Update Skill Scoring Specification and Implementation to v1.2

- Enhanced the skill scoring system with category grouping and a universal scale for improved comparability across programs.
- Introduced new calculations for artifact share percentage and universal percent, allowing for a more nuanced understanding of skill contributions.
- Updated the API to reflect changes in the skill profile structure, including main category and top skill details.
- Improved frontend components to display skills by main category, enhancing user experience in skill discovery and profile visualization.
- Adjusted tests to validate the new scoring logic and ensure accurate representation of skills and their weights.

.claude/docs/technical/SKILL_SCORING_SPEC.md

@@ -1,7 +1,7 @@
 # Gewichtetes Fähigkeiten-Scoring (Phase 3)
 
 **Stand:** 2026-05-20  
-**Status:** Variante A (regelbasiert) umgesetzt — **v1.1** (Intensität + Stufen-Spanne, ohne Primär)  
+**Status:** Variante A (regelbasiert) umgesetzt — **v1.2** (Kategorien-Gruppierung + universelle Skala)  
 **Modul:** `backend/skill_scoring.py`, Router `skill_profiles`
 
 ## Ziel
@@ -55,8 +55,12 @@ Beispiel: von Grundlagen bis Aufbau (Spanne 2) > nur Basis (Spanne 1).
 
 Aggregation:
 
-- Summe pro `skill_id` → `weight`
-- `share_percent` = Anteil an `total_weight` (100 % über alle Skills im Profil)
+- Summe pro `skill_id` → `weight` / `score` (Trainingsgewicht in gewichteten Minuten — **absolut, über Programme vergleichbar**)
+- `artifact_share_percent` / `share_percent` = Anteil an `total_weight` **innerhalb dieses Artefakts** (summiert 100 % — nur noch sekundär)
+- `by_main_category[]` → je Unterkategorie `top_skill` (stärkste Fähigkeit nach absolutem Gewicht)
+- `universal_percent` = `weight / max_weight_in_corpus(skill_id) × 100` — Referenz aus sichtbarer Bibliothek (`compute_corpus_skill_max_weights`, bis 50 Artefakte je Typ)
+
+Discovery-Sortierung und Vorschläge nutzen **`match_score`** (= Summe absoluter Gewichte der gewählten Fähigkeiten), nicht den Plan-internen Anteil.
 
 ## API
 

backend/routers/skill_profiles.py

@@ -17,6 +17,7 @@ from skill_scoring import (
     collect_module_exercise_occurrences,
     collect_progression_graph_exercise_occurrences,
     collect_unit_exercise_occurrences,
+    compute_corpus_skill_max_weights,
     compute_skill_profile,
     match_score_for_skill_ids,
     profile_for_occurrences,
@@ -69,6 +70,13 @@ def framework_program_skill_profile(
         )
         slots_raw = [r2d(r) for r in cur.fetchall()]
 
+        ref_max = compute_corpus_skill_max_weights(
+            cur,
+            profile_id=profile_id,
+            role=role,
+            effective_club_id=tenant.effective_club_id,
+        )
+
         all_occurrences: List[ExerciseOccurrence] = []
         slot_profiles: List[Dict[str, Any]] = []
 
@@ -89,7 +97,11 @@ def framework_program_skill_profile(
                 all_occurrences.extend(slot_occ)
             else:
                 slot_occ = []
-            slot_profile = profile_for_occurrences(cur, slot_occ) if slot_occ else _empty_profile()
+            slot_profile = (
+                profile_for_occurrences(cur, slot_occ, reference_max_by_skill=ref_max)
+                if slot_occ
+                else _empty_profile()
+            )
             slot_profiles.append(
                 {
                     "slot_id": slot["id"],
@@ -101,12 +113,20 @@ def framework_program_skill_profile(
                 }
             )
 
-        overall = profile_for_occurrences(cur, all_occurrences) if all_occurrences else _empty_profile()
+        overall = (
+            profile_for_occurrences(cur, all_occurrences, reference_max_by_skill=ref_max)
+            if all_occurrences
+            else _empty_profile()
+        )
 
         return {
             "artifact_type": "framework_program",
             "artifact_id": framework_id,
             "artifact_title": row.get("title"),
+            "reference_scale": {
+                "skills_in_corpus": len(ref_max),
+                "description": "universal_percent = Anteil am höchsten Trainingsgewicht dieser Fähigkeit in der sichtbaren Bibliothek",
+            },
             "overall": overall,
             "slots": slot_profiles,
         }
@@ -122,12 +142,25 @@ def training_module_skill_profile(
     with get_db() as conn:
         cur = get_cursor(conn)
         row = _module_access(cur, module_id, profile_id, role)
+        ref_max = compute_corpus_skill_max_weights(
+            cur,
+            profile_id=profile_id,
+            role=role,
+            effective_club_id=tenant.effective_club_id,
+        )
         occurrences = collect_module_exercise_occurrences(cur, module_id)
-        overall = profile_for_occurrences(cur, occurrences) if occurrences else _empty_profile()
+        overall = (
+            profile_for_occurrences(cur, occurrences, reference_max_by_skill=ref_max)
+            if occurrences
+            else _empty_profile()
+        )
         return {
             "artifact_type": "training_module",
             "artifact_id": module_id,
             "artifact_title": row.get("title"),
+            "reference_scale": {
+                "skills_in_corpus": len(ref_max),
+            },
             "overall": overall,
         }
 
@@ -142,22 +175,34 @@ def progression_graph_skill_profile(
     with get_db() as conn:
         cur = get_cursor(conn)
         row = _require_graph_read(cur, graph_id, profile_id, role)
+        ref_max = compute_corpus_skill_max_weights(
+            cur,
+            profile_id=profile_id,
+            role=role,
+            effective_club_id=tenant.effective_club_id,
+        )
         occurrences = collect_progression_graph_exercise_occurrences(cur, graph_id)
-        overall = profile_for_occurrences(
-            cur, occurrences, default_item_minutes=GRAPH_DEFAULT_ITEM_MINUTES
-        ) if occurrences else _empty_profile()
+        overall = (
+            profile_for_occurrences(
+                cur,
+                occurrences,
+                default_item_minutes=GRAPH_DEFAULT_ITEM_MINUTES,
+                reference_max_by_skill=ref_max,
+            )
+            if occurrences
+            else _empty_profile()
+        )
         return {
             "artifact_type": "progression_graph",
             "artifact_id": graph_id,
             "artifact_title": row.get("name"),
+            "reference_scale": {
+                "skills_in_corpus": len(ref_max),
+            },
             "overall": overall,
         }
 
 
-def _empty_profile() -> Dict[str, Any]:
-    return compute_skill_profile([], {})
-
-
 @router.get("/skill-discovery/suggestions")
 def skill_discovery_suggestions(
     skill_ids: str = Query(..., description="Komma-getrennte skill-IDs"),
@@ -232,11 +277,8 @@ def skill_discovery_suggestions(
                         "path": f"/planning/framework-programs/{fid}",
                         "match": match,
                         "skill_profile_summary": {
-                            "total_weight": prof.get("total_weight"),
-                            "top_skills": [
-                                {"skill_id": s["skill_id"], "skill_name": s["skill_name"], "share_percent": s["share_percent"]}
-                                for s in (prof.get("skills") or [])[:5]
-                            ],
+                            "total_score": prof.get("total_score"),
+                            "top_by_category": _top_categories_summary(prof),
                         },
                     }
                 )
@@ -279,11 +321,8 @@ def skill_discovery_suggestions(
                         "path": f"/planning/training-modules/{mid}",
                         "match": match,
                         "skill_profile_summary": {
-                            "total_weight": prof.get("total_weight"),
-                            "top_skills": [
-                                {"skill_id": s["skill_id"], "skill_name": s["skill_name"], "share_percent": s["share_percent"]}
-                                for s in (prof.get("skills") or [])[:5]
-                            ],
+                            "total_score": prof.get("total_score"),
+                            "top_by_category": _top_categories_summary(prof),
                         },
                     }
                 )
@@ -328,20 +367,14 @@ def skill_discovery_suggestions(
                         "path": None,
                         "match": match,
                         "skill_profile_summary": {
-                            "total_weight": prof.get("total_weight"),
-                            "top_skills": [
-                                {"skill_id": s["skill_id"], "skill_name": s["skill_name"], "share_percent": s["share_percent"]}
-                                for s in (prof.get("skills") or [])[:5]
-                            ],
+                            "total_score": prof.get("total_score"),
+                            "top_by_category": _top_categories_summary(prof),
                         },
                     }
                 )
 
     results.sort(
-        key=lambda x: (
-            -float(x.get("match", {}).get("match_weight") or 0),
-            -(float(x.get("match", {}).get("match_percent") or 0)),
-        )
+        key=lambda x: -float(x.get("match", {}).get("match_score") or x.get("match", {}).get("match_weight") or 0),
     )
     return {
         "skill_ids": wanted,
@@ -350,5 +383,27 @@ def skill_discovery_suggestions(
     }
 
 
+def _top_categories_summary(profile: Dict[str, Any], limit: int = 6) -> List[Dict[str, Any]]:
+    """Kurzliste Top-Fähigkeit je Unterkategorie für Discovery-Treffer."""
+    out: List[Dict[str, Any]] = []
+    for mc in profile.get("by_main_category") or []:
+        for cat in mc.get("categories") or []:
+            top = cat.get("top_skill")
+            if not top:
+                continue
+            out.append(
+                {
+                    "main_category_name": mc.get("main_category_name"),
+                    "category_name": cat.get("category_name"),
+                    "skill_id": top.get("skill_id"),
+                    "skill_name": top.get("skill_name"),
+                    "score": top.get("score") or top.get("weight"),
+                }
+            )
+            if len(out) >= limit:
+                return out
+    return out
+
+
 def _empty_profile() -> Dict[str, Any]:
     return compute_skill_profile([], {})

backend/skill_scoring.py

@@ -116,11 +116,93 @@ def _round2(val: float) -> float:
     return round(val, 2)
 
 
+def _build_by_main_category(skills_out: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
+    """Hierarchie Hauptkategorie → Unterkategorie, je Top-Fähigkeit nach absolutem Gewicht."""
+    main_map: Dict[int, Dict[str, Any]] = {}
+
+    for sk in skills_out:
+        mc_id = int(sk.get("main_category_id") or 0)
+        mc_name = (sk.get("main_category_name") or "").strip() or "—"
+        cat_id = int(sk.get("category_id") or 0)
+        cat_name = (sk.get("category_name") or sk.get("category") or "").strip() or "—"
+
+        if mc_id not in main_map:
+            main_map[mc_id] = {
+                "main_category_id": mc_id if mc_id else None,
+                "main_category_name": mc_name,
+                "weight": 0.0,
+                "categories": {},
+            }
+        main = main_map[mc_id]
+        main["weight"] += float(sk.get("weight") or 0)
+
+        if cat_id not in main["categories"]:
+            main["categories"][cat_id] = {
+                "category_id": cat_id if cat_id else None,
+                "category_name": cat_name,
+                "weight": 0.0,
+                "skills": [],
+            }
+        cat = main["categories"][cat_id]
+        cat["weight"] += float(sk.get("weight") or 0)
+        cat["skills"].append(sk)
+
+    result: List[Dict[str, Any]] = []
+    for mc in sorted(main_map.values(), key=lambda x: (-x["weight"], x.get("main_category_name") or "")):
+        cats_out: List[Dict[str, Any]] = []
+        for cat in sorted(
+            mc["categories"].values(),
+            key=lambda x: (-x["weight"], x.get("category_name") or ""),
+        ):
+            cat_skills = sorted(
+                cat["skills"],
+                key=lambda x: (-float(x.get("weight") or 0), x.get("skill_name") or ""),
+            )
+            top = cat_skills[0] if cat_skills else None
+            cats_out.append(
+                {
+                    "category_id": cat["category_id"],
+                    "category_name": cat["category_name"],
+                    "weight": _round2(cat["weight"]),
+                    "skills_count": len(cat_skills),
+                    "top_skill": top,
+                }
+            )
+        result.append(
+            {
+                "main_category_id": mc["main_category_id"],
+                "main_category_name": mc["main_category_name"],
+                "weight": _round2(mc["weight"]),
+                "categories": cats_out,
+            }
+        )
+    return result
+
+
+def _apply_reference_universal_percent(
+    skills_out: List[Dict[str, Any]],
+    reference_max_by_skill: Optional[Dict[int, float]] = None,
+) -> None:
+    """
+    Optional: Stärke relativ zum Maximum in der sichtbaren Bibliothek (gleiche Skala über Artefakte).
+    """
+    if not reference_max_by_skill:
+        for sk in skills_out:
+            sk["universal_percent"] = None
+        return
+    for sk in skills_out:
+        sid = int(sk["skill_id"])
+        ref = float(reference_max_by_skill.get(sid) or 0)
+        w = float(sk.get("weight") or 0)
+        sk["universal_percent"] = _round2(w / ref * 100.0) if ref > 0 else None
+
+
 def compute_skill_profile(
     occurrences: Sequence[ExerciseOccurrence],
     skill_rows_by_exercise: Dict[int, List[Dict[str, Any]]],
     *,
     default_item_minutes: int = DEFAULT_ITEM_MINUTES,
+    reference_max_by_skill: Optional[Dict[int, float]] = None,
 ) -> Dict[str, Any]:
     """
     Erzeugt ein normalisiertes Fähigkeiten-Profil aus Übungsvorkommen und exercise_skills.
@@ -168,7 +250,11 @@ def compute_skill_profile(
                 skill_acc[sid] = {
                     "skill_id": sid,
                     "skill_name": link.get("skill_name") or f"Fähigkeit #{sid}",
-                    "category": link.get("category"),
+                    "category": link.get("category_name") or link.get("category"),
+                    "category_id": link.get("category_id"),
+                    "category_name": link.get("category_name") or link.get("category"),
+                    "main_category_id": link.get("main_category_id"),
+                    "main_category_name": link.get("main_category_name"),
                     "focus_areas": link.get("focus_areas"),
                     "weight": 0.0,
                     "occurrence_count": 0,
@@ -205,9 +291,15 @@ def compute_skill_profile(
             {
                 "skill_id": sid,
                 "skill_name": acc["skill_name"],
-                "category": acc.get("category"),
+                "category": acc.get("category_name") or acc.get("category"),
+                "category_id": acc.get("category_id"),
+                "category_name": acc.get("category_name") or acc.get("category"),
+                "main_category_id": acc.get("main_category_id"),
+                "main_category_name": acc.get("main_category_name"),
                 "focus_areas": acc.get("focus_areas"),
                 "weight": _round2(acc["weight"]),
+                "score": _round2(acc["weight"]),
+                "artifact_share_percent": _round2(share),
                 "share_percent": _round2(share),
                 "occurrence_count": acc["occurrence_count"],
                 "top_exercises": ex_list,
@@ -215,27 +307,32 @@ def compute_skill_profile(
         )
     skills_out.sort(key=lambda x: (-x["weight"], x.get("skill_name") or ""))
 
-    by_category: Dict[str, float] = defaultdict(float)
-    for sk in skills_out:
-        cat = (sk.get("category") or "").strip() or "—"
-        by_category[cat] += sk["weight"]
-    category_rows = []
-    for cat, w in sorted(by_category.items(), key=lambda x: (-x[1], x[0])):
-        share = (w / total_weight * 100.0) if total_weight > 0 else 0.0
-        category_rows.append(
-            {"category": cat, "weight": _round2(w), "share_percent": _round2(share)}
-        )
+    _apply_reference_universal_percent(skills_out, reference_max_by_skill)
+
+    by_main_category = _build_by_main_category(skills_out)
+    for mc in by_main_category:
+        for cat in mc.get("categories") or []:
+            top = cat.get("top_skill")
+            if top and reference_max_by_skill:
+                sid = int(top["skill_id"])
+                ref = float(reference_max_by_skill.get(sid) or 0)
+                if ref > 0:
+                    top["universal_percent"] = _round2(float(top["weight"]) / ref * 100.0)
 
     unique_exercises = len(exercise_meta)
     return {
         "computed_at": datetime.now(timezone.utc).isoformat(),
-        "scoring_version": "1.1",
+        "scoring_version": "1.2",
+        "score_unit": "weighted_minutes",
+        "score_unit_label": "Trainingsgewicht (gewichtete Minuten, über Programme vergleichbar)",
         "total_weight": _round2(total_weight),
+        "total_score": _round2(total_weight),
         "exercise_occurrence_count": total_occurrences,
         "distinct_exercise_count": unique_exercises,
         "exercises_with_skills_count": len(exercises_with_skills),
         "skills": skills_out,
-        "by_category": category_rows,
+        "by_main_category": by_main_category,
+        "has_reference_scale": bool(reference_max_by_skill),
     }
 
 
@@ -250,10 +347,15 @@ def fetch_exercise_skills_bulk(
         f"""
         SELECT es.exercise_id, es.skill_id, es.is_primary, es.intensity,
                es.development_contribution, es.required_level, es.target_level,
-               s.name AS skill_name, s.category, s.focus_areas,
+               s.name AS skill_name, s.category,
+               sc.id AS category_id, sc.name AS category_name,
+               mc.id AS main_category_id, mc.name AS main_category_name,
+               s.focus_areas,
                e.title AS exercise_title
         FROM exercise_skills es
         JOIN skills s ON s.id = es.skill_id
+        LEFT JOIN skill_categories sc ON sc.id = s.category_id
+        LEFT JOIN skill_main_categories mc ON mc.id = COALESCE(s.main_category_id, sc.main_category_id)
         JOIN exercises e ON e.id = es.exercise_id
         WHERE es.exercise_id IN ({ph})
           AND (s.status = 'active' OR s.status IS NULL)
@@ -346,21 +448,145 @@ def profile_for_occurrences(
     occurrences: Sequence[ExerciseOccurrence],
     *,
     default_item_minutes: int = DEFAULT_ITEM_MINUTES,
+    reference_max_by_skill: Optional[Dict[int, float]] = None,
 ) -> Dict[str, Any]:
     eids = [o.exercise_id for o in occurrences]
     skills_map = fetch_exercise_skills_bulk(cur, eids)
     return compute_skill_profile(
-        occurrences, skills_map, default_item_minutes=default_item_minutes
+        occurrences,
+        skills_map,
+        default_item_minutes=default_item_minutes,
+        reference_max_by_skill=reference_max_by_skill,
     )
 
 
+def merge_skill_weights_into_max(
+    target: Dict[int, float],
+    profile: Dict[str, Any],
+) -> None:
+    for sk in profile.get("skills") or []:
+        sid = int(sk["skill_id"])
+        w = float(sk.get("weight") or 0)
+        if w > target.get(sid, 0.0):
+            target[sid] = w
+
+
+def compute_corpus_skill_max_weights(
+    cur,
+    *,
+    profile_id: int,
+    role: Optional[str],
+    effective_club_id: Optional[int],
+    limit_per_type: int = 50,
+) -> Dict[int, float]:
+    """
+    Maximum absolutes Trainingsgewicht je Fähigkeit über sichtbare Bibliotheksartefakte.
+    Basis für universal_percent (Skala über alle Programme).
+    """
+    from tenant_context import library_content_visibility_sql
+
+    max_by_skill: Dict[int, float] = {}
+
+    def scan_frameworks():
+        vis_clause, vis_params = library_content_visibility_sql(
+            alias="fp",
+            profile_id=profile_id,
+            role=role,
+            effective_club_id=effective_club_id,
+        )
+        cur.execute(
+            f"""
+            SELECT fp.id FROM training_framework_programs fp
+            WHERE ({vis_clause})
+            ORDER BY fp.updated_at DESC NULLS LAST
+            LIMIT %s
+            """,
+            (*vis_params, limit_per_type),
+        )
+        for row in cur.fetchall():
+            fid = int(row["id"])
+            cur.execute(
+                """
+                SELECT tu.id
+                FROM training_framework_slots s
+                INNER JOIN training_units tu ON tu.framework_slot_id = s.id
+                WHERE s.framework_program_id = %s
+                """,
+                (fid,),
+            )
+            occ: List[ExerciseOccurrence] = []
+            for u in cur.fetchall():
+                occ.extend(collect_unit_exercise_occurrences(cur, int(u["id"])))
+            if not occ:
+                continue
+            prof = profile_for_occurrences(cur, occ)
+            merge_skill_weights_into_max(max_by_skill, prof)
+
+    def scan_modules():
+        vis_clause, vis_params = library_content_visibility_sql(
+            alias="m",
+            profile_id=profile_id,
+            role=role,
+            effective_club_id=effective_club_id,
+        )
+        cur.execute(
+            f"""
+            SELECT m.id FROM training_modules m
+            WHERE ({vis_clause})
+            ORDER BY m.updated_at DESC NULLS LAST
+            LIMIT %s
+            """,
+            (*vis_params, limit_per_type),
+        )
+        for row in cur.fetchall():
+            mid = int(row["id"])
+            occ = collect_module_exercise_occurrences(cur, mid)
+            if not occ:
+                continue
+            prof = profile_for_occurrences(cur, occ)
+            merge_skill_weights_into_max(max_by_skill, prof)
+
+    def scan_graphs():
+        vis_clause, vis_params = library_content_visibility_sql(
+            alias="g",
+            profile_id=profile_id,
+            role=role,
+            effective_club_id=effective_club_id,
+        )
+        cur.execute(
+            f"""
+            SELECT g.id FROM exercise_progression_graphs g
+            WHERE ({vis_clause})
+            ORDER BY g.updated_at DESC NULLS LAST
+            LIMIT %s
+            """,
+            (*vis_params, limit_per_type),
+        )
+        for row in cur.fetchall():
+            gid = int(row["id"])
+            occ = collect_progression_graph_exercise_occurrences(cur, gid)
+            if not occ:
+                continue
+            prof = profile_for_occurrences(
+                cur, occ, default_item_minutes=GRAPH_DEFAULT_ITEM_MINUTES
+            )
+            merge_skill_weights_into_max(max_by_skill, prof)
+
+    scan_frameworks()
+    scan_modules()
+    scan_graphs()
+    return max_by_skill
+
+
 def match_score_for_skill_ids(profile: Dict[str, Any], skill_ids: Sequence[int]) -> Dict[str, Any]:
     """Überlappung eines Profils mit gewünschten Fähigkeiten (für Vorschläge)."""
     wanted = {int(x) for x in skill_ids if x is not None}
     if not wanted:
         return {
             "match_weight": 0.0,
+            "match_score": 0.0,
             "match_percent": 0.0,
+            "artifact_focus_percent": 0.0,
             "matched_skill_ids": [],
             "matched_skills": [],
         }
@@ -372,10 +598,12 @@ def match_score_for_skill_ids(profile: Dict[str, Any], skill_ids: Sequence[int])
         if sid in wanted:
             matched.append(sk)
             match_weight += float(sk.get("weight") or 0)
-    match_percent = (match_weight / total * 100.0) if total > 0 else 0.0
+    artifact_focus = (match_weight / total * 100.0) if total > 0 else 0.0
     return {
         "match_weight": _round2(match_weight),
-        "match_percent": _round2(match_percent),
+        "match_score": _round2(match_weight),
+        "match_percent": _round2(artifact_focus),
+        "artifact_focus_percent": _round2(artifact_focus),
         "matched_skill_ids": [int(m["skill_id"]) for m in matched],
         "matched_skills": matched,
     }

backend/tests/test_skill_scoring.py

@@ -40,6 +40,10 @@ def test_compute_skill_profile_aggregates_weights():
                 "skill_id": 10,
                 "skill_name": "Distanz",
                 "category": "kihon",
+                "category_id": 1,
+                "category_name": "kihon",
+                "main_category_id": 100,
+                "main_category_name": "Technik",
                 "intensity": "hoch",
                 "required_level": "grundlagen",
                 "target_level": "aufbau",
@@ -49,6 +53,10 @@ def test_compute_skill_profile_aggregates_weights():
                 "skill_id": 11,
                 "skill_name": "Balance",
                 "category": "kihon",
+                "category_id": 1,
+                "category_name": "kihon",
+                "main_category_id": 100,
+                "main_category_name": "Technik",
                 "intensity": "niedrig",
                 "required_level": "basis",
                 "target_level": "basis",
@@ -57,13 +65,40 @@ def test_compute_skill_profile_aggregates_weights():
         ],
     }
     profile = compute_skill_profile(occurrences, skills_map)
-    assert profile["scoring_version"] == "1.1"
+    assert profile["scoring_version"] == "1.2"
     assert profile["exercise_occurrence_count"] == 2
     assert profile["distinct_exercise_count"] == 1
     assert len(profile["skills"]) == 2
     assert profile["skills"][0]["skill_id"] == 10
     assert profile["total_weight"] > profile["skills"][1]["weight"]
     assert abs(sum(s["share_percent"] for s in profile["skills"]) - 100.0) < 0.1
+    assert len(profile["by_main_category"]) == 1
+    assert profile["by_main_category"][0]["categories"][0]["top_skill"]["skill_id"] == 10
+
+
+def test_universal_percent_against_corpus_max():
+    occurrences = [ExerciseOccurrence(exercise_id=1, planned_duration_min=50)]
+    skills_map = {
+        1: [
+            {
+                "skill_id": 10,
+                "skill_name": "Koordination",
+                "category_name": "Koordination",
+                "category_id": 2,
+                "main_category_id": 200,
+                "main_category_name": "Körper",
+            },
+        ],
+    }
+    profile = compute_skill_profile(
+        occurrences,
+        skills_map,
+        reference_max_by_skill={10: 100.0},
+    )
+    assert profile["has_reference_scale"] is True
+    assert profile["skills"][0]["universal_percent"] == 50.0
+    top = profile["by_main_category"][0]["categories"][0]["top_skill"]
+    assert top["universal_percent"] == 50.0
 
 
 def test_match_score_for_skill_ids():
@@ -76,5 +111,7 @@ def test_match_score_for_skill_ids():
     }
     m = match_score_for_skill_ids(profile, [1])
     assert m["match_weight"] == 40.0
+    assert m["match_score"] == 40.0
     assert m["match_percent"] == 40.0
+    assert m["artifact_focus_percent"] == 40.0
     assert m["matched_skill_ids"] == [1]

frontend/src/app.css

@@ -2591,6 +2591,46 @@ html.modal-scroll-locked .app-main {
   color: var(--text3);
   margin-top: 3px;
 }
+.skill-profile__by-category {
+  display: flex;
+  flex-direction: column;
+  gap: 14px;
+}
+.skill-profile__main-cat {
+  padding: 10px 12px;
+  border-radius: 10px;
+  background: var(--surface2);
+  border: 1px solid var(--border);
+}
+.skill-profile__main-cat-title {
+  margin: 0 0 8px;
+  font-size: 0.82rem;
+  font-weight: 700;
+  color: var(--text2);
+  text-transform: uppercase;
+  letter-spacing: 0.04em;
+}
+.skill-profile__cat-list {
+  list-style: none;
+  margin: 0;
+  padding: 0;
+  display: flex;
+  flex-direction: column;
+  gap: 10px;
+}
+.skill-profile__cat-item {
+  display: flex;
+  flex-direction: column;
+  gap: 4px;
+}
+.skill-profile__cat-label {
+  font-size: 0.75rem;
+  font-weight: 600;
+  color: var(--text3);
+}
+.skill-profile__cat-row {
+  list-style: none;
+}
 .skill-profile__categories {
   margin-top: 0.85rem;
 }
@@ -2744,6 +2784,25 @@ html.modal-scroll-locked .app-main {
   font-size: 0.85rem;
   color: var(--text2);
 }
+.skill-discovery__result-cats {
+  list-style: none;
+  margin: 0 0 8px;
+  padding: 0;
+  display: flex;
+  flex-direction: column;
+  gap: 4px;
+  font-size: 0.8rem;
+  color: var(--text2);
+}
+.skill-discovery__result-cats li {
+  display: flex;
+  flex-wrap: wrap;
+  gap: 6px;
+}
+.skill-discovery__result-cat-name {
+  font-weight: 600;
+  color: var(--text3);
+}
 .skill-discovery__result-link {
   text-decoration: none;
 }

