Implement Roadmap Review Features and Enhance Progression Path Management

- Added support for editable major steps in the roadmap, allowing users to modify phase, learning goals, and order before exercise matching.
- Introduced a new `roadmap_override` feature to facilitate customized retrieval without re-invoking the roadmap AI.
- Updated the `ExerciseProgressionPathBuilder` component to incorporate these new features, enhancing user interaction and flexibility.
- Incremented application version to 0.8.207 to reflect these changes.

.claude/docs/working/PLANNING_PROGRESSION_ROADMAP_SPEC.md

@@ -188,7 +188,7 @@ Jede Phase: `(ctx) → ctx`, Zwischenergebnisse in API-Response für **Human-in-
 | **F1** | `include_roadmap_preview` in API + deterministische A/B | 🔄 0.8.204 |
 | **F2** | LLM Phase A/B/C über `ai_prompts` (078/079), `include_llm_roadmap` | 🔄 0.8.205 |
 | **F3** | Retrieval aus `stage_specs` (roadmap_first) | ✅ 0.8.206 |
-| **F4** | UI Roadmap-Review | 🔲 |
+| **F4** | UI Roadmap-Review | ✅ 0.8.207 |
 | **F5** | Trainingsplanung: eigene Pipeline + ggf. Workflow-Engine | 🔲 |
 
 ---

backend/planning_exercise_path_builder.py

@@ -53,10 +53,12 @@ from planning_exercise_suggest import (
 from planning_progression_roadmap import (
     MajorStep,
     ProgressionRoadmapContext,
+    RoadmapOverridePayload,
     StageSpecArtifact,
     build_roadmap_unfilled_gap_specs,
     progression_roadmap_to_api_dict,
     resolve_step_exercise_kind_filter,
+    roadmap_context_from_override,
     run_progression_roadmap_pipeline,
     stage_spec_retrieval_query,
 )
@@ -74,6 +76,8 @@ class ProgressionPathSuggestRequest(BaseModel):
     include_roadmap_preview: bool = False
     include_llm_roadmap: bool = True
     roadmap_first: bool = False
+    roadmap_only: bool = False
+    roadmap_override: Optional[RoadmapOverridePayload] = None
     progression_graph_id: Optional[int] = Field(default=None, ge=1)
     exercