Set, Tuple
+
+from skill_scoring import (
+    ExerciseOccurrence,
+    collect_unit_exercise_occurrences,
+    fetch_exercise_skills_bulk,
+    profile_for_occurrences,
+    _skill_link_multiplier,
+    DEFAULT_ITEM_MINUTES,
+)
+
+
+def _ids_to_weights(ids: Sequence[int], primary_id: Optional[int] = None) -> Dict[int, float]:
+    out: Dict[int, float] = {}
+    for raw in ids or []:
+        try:
+            fid = int(raw)
+        except (TypeError, ValueError):
+            continue
+        if fid < 1:
+            continue
+        w = 1.0 if primary_id is not None and fid == int(primary_id) else 0.85
+        out[fid] = max(out.get(fid, 0.0), w)
+    return out
+
+
+def _merge_weight_maps(*maps: Optional[Dict[int, float]], scale: float = 1.0) -> Dict[int, float]:
+    out: Dict[int, float] = {}
+    for m in maps:
+        if not m:
+            continue
+        for k, v in m.items():
+            try:
+                kid = int(k)
+                val = float(v) * scale
+            except (TypeError, ValueError):
+                continue
+            if kid < 1 or val <= 0:
+                continue
+            out[kid] = max(out.get(kid, 0.0), val)
+    return out
+
+
+def _normalize_weight_map(m: Dict[int, float]) -> Dict[int, float]:
+    if not m:
+        return {}
+    mx = max(m.values())
+    if mx <= 0:
+        return {}
+    return {k: v / mx for k, v in m.items() if v > 0}
+
+
+def weighted_overlap(a: Dict[int, float], b: Dict[int, float]) -> float:
+    """Gewichtete Überlappung 0..1 (min-Summe / max-Summe)."""
+    if not a or not b:
+        return 0.0
+    keys = set(a) | set(b)
+    num = sum(min(a.get(k, 0.0), b.get(k, 0.0)) for k in keys)
+    den = sum(max(a.get(k, 0.0), b.get(k, 0.0)) for k in keys)
+    return num / den if den > 0 else 0.0
+
+
+def gap_coverage(gap: Dict[int, float], candidate: Dict[int, float]) -> float:
+    """Anteil der Skill-Lücke, den der Kandidat abdeckt (0..1)."""
+    if not gap:
+        return 0.0
+    total_gap = sum(gap.values())
+    if total_gap <= 0:
+        return 0.0
+    covered = sum(min(gap.get(k, 0.0), candidate.get(k, 0.0)) for k in gap)
+    return covered / total_gap
+
+
+@dataclass
+class ExerciseMatchProfile:
+    exercise_id: int
+    focus_area_ids: Dict[int, float] = field(default_factory=dict)
+    style_direction_ids: Dict[int, float] = field(default_factory=dict)
+    training_type_ids: Dict[int, float] = field(default_factory=dict)
+    target_group_ids: Dict[int, float] = field(default_factory=dict)
+    skill_weights: Dict[int, float] = field(default_factory=dict)
+
+    def to_dict(self) -> Dict[str, Any]:
+        return {
+            "exercise_id": self.exercise_id,
+            "focus_area_ids": self.focus_area_ids,
+            "style_direction_ids": self.style_direction_ids,
+            "training_type_ids": self.training_type_ids,
+            "target_group_ids": self.target_group_ids,
+            "skill_weights": self.skill_weights,
+        }
+
+
+@dataclass
+class PlanningTargetProfile:
+    focus_area_ids: Dict[int, float] = field(default_factory=dict)
+    style_direction_ids: Dict[int, float] = field(default_factory=dict)
+    training_type_ids: Dict[int, float] = field(default_factory=dict)
+    target_group_ids: Dict[int, float] = field(default_factory=dict)
+    skill_weights: Dict[int, float] = field(default_factory=dict)
+    skill_gap_weights: Dict[int, float] = field(default_factory=dict)
+    sources: List[str] = field(default_factory=list)
+
+    def to_summary_dict(self, cur, limit_skills: int = 5) -> Dict[str, Any]:
+        focus_labels = _load_focus_labels(cur, list(self.focus_area_ids.keys())[:6])
+        top_skills = sorted(self.skill_weights.items(), key=lambda x: -x[1])[:limit_skills]
+        skill_names = _load_skill_names(cur, [s[0] for s in top_skills])
+        return {
+            "sources": list(self.sources),
+            "focus_areas": focus_labels,
+            "top_skills": [
+                {"skill_id": sid, "name": skill_names.get(sid, f"#{sid}"), "weight": round(w, 2)}
+                for sid, w in top_skills
+            ],
+            "has_skill_gap": bool(self.skill_gap_weights),
+        }
+
+
+def _load_focus_labels(cur, ids: Sequence[int]) -> List[str]:
+    if not ids:
+        return []
+    ph = ",".join(["%s"] * len(ids))