frontend/src/components/skills/SkillDiscoveryPanel.jsx

@@ -8,6 +8,12 @@ const ARTIFACT_LABELS = {
   progression_graph: 'Regressionspfad',
 }
 
+function formatScore(value) {
+  const n = Number(value)
+  if (!Number.isFinite(n)) return '0'
+  return n % 1 === 0 ? String(n) : n.toFixed(1)
+}
+
 /**
  * Vorschläge für Planungsartefakte anhand gewählter Fähigkeiten (Phase 3).
  */
@@ -60,8 +66,8 @@ export default function SkillDiscoveryPanel({ skills = [] }) {
       <h2 className="skill-discovery__title">Planungs-Vorschläge</h2>
       <p className="form-sub skill-discovery__lead">
         Wähle Fähigkeiten, die du schwerpunktmäßig entwickeln willst — Shinkan schlägt passende
-        Rahmenprogramme, Trainingsmodule und Regressionspfade aus der Bibliothek vor (gewichtet nach
-        Übungs-Verknüpfungen).
+        Rahmenprogramme, Trainingsmodule und Regressionspfade vor. Sortierung nach absolutem
+        Trainingsgewicht (nicht nach Anteil innerhalb des Plans).
       </p>
 
       <div className="form-row">
@@ -122,35 +128,51 @@ export default function SkillDiscoveryPanel({ skills = [] }) {
 
       {result?.suggestions?.length > 0 ? (
         <ul className="skill-discovery__results">
-          {result.suggestions.map((item) => (
-            <li key={`${item.artifact_type}-${item.artifact_id}`} className="skill-discovery__result card">
-              <div className="skill-discovery__result-head">
-                <span className="skill-discovery__result-type">
-                  {ARTIFACT_LABELS[item.artifact_type] || item.artifact_type}
-                </span>
-                <span className="skill-discovery__result-match">
-                  Passung {item.match?.match_percent ?? 0}%
-                </span>
-              </div>
-              <strong className="skill-discovery__result-title">
-                {item.artifact_title || `#${item.artifact_id}`}
-              </strong>
-              {item.match?.matched_skills?.length > 0 ? (
-                <p className="skill-discovery__result-skills">
-                  {item.match.matched_skills.map((m) => m.skill_name).join(' · ')}
-                </p>
-              ) : null}
-              {item.path ? (
-                <Link to={item.path} className="btn btn-secondary btn-small skill-discovery__result-link">
-                  Öffnen
-                </Link>
-              ) : item.artifact_type === 'progression_graph' ? (
-                <p className="form-sub" style={{ margin: '8px 0 0' }}>
-                  Regressionspfad in der Übungsliste unter „Progressionsgraph“ bearbeiten.
-                </p>
-              ) : null}
-            </li>
-          ))}
+          {result.suggestions.map((item) => {
+            const matchScore = item.match?.match_score ?? item.match?.match_weight ?? 0
+            const focusPct = item.match?.artifact_focus_percent ?? item.match?.match_percent
+            const topByCat = item.skill_profile_summary?.top_by_category || []
+            return (
+              <li key={`${item.artifact_type}-${item.artifact_id}`} className="skill-discovery__result card">
+                <div className="skill-discovery__result-head">
+                  <span className="skill-discovery__result-type">
+                    {ARTIFACT_LABELS[item.artifact_type] || item.artifact_type}
+                  </span>
+                  <span className="skill-discovery__result-match" title="Summe der Trainingsgewichte der gewählten Fähigkeiten">
+                    Gewicht {formatScore(matchScore)}
+                  </span>
+                </div>
+                <strong className="skill-discovery__result-title">
+                  {item.artifact_title || `#${item.artifact_id}`}
+                </strong>
+                {item.match?.matched_skills?.length > 0 ? (
+                  <p className="skill-discovery__result-skills">
+                    {item.match.matched_skills.map((m) => m.skill_name).join(' · ')}
+                    {focusPct != null ? ` (${formatScore(focusPct)}% des Plans)` : null}
+                  </p>
+                ) : null}
+                {topByCat.length > 0 ? (
+                  <ul className="skill-discovery__result-cats">
+                    {topByCat.slice(0, 4).map((row) => (
+                      <li key={`${row.category_name}-${row.skill_id}`}>
+                        <span className="skill-discovery__result-cat-name">{row.category_name}</span>
+                        <span>{row.skill_name}</span>
+                      </li>
+                    ))}
+                  </ul>
+                ) : null}
+                {item.path ? (
+                  <Link to={item.path} className="btn btn-secondary btn-small skill-discovery__result-link">
+                    Öffnen
+                  </Link>
+                ) : item.artifact_type === 'progression_graph' ? (
+                  <p className="form-sub" style={{ margin: '8px 0 0' }}>
+                    Regressionspfad in der Übungsliste unter „Progressionsgraph“ bearbeiten.
+                  </p>
+                ) : null}
+              </li>
+            )
+          })}
         </ul>
       ) : result && !loading ? (
         <p className="form-sub skill-discovery__no-hit">

frontend/src/components/skills/SkillProfilePanel.jsx

@@ -1,25 +1,107 @@
 import React, { useMemo, useState } from 'react'
 