+    cur.execute(
+        f"SELECT id, name FROM focus_areas WHERE id IN ({ph}) ORDER BY name",
+        list(ids),
+    )
+    return [f"{r['name'] or r['id']}" for r in cur.fetchall()]
+
+
+def _load_skill_names(cur, ids: Sequence[int]) -> Dict[int, str]:
+    if not ids:
+        return {}
+    ph = ",".join(["%s"] * len(ids))
+    cur.execute(f"SELECT id, name FROM skills WHERE id IN ({ph})", list(ids))
+    return {int(r["id"]): str(r["name"] or "") for r in cur.fetchall()}
+
+
+def _skill_weights_from_profile(skills_out: Sequence[Dict[str, Any]]) -> Dict[int, float]:
+    out: Dict[int, float] = {}
+    for row in skills_out or []:
+        sid = row.get("skill_id")
+        if sid is None:
+            continue
+        w = float(row.get("weight") or row.get("score") or 0)
+        if w > 0:
+            out[int(sid)] = w
+    return out
+
+
+def _single_exercise_skill_weights(
+    skill_rows: Sequence[Dict[str, Any]],
+    *,
+    minutes: float = DEFAULT_ITEM_MINUTES,
+) -> Dict[int, float]:
+    out: Dict[int, float] = {}
+    for link in skill_rows or []:
+        sid = link.get("skill_id")
+        if sid is None:
+            continue
+        sid = int(sid)
+        mult = _skill_link_multiplier(
+            intensity=link.get("intensity"),
+            required_level=link.get("required_level"),
+            target_level=link.get("target_level"),
+        )
+        w = minutes * mult
+        if w > 0:
+            out[sid] = out.get(sid, 0.0) + w
+    return out
+
+
+def _load_relation_maps_bulk(
+    cur,
+    exercise_ids: Sequence[int],
+    table: str,
+    id_column: str,
+) -> Dict[int, Dict[int, float]]:
+    ids = [int(x) for x in exercise_ids if int(x) > 0]
+    if not ids:
+        return {}
+    ph = ",".join(["%s"] * len(ids))
+    cur.execute(
+        f"""
+        SELECT exercise_id, {id_column} AS rel_id, is_primary
+        FROM {table}
+        WHERE exercise_id IN ({ph})
+        """,
+        ids,
+    )
+    out: Dict[int, Dict[int, float]] = {eid: {} for eid in ids}
+    for row in cur.fetchall():
+        eid = int(row["exercise_id"])
+        rid = int(row["rel_id"])
+        w = 1.0 if row.get("is_primary") else 0.85
+        out.setdefault(eid, {})[rid] = max(out[eid].get(rid, 0.0), w)
+    return out
+
+
+def load_exercise_match_profiles_bulk(cur, exercise_ids: Sequence[int]) -> Dict[int, ExerciseMatchProfile]:
+    ids = sorted({int(x) for x in exercise_ids if int(x) > 0})
+    if not ids:
+        return {}
+
+    focus_map = _load_relation_maps_bulk(cur, ids, "exercise_focus_areas", "focus_area_id")
+    style_map = _load_relation_maps_bulk(cur, ids, "exercise_style_directions", "style_direction_id")
+    type_map = _load_relation_maps_bulk(cur, ids, "exercise_training_types", "training_type_id")
+    tg_map = _load_relation_maps_bulk(cur, ids, "exercise_target_groups", "target_group_id")
+    skills_bulk = fetch_exercise_skills_bulk(cur, ids)
+
+    profiles: Dict[int, ExerciseMatchProfile] = {}
+    for eid in ids:
+        profiles[eid] = ExerciseMatchProfile(
+            exercise_id=eid,
+            focus_area_ids=focus_map.get(eid, {}),
+            style_direction_ids=style_map.get(eid, {}),
+            training_type_ids=type_map.get(eid, {}),
+            target_group_ids=tg_map.get(eid, {}),
+            skill_weights=_single_exercise_skill_weights(skills_bulk.get(eid, [])),
+        )
+    return profiles
+
+
+def _resolve_framework_for_unit(cur, unit: Dict[str, Any]) -> Optional[Dict[str, Any]]:
+    slot_id = unit.get("framework_slot_id") or unit.get("origin_framework_slot_id")
+    if not slot_id:
+        return None
+    cur.execute(
+        """
+        SELECT s.id AS slot_id, s.framework_program_id, s.sort_order, s.title AS slot_title,
+               fp.title AS framework_title, fp.focus_area_id AS header_focus_area_id
+        FROM training_framework_slots s
+        JOIN training_framework_programs fp ON fp.id = s.framework_program_id
+        WHERE s.id = %s
+        """,
+        (int(slot_id),),
+    )
+    row = cur.fetchone()
+    return dict(row) if row else None
+
+
+def _framework_catalog_weights(cur, framework_id: int) -> Tuple[Dict[int, float], Dict[int, float], Dict[int, float], Dict[int, float]]:
+    cur.execute(
+        "SELECT focus_area_id FROM training_framework_programs WHERE id = %s",