-function SkillBar({ skill, maxShare }) {
-  const pct = maxShare > 0 ? Math.min(100, (skill.share_percent / maxShare) * 100) : 0
+function skillWeight(skill) {
+  return Number(skill?.weight ?? skill?.score ?? 0)
+}
+
+function formatWeight(value) {
+  const n = Number(value)
+  if (!Number.isFinite(n)) return '0'
+  return n % 1 === 0 ? String(n) : n.toFixed(1)
+}
+
+function barFillPercent(skill, maxWeight, hasReferenceScale) {
+  if (hasReferenceScale && skill?.universal_percent != null) {
+    return Math.min(100, Number(skill.universal_percent))
+  }
+  const w = skillWeight(skill)
+  return maxWeight > 0 ? Math.min(100, (w / maxWeight) * 100) : 0
+}
+
+function metricLabel(skill, hasReferenceScale) {
+  if (hasReferenceScale && skill?.universal_percent != null) {
+    return `${skill.universal_percent}% Bibliothek`
+  }
+  return formatWeight(skillWeight(skill))
+}
+
+function CategoryTopSkill({ skill, maxWeight, hasReferenceScale }) {
+  if (!skill) return null
+  const pct = barFillPercent(skill, maxWeight, hasReferenceScale)
   return (
-    <li className="skill-profile__row">
+    <li className="skill-profile__cat-row">
       <div className="skill-profile__row-head">
         <span className="skill-profile__name" title={skill.skill_name}>
           {skill.skill_name}
         </span>
-        <span className="skill-profile__pct">{skill.share_percent}%</span>
+        <span className="skill-profile__pct" title="Trainingsgewicht (gewichtete Minuten)">
+          {metricLabel(skill, hasReferenceScale)}
+        </span>
       </div>
       <div className="skill-profile__bar-track" aria-hidden="true">
-        <div
-          className="skill-profile__bar-fill"
-          style={{ width: `${pct}%` }}
-        />
+        <div className="skill-profile__bar-fill" style={{ width: `${pct}%` }} />
       </div>
+      {hasReferenceScale ? (
+        <span className="skill-profile__meta-hint">
+          Absolut: {formatWeight(skillWeight(skill))} · Skala über alle Programme
+        </span>
+      ) : null}
     </li>
   )
 }
 
+function CategoryGroupedProfile({ profile, ariaLabel }) {
+  const groups = profile?.by_main_category || []
+  const hasReferenceScale = Boolean(profile?.has_reference_scale)
+  const maxWeight = useMemo(() => {
+    let max = 1
+    for (const mc of groups) {
+      for (const cat of mc.categories || []) {
+        const w = skillWeight(cat.top_skill)
+        if (w > max) max = w
+      }
+    }
+    return max
+  }, [groups])
+
+  if (!groups.length) return null
+
+  return (
+    <div className="skill-profile__by-category" aria-label={ariaLabel}>
+      {groups.map((mc) => (
+        <section key={mc.main_category_id ?? mc.main_category_name} className="skill-profile__main-cat">
+          <h4 className="skill-profile__main-cat-title">{mc.main_category_name}</h4>
+          <ul className="skill-profile__cat-list">
+            {(mc.categories || []).map((cat) => (
+              <li key={cat.category_id ?? cat.category_name} className="skill-profile__cat-item">
+                <span className="skill-profile__cat-label">{cat.category_name}</span>
+                <CategoryTopSkill
+                  skill={cat.top_skill}
+                  maxWeight={maxWeight}
+                  hasReferenceScale={hasReferenceScale}
+                />
+              </li>
+            ))}
+          </ul>
+        </section>
+      ))}
+    </div>
+  )
+}
+
+function topCategoryBadge(profile) {
+  const parts = []
+  for (const mc of profile?.by_main_category || []) {
+    for (const cat of mc.categories || []) {
+      const top = cat.top_skill
+      if (!top) continue
+      parts.push(`${cat.category_name}: ${top.skill_name}`)
+      if (parts.length >= 2) return parts.join(' · ')
+    }
+  }
+  return parts.join(' · ')
+}
+
 /**
  * Gewichtetes Fähigkeiten-Profil (Phase 3) — Anzeige für Planungsartefakte.
  */
@@ -29,17 +111,13 @@ export default function SkillProfilePanel({
   loading = false,
   error = '',
   title = 'Fähigkeiten-Profil',
-  hint = 'Aus verknüpften Übungen berechnet (Dauer, Vorkommen, Intensität, Stufen von/bis).',
+  hint = 'Aus verknüpften Übungen berechnet (Dauer, Vorkommen, Intensität, Stufen von/bis). Trainingsgewicht ist über Programme vergleichbar; die Balken zeigen die Stärke relativ zur Bibliothek.',
   defaultExpanded = true,
 }) {
   const [expanded, setExpanded] = useState(defaultExpanded)
   const [slotOpenId, setSlotOpenId] = useState(null)
 
-  const skills = profile?.skills || []
-  const maxShare = useMemo(
-    () => Math.max(...skills.map((s) => s.share_percent || 0), 1),
-    [skills]
-  )
+  const badge = useMemo(() => topCategoryBadge(profile), [profile])
 
   if (loading) {
     return (
@@ -61,6 +139,11 @@ export default function SkillProfilePanel({
     !profile ||
     (profile.exercise_occurrence_count === 0 && profile.distinct_exercise_count === 0)
 
+  const categoryCount = (profile?.by_main_category || []).reduce(
+    (n, mc) => n + (mc.categories?.length || 0),
+    0
+  )
+
   return (
     <div className="card skill-profile">
       <button
@@ -70,10 +153,8 @@ export default function SkillProfilePanel({
         aria-expanded={expanded}
       >
         <span className="skill-profile__toggle-title">{title}</span>
-        {!noData && skills.length > 0 ? (
-          <span className="skill-profile__toggle-badge">
-            Top: {skills[0].skill_name} ({skills[0].share_percent}%)
-          </span>
+        {!noData && badge ? (
+          <span className="skill-profile__toggle-badge">{badge}</span>
         ) : null}
         <span className="skill-profile__toggle-icon" aria-hidden="true">
           {expanded ? '▾' : '▸'}
@@ -101,31 +182,20 @@ export default function SkillProfilePanel({
                   <strong>{profile.distinct_exercise_count}</strong> Übungen
                 </span>
                 <span>
-                  <strong>{skills.length}</strong> Fähigkeiten
+                  <strong>{profile.skills?.length ?? 0}</strong> Fähigkeiten
                 </span>
                 <span>
-                  <strong>{profile.exercise_occurrence_count}</strong> Positionen
+                  <strong>{categoryCount}</strong> Kategorien
+                </span>
+                <span>
+                  <strong>{formatWeight(profile.total_score ?? profile.total_weight)}</strong> Gesamt-Gewicht
                 </span>
               </div>
 
-              <ul className="skill-profile__list" aria-label="Fähigkeiten nach Gewicht">
-                {skills.slice(0, 12).map((sk) => (
-                  <SkillBar key={sk.skill_id} skill={sk} maxShare={maxShare} />
-                ))}
-              </ul>
-
-              {profile.by_category?.length > 1 ? (
-                <div className="skill-profile__categories">
-                  <span className="skill-profile__categories-label">Nach Kategorie</span>
-                  <div className="skill-profile__category-chips">
-                    {profile.by_category.slice(0, 6).map((c) => (
-                      <span key={c.category} className="skill-profile__category-chip">
-                        {c.category} {c.share_percent}%
-                      </span>
-                    ))}
-                  </div>
-                </div>
-              ) : null}
+              <CategoryGroupedProfile
+                profile={profile}
+                ariaLabel="Top-Fähigkeit je Kategorie"
+              />
             </>
           )}
 
@@ -135,7 +205,7 @@ export default function SkillProfilePanel({
               <ul className="skill-profile__slot-list">
                 {slots.map((sl) => {
                   const open = slotOpenId === sl.slot_id
-                  const top = sl.profile?.skills?.[0]
+                  const slotBadge = topCategoryBadge(sl.profile)
                   return (
                     <li key={sl.slot_id} className="skill-profile__slot-item">
                       <button
@@ -147,29 +217,19 @@ export default function SkillProfilePanel({
                         <span>
                           {(sl.slot_title || '').trim() || `Session ${(sl.sort_order ?? 0) + 1}`}
                         </span>
-                        {top ? (
-                          <span className="skill-profile__slot-top">
-                            {top.skill_name} {top.share_percent}%
-                          </span>
+                        {slotBadge ? (
+                          <span className="skill-profile__slot-top">{slotBadge}</span>
                         ) : (
                           <span className="skill-profile__slot-top skill-profile__slot-top--muted">
                             {sl.exercise_occurrence_count > 0 ? 'ohne Fähigkeiten' : 'kein Ablauf'}
                           </span>
                         )}
                       </button>
-                      {open && sl.profile?.skills?.length > 0 ? (
-                        <ul className="skill-profile__list skill-profile__list--nested">
-                          {sl.profile.skills.slice(0, 6).map((sk) => (
-                            <SkillBar
-                              key={sk.skill_id}
-                              skill={sk}
-                              maxShare={Math.max(
-                                ...sl.profile.skills.map((x) => x.share_percent || 0),
-                                1
-                              )}
-                            />
-                          ))}
-                        </ul>
+                      {open && sl.profile?.by_main_category?.length > 0 ? (
+                        <CategoryGroupedProfile
+                          profile={sl.profile}
+                          ariaLabel={`Fähigkeiten Session ${(sl.sort_order ?? 0) + 1}`}
+                        />
                       ) : null}
                     </li>
                   )

frontend/src/pages/TrainingFrameworkProgramEditPage.jsx

@@ -975,7 +975,7 @@ export default function TrainingFrameworkProgramEditPage() {
         {!isNew ? (
           <SkillProfilePanel
             title="Fähigkeiten-Schwerpunkte (aus Übungen)"
-            hint="Gewichtung nach geplanter Dauer, Häufigkeit, Intensität (niedrig/mittel/hoch) und Stufen-Spanne (von/bis). Vergleichbar mit manuell gesetzten Fokusbereichen in den Stammdaten."
+            hint="Trainingsgewicht aus Dauer, Häufigkeit, Intensität und Stufen-Spanne. Pro Kategorie die stärkste Fähigkeit — Balken relativ zur Bibliothek, nicht 100 % innerhalb des Plans."
             profile={skillProfileData?.overall}
             slots={skillProfileData?.slots}
             loading={skillProfileLoading}