+        (framework_id,),
+    )
+    hdr = cur.fetchone()
+    header_fa = int(hdr["focus_area_id"]) if hdr and hdr.get("focus_area_id") else None
+
+    cur.execute(
+        "SELECT focus_area_id FROM training_framework_program_focus_areas WHERE framework_program_id = %s",
+        (framework_id,),
+    )
+    fa_ids = [int(r["focus_area_id"]) for r in cur.fetchall()]
+    if header_fa and header_fa not in fa_ids:
+        fa_ids.insert(0, header_fa)
+    focus = _ids_to_weights(fa_ids, primary_id=header_fa)
+
+    cur.execute(
+        "SELECT style_direction_id FROM training_framework_program_style_directions WHERE framework_program_id = %s",
+        (framework_id,),
+    )
+    style = _ids_to_weights([int(r["style_direction_id"]) for r in cur.fetchall()])
+
+    cur.execute(
+        "SELECT training_type_id FROM training_framework_program_training_types WHERE framework_program_id = %s",
+        (framework_id,),
+    )
+    tt = _ids_to_weights([int(r["training_type_id"]) for r in cur.fetchall()])
+
+    cur.execute(
+        "SELECT target_group_id FROM training_framework_program_target_groups WHERE framework_program_id = %s",
+        (framework_id,),
+    )
+    tg = _ids_to_weights([int(r["target_group_id"]) for r in cur.fetchall()])
+
+    return focus, style, tt, tg
+
+
+def _profile_from_unit_occurrences(cur, unit_id: int) -> Dict[int, float]:
+    occ = collect_unit_exercise_occurrences(cur, int(unit_id))
+    if not occ:
+        return {}
+    prof = profile_for_occurrences(cur, occ, reference_max_by_skill=None)
+    return _skill_weights_from_profile(prof.get("skills") or [])
+
+
+def build_planning_target_profile(
+    cur,
+    *,
+    unit: Dict[str, Any],
+    planned_exercise_ids: Sequence[int],
+    anchor_exercise_id: Optional[int],
+    intent: str,
+) -> PlanningTargetProfile:
+    sources: List[str] = []
+    focus: Dict[int, float] = {}
+    style: Dict[int, float] = {}
+    tt: Dict[int, float] = {}
+    tg: Dict[int, float] = {}
+    skill_target: Dict[int, float] = {}
+    skill_plan: Dict[int, float] = {}
+
+    fw = _resolve_framework_for_unit(cur, unit)
+    if fw:
+        fid = int(fw["framework_program_id"])
+        f_focus, f_style, f_tt, f_tg = _framework_catalog_weights(cur, fid)
+        focus = _merge_weight_maps(focus, f_focus)
+        style = _merge_weight_maps(style, f_style)
+        tt = _merge_weight_maps(tt, f_tt)
+        tg = _merge_weight_maps(tg, f_tg)
+        sources.append("framework_catalog")
+
+        slot_id = fw.get("slot_id")
+        cur.execute(
+            "SELECT id FROM training_units WHERE framework_slot_id = %s LIMIT 1",
+            (int(slot_id),),
+        )
+        bp = cur.fetchone()
+        if bp and bp.get("id"):
+            slot_skills = _profile_from_unit_occurrences(cur, int(bp["id"]))
+            if slot_skills:
+                skill_target = _merge_weight_maps(skill_target, slot_skills, scale=1.0)
+                sources.append("framework_slot_skill_profile")
+        if not skill_target:
+            cur.execute(
+                """
+                SELECT tu.id FROM training_framework_slots s
+                LEFT JOIN training_units tu ON tu.framework_slot_id = s.id
+                WHERE s.framework_program_id = %s AND tu.id IS NOT NULL
+                """,
+                (fid,),
+            )
+            all_occ: List[ExerciseOccurrence] = []
+            for r in cur.fetchall():
+                all_occ.extend(collect_unit_exercise_occurrences(cur, int(r["id"])))
+            if all_occ:
+                prof = profile_for_occurrences(cur, all_occ, reference_max_by_skill=None)
+                skill_target = _merge_weight_maps(
+                    skill_target, _skill_weights_from_profile(prof.get("skills") or []), scale=0.85
+                )
+                sources.append("framework_overall_skill_profile")
+
+    if planned_exercise_ids:
+        occ = [ExerciseOccurrence(exercise_id=int(eid)) for eid in planned_exercise_ids]
+        prof = profile_for_occurrences(cur, occ, reference_max_by_skill=None)
+        skill_plan = _skill_weights_from_profile(prof.get("skills") or [])
+        if skill_plan:
+            sources.append("current_unit_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))
+        if ap:
+            if intent in ("deepen_exercise", "suggest_next", "progression_next", "continue_plan_goal"):
+                skill_target = _merge_weight_maps(skill_target, ap.skill_weights, scale=1.0)
+                focus = _merge_weight_maps(focus, ap.focus_area_ids, scale=0.9)
+                style = _merge_weight_maps(style, ap.style_direction_ids, scale=0.75)
+                tt = _merge_weight_maps(tt, ap.training_type_ids, scale=0.75)
+                tg = _merge_weight_maps(tg, ap.target_group_ids, scale=0.75)
+                sources.append("anchor_exercise")
+
+    skill_target = _normalize_weight_map(skill_target)
+    skill_plan_norm = _normalize_weight_map(skill_plan)
+    skill_gap: Dict[int, float] = {}
+    for sid, tw in skill_target.items():
+        pw = skill_plan_norm.get(sid, 0.0)
+        gap = tw - pw * 0.85
+        if gap > 0.08:
+            skill_gap[sid] = gap
+    if skill_gap:
+        sources.append("skill_gap_vs_plan")
+
+    return PlanningTargetProfile(
+        focus_area_ids=_normalize_weight_map(focus) if focus else focus,
+        style_direction_ids=_normalize_weight_map(style) if style else style,
+        training_type_ids=_normalize_weight_map(tt) if tt else tt,
+        target_group_ids=_normalize_weight_map(tg) if tg else tg,
+        skill_weights=skill_target,
+        skill_gap_weights=_normalize_weight_map(skill_gap) if skill_gap else skill_gap,
+        sources=sources,
+    )
+
+
+def score_exercise_against_target(
+    exercise: ExerciseMatchProfile,
+    target: PlanningTargetProfile,
+    *,
+    intent: str,
+) -> Tuple[float, List[str]]:
+    """Profil-Match 0..1 + deutschsprachige Gründe."""
+    reasons: List[str] = []
+
+    focus_sim = weighted_overlap(exercise.focus_area_ids, target.focus_area_ids)
+    style_sim = weighted_overlap(exercise.style_direction_ids, target.style_direction_ids)
+    tt_sim = weighted_overlap(exercise.training_type_ids, target.training_type_ids)
+    tg_sim = weighted_overlap(exercise.target_group_ids, target.target_group_ids)
+    skill_sim = weighted_overlap(
+        _normalize_weight_map(exercise.skill_weights),
+        target.skill_weights,
+    )
+    gap_sim = gap_coverage(target.skill_gap_weights, _normalize_weight_map(exercise.skill_weights))
+
+    if focus_sim >= 0.5 and target.focus_area_ids:
+        reasons.append("Fokusbereich passend zum Planungsziel")
+    if style_sim >= 0.5 and target.style_direction_ids:
+        reasons.append("Stilrichtung passend")
+    if tt_sim >= 0.5 and target.training_type_ids:
+        reasons.append("Trainingsstil passend")
+    if tg_sim >= 0.5 and target.target_group_ids:
+        reasons.append("Zielgruppe passend")
+    if skill_sim >= 0.35 and target.skill_weights:
+        reasons.append("Fähigkeiten-Schwerpunkt passend (Profilmetrik)")
+    if gap_sim >= 0.25 and target.skill_gap_weights:
+        reasons.append("Deckt Skill-Lücke im bisherigen Plan")
+
+    # Intent-gewichtete Dimensionen (Summe = 1.0)
+    if intent == INTENT_FREE_SEARCH:
+        weights = {"focus": 0.15, "style": 0.10, "tt": 0.10, "tg": 0.10, "skill": 0.25, "gap": 0.30}
+    elif intent == INTENT_DEEPEN_EXERCISE:
+        weights = {"focus": 0.15, "style": 0.10, "tt": 0.10, "tg": 0.05, "skill": 0.45, "gap": 0.15}
+    elif intent == INTENT_PROGRESSION_NEXT:
+        weights = {"focus": 0.20, "style": 0.10, "tt": 0.10, "tg": 0.05, "skill": 0.35, "gap": 0.20}
+    else:
+        weights = {"focus": 0.20, "style": 0.10, "tt": 0.10, "tg": 0.10, "skill": 0.30, "gap": 0.20}
+
+    score = (
+        weights["focus"] * focus_sim
+        + weights["style"] * style_sim
+        + weights["tt"] * tt_sim
+        + weights["tg"] * tg_sim
+        + weights["skill"] * skill_sim
+        + weights["gap"] * gap_sim
+    )
+    return max(0.0, min(1.0, score)), reasons
+
+
+# Re-export intent constants for typing (avoid circular import at runtime in suggest module)
+INTENT_FREE_SEARCH = "free_search"
+INTENT_DEEPEN_EXERCISE = "deepen_exercise"
+INTENT_PROGRESSION_NEXT = "progression_next"
diff --git a/backend/planning_exercise_suggest.py b/backend/planning_exercise_suggest.py
index 0e49c01..f0538a3 100644
--- a/backend/planning_exercise_suggest.py
+++ b/backend/planning_exercise_suggest.py
@@ -11,8 +11,12 @@ from typing import Any, Dict, List, Optional, Sequence, Set, Tuple
 from fastapi import HTTPException
 from pydantic import BaseModel, Field
 
-from tenant_context import TenantContext
-from club_tenancy import library_content_visibility_sql
+from tenant_context import TenantContext, library_content_visibility_sql
+from planning_exercise_profiles import (
+    build_planning_target_profile,
+    load_exercise_match_profiles_bulk,
+    score_exercise_against_target,
+)
 
 # Planungs-Berechtigung + Sektionen (bestehende Implementierung)
 from routers.training_planning import (
@@ -71,23 +75,31 @@ def resolve_planning_exercise_intent(query: Optional[str], intent_hint: Optional
 
 def _intent_weights(intent: str) -> Dict[str, float]:
     base = {
-        "fulltext": 0.25,
-        "progression": 0.25,
-        "skill": 0.20,
-        "plan": 0.10,
+        "fulltext": 0.18,
+        "progression": 0.18,
+        "skill": 0.12,
+        "plan": 0.08,
+        "profile": 0.22,
         "repeat_unit": -0.30,
         "repeat_group": -0.15,
     }
     if intent == INTENT_SUGGEST_NEXT:
-        return {**base, "progression": 0.35, "skill": 0.25, "plan": 0.15, "fulltext": 0.10}
+        return {
+            **base,
+            "progression": 0.28,
+            "skill": 0.12,
+            "plan": 0.10,
+            "profile": 0.25,
+            "fulltext": 0.08,
+        }
     if intent == INTENT_PROGRESSION_NEXT:
-        return {**base, "progression": 0.50, "fulltext": 0.15, "skill": 0.15}
+        return {**base, "progression": 0.42, "fulltext": 0.12, "skill": 0.10, "profile": 0.20}
     if intent == INTENT_DEEPEN_EXERCISE:
-        return {**base, "skill": 0.40, "fulltext": 0.20, "progression": 0.15}
+        return {**base, "skill": 0.15, "profile": 0.35, "fulltext": 0.15, "progression": 0.10}
     if intent == INTENT_CONTINUE_PLAN:
-        return {**base, "plan": 0.30, "skill": 0.25, "fulltext": 0.15, "progression": 0.10}
+        return {**base, "plan": 0.12, "skill": 0.10, "profile": 0.30, "fulltext": 0.10, "progression": 0.08}
     if intent == INTENT_FREE_SEARCH:
-        return {**base, "fulltext": 0.55, "progression": 0.10, "skill": 0.10}
+        return {**base, "fulltext": 0.45, "progression": 0.08, "skill": 0.08, "profile": 0.15}
     return base
 
 
@@ -287,6 +299,11 @@ def build_planning_exercise_context_pack(
 
     return {
         "unit_id": int(body.unit_id),
+        "unit": {
+            "id": int(body.unit_id),
+            "framework_slot_id": unit.get("framework_slot_id"),
+            "origin_framework_slot_id": unit.get("origin_framework_slot_id"),
+        },
         "unit_title": (unit.get("title") or unit.get("planned_focus") or "").strip() or None,
         "group_id": unit.get("group_id"),
         "group_name": (unit.get("group_name") or "").strip() or None,
@@ -313,6 +330,14 @@ def suggest_planning_exercises(
     query = _normalize_query(body.query)
     intent = resolve_planning_exercise_intent(query, body.intent_hint)
     weights = _intent_weights(intent)
+    target_profile = build_planning_target_profile(
+        cur,
+        unit=pack["unit"],
+        planned_exercise_ids=pack["planned_exercise_ids"],
+        anchor_exercise_id=pack.get("anchor_exercise_id"),
+        intent=intent,
+    )
+    target_profile_summary = target_profile.to_summary_dict(cur)
 
     profile_id = tenant.profile_id
     role = tenant.global_role
@@ -374,9 +399,10 @@ def suggest_planning_exercises(
     if pack["planned_exercise_ids"]:
         last_planned_skills = _load_skill_ids_for_exercise(cur, pack["planned_exercise_ids"][-1])
 
-    # Skill-IDs pro Kandidat (Batch)
+    # Skill-IDs + ExerciseMatchProfile pro Kandidat (Batch)
     cand_ids = [int(r["id"]) for r in rows]
     skills_by_ex: Dict[int, Set[int]] = {cid: set() for cid in cand_ids}
+    match_profiles = load_exercise_match_profiles_bulk(cur, cand_ids)
     if cand_ids:
         ph = ",".join(["%s"] * len(cand_ids))
         cur.execute(
@@ -416,12 +442,20 @@ def suggest_planning_exercises(
             plan_aff = _skill_jaccard(last_planned_skills, item["skills"])
         repeat_unit = 1.0 if eid in planned_set else 0.0
         repeat_group = 1.0 if eid in group_recent_set else 0.0
+        profile_score = 0.0
+        profile_reasons: List[str] = []
+        emp = match_profiles.get(eid)
+        if emp:
+            profile_score, profile_reasons = score_exercise_against_target(
+                emp, target_profile, intent=intent
+            )
 
         score = (
             weights["fulltext"] * ft_norm
             + weights["progression"] * prog_hit
             + weights["skill"] * skill_sim
             + weights["plan"] * plan_aff
+            + weights["profile"] * profile_score
             + weights["repeat_unit"] * repeat_unit
             + weights["repeat_group"] * repeat_group
         )
@@ -442,11 +476,14 @@ def suggest_planning_exercises(
             reasons.append("Bereits in dieser Einheit eingeplant")
         if repeat_group > 0 and repeat_unit <= 0:
             reasons.append("Kürzlich in der Gruppe verwendet")
+        for pr in profile_reasons:
+            if pr not in reasons:
+                reasons.append(pr)
 
         if score <= 0 and not reasons and not query:
             # Leere Query: trotzdem schwache Kandidaten mit Skill/Progression
-            if prog_hit or skill_sim or plan_aff:
-                score = 0.05 + prog_hit * 0.3 + skill_sim * 0.2
+            if prog_hit or skill_sim or plan_aff or profile_score:
+                score = 0.05 + prog_hit * 0.3 + skill_sim * 0.2 + profile_score * 0.25
 
         hits.append(
             {
@@ -474,6 +511,8 @@ def suggest_planning_exercises(
 
     return {
         "context_summary": context_summary,
+        "target_profile_summary": target_profile_summary,
+        "retrieval_phase": "profile_v1",
         "intent_resolved": intent,
         "query_normalized": query or None,
         "hits": hits,
diff --git a/backend/version.py b/backend/version.py
index de47331..233cd86 100644
--- a/backend/version.py
+++ b/backend/version.py
@@ -1,6 +1,6 @@
 # Shinkan Jinkendo Version Information
 
-APP_VERSION = "0.8.167"
+APP_VERSION = "0.8.169"
 BUILD_DATE = "2026-05-22"
 DB_SCHEMA_VERSION = "20260531071"
 
@@ -27,8 +27,8 @@ MODULE_VERSIONS = {
     "skills": "0.1.1",  # DB 065 karate_relevance + relevance_level; CRUD unterstützt Felder
     "skill_profiles": "1.0.0",  # Phase 3: gewichtetes Fähigkeiten-Profil + skill-discovery/suggestions
     "methods": "0.1.0",
-    "exercises": "2.34.0",  # Planungs-KI P0: POST /planning/exercise-suggest; Picker Kontext
-    "planning_exercise_suggest": "0.1.0",  # Kontext-Pack + Hybrid-Retrieval Übungssuche
+    "exercises": "2.35.0",  # Planungs-KI P0.1: Profil-Score profile_v1 + target_profile_summary
+    "planning_exercise_suggest": "0.2.1",  # Fix Import library_content_visibility_sql aus tenant_context
     "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 +43,21 @@ MODULE_VERSIONS = {
 }
 
 CHANGELOG = [
+    {
+        "version": "0.8.169",
+        "date": "2026-05-22",
+        "changes": [
+            "Fix: planning_exercise_suggest Import library_content_visibility_sql aus tenant_context (Backend-Start/502).",
+        ],
+    },
+    {
+        "version": "0.8.168",
+        "date": "2026-05-22",
+        "changes": [
+            "Planungs-KI P0.1: ExerciseMatchProfile + PlanningTargetProfile — Profil-Score (Fokus, Stil, Skills, Gap) im Hybrid-Retrieval.",
+            "API exercise-suggest: retrieval_phase profile_v1, target_profile_summary; Doku §12–§14.",
+        ],
+    },
     {
         "version": "0.8.167",
         "date": "2026-05-22",
diff --git a/docs/HANDOVER.md b/docs/HANDOVER.md
index 76309f7..a965601 100644
--- a/docs/HANDOVER.md
+++ b/docs/HANDOVER.md
@@ -89,10 +89,10 @@ Das Schema ist gegenüber dem Code zurück: Migration **`022_skills_schema_compl
 - **Varianten:** Speichern in der **Aktionsleiste** persistiert zuerst geänderte Varianten (`persistPendingVariantChanges`), dann Übungs-Stammdaten; „Variante anlegen“ als `type="button"` ohne verschachteltes Formular (`createVariantFromDraft`)
 - **Governance (Übungen):** Owner = `created_by`; Bearbeiten = Ersteller, Plattform-Admin oder `can_plan_in_club` bei `visibility=club`; Löschen `club` = nur `club_admin`; Details **`FEATURES_DELIVERED_2026-Q2.md`** §16, **`EXERCISES_API_SPEC.md`** Permissions
 
-### 2.8 KI Assistenz Übungen & Skill-Katalog-Retrieval (Stand **0.8.167**)
+### 2.8 KI Assistenz Übungen & Skill-Katalog-Retrieval (Stand **0.8.168**)
 
 - **Zielarchitektur (Pflicht fuer Erweiterungen):** `.claude/docs/technical/AI_PROMPT_TARGET_ARCHITECTURE.md` — Kontext-Arten, Composition, Einbindung Planung/Rahmen; Phasenplan P0–P4.
-- **Planungs-Übungssuche (P0):** `.claude/docs/working/PLANNING_EXERCISE_SUGGEST_CONTEXT.md` — Intent-Typen, Context-Pack, Hybrid-Retrieval; **`POST /api/planning/exercise-suggest`**; Frontend **`ExercisePickerModal`** + **`planningContext`** aus **`TrainingUnitEditPage`**.
+- **Planungs-Übungssuche (P0.1):** `.claude/docs/working/PLANNING_EXERCISE_SUGGEST_CONTEXT.md` — Context-Pack, Hybrid-Retrieval + **Profil-Score** (`profile_v1`, `ExerciseMatchProfile` / `PlanningTargetProfile`); **`POST /api/planning/exercise-suggest`**; Frontend **`ExercisePickerModal`** + **`planningContext`** aus **`TrainingUnitEditPage`**.
 - **Doku:** Umsetzung `.claude/docs/working/AI_EXERCISE_IMPLEMENTATION_PLAN.md`; Profil-/JSON-Konzept `.claude/docs/working/AI_SKILL_RETRIEVAL_PROFILES_SPEC.md`; Ist-Prompt/UI **`AI_PROMPT_SYSTEM_SPEC.md`**; API-Felder **`KI_FEATURES_SPEC.md`** §5.2  
 - **Kontext / Job:** **`ai_prompt_context`** (Titel, Ziel, Durchführung, Vorbereitung, Trainer-Hinweise, Fokus); **`ai_prompt_job`** — **`run_exercise_form_ai_suggestion`**; **`ai_prompt_runtime`**; **`exercise_ai`** — OpenRouter  
 - **DB:** **`067`** ai_prompts · **`069`** default_template · **`068`** ai_skill_retrieval_profiles · **`070`** openrouter_model · **`071`** **`exercise_instruction_rewrite`**  
diff --git a/frontend/src/components/ExercisePickerModal.jsx b/frontend/src/components/ExercisePickerModal.jsx
index 3ee890f..e8b0cd4 100644
--- a/frontend/src/components/ExercisePickerModal.jsx
+++ b/frontend/src/components/ExercisePickerModal.jsx
@@ -67,6 +67,7 @@ export default function ExercisePickerModal({
   const [quickAiError, setQuickAiError] = useState('')
   const [quickCreateDraft, setQuickCreateDraft] = useState(null)
   const [planningContextSummary, setPlanningContextSummary] = useState(null)
+  const [planningTargetProfileSummary, setPlanningTargetProfileSummary] = useState(null)
   const [planningIntentResolved, setPlanningIntentResolved] = useState(null)
   const pickerScrollRef = useRef(null)
 
@@ -153,6 +154,7 @@ export default function ExercisePickerModal({
       setQuickAiError('')
       setQuickCreateDraft(null)
       setPlanningContextSummary(null)
+      setPlanningTargetProfileSummary(null)
       setPlanningIntentResolved(null)
       return
     }
@@ -276,6 +278,7 @@ export default function ExercisePickerModal({
             Array.isArray(exerciseKindAny) && exerciseKindAny.length > 0 ? exerciseKindAny : undefined,
         })
         setPlanningContextSummary(res?.context_summary || null)
+        setPlanningTargetProfileSummary(res?.target_profile_summary || null)
         setPlanningIntentResolved(res?.intent_resolved || null)
         const hits = (Array.isArray(res?.hits) ? res.hits : []).map((h) => ({
           id: h.id,
@@ -290,6 +293,7 @@ export default function ExercisePickerModal({
         setHasMore(false)
       } else {
         setPlanningContextSummary(null)
+        setPlanningTargetProfileSummary(null)
         setPlanningIntentResolved(null)
         const batch = await api.listExercises({
           ...queryBase,
@@ -307,6 +311,7 @@ export default function ExercisePickerModal({
       setList([])
       setHasMore(false)
       setPlanningContextSummary(null)
+      setPlanningTargetProfileSummary(null)
       setPlanningIntentResolved(null)
     } finally {
       setLoading(false)
@@ -508,7 +513,28 @@ export default function ExercisePickerModal({
                     Anker: {planningContextSummary.anchor_title}
                   </span>
                 ) : null}
+                {Array.isArray(planningTargetProfileSummary?.focus_areas) &&
+                planningTargetProfileSummary.focus_areas.length > 0
+                  ? planningTargetProfileSummary.focus_areas.map((fa) => (
+                      <span key={fa} className="exercise-tag">
+                        Fokus: {fa}
+                      </span>
+                    ))
+                  : null}
+                {Array.isArray(planningTargetProfileSummary?.top_skills) &&
+                planningTargetProfileSummary.top_skills.length > 0
+                  ? planningTargetProfileSummary.top_skills.slice(0, 3).map((sk) => (
+                      <span key={sk.skill_id} className="exercise-tag">
+                        {sk.name}
+                      </span>
+                    ))
+                  : null}
               </div>
+              {planningTargetProfileSummary?.has_skill_gap ? (
+                <p style={{ margin: '6px 0 0', fontSize: '11px', color: 'var(--text3)' }}>
+                  Skill-Lücke zum bisherigen Plan berücksichtigt
+                </p>
+              ) : null}
               {planningIntentResolved ? (
                 <p style={{ margin: '6px 0 0', fontSize: '11px', color: 'var(--text3)' }}>
                   Modus: {planningIntentResolved.replace(/_/g, ' ')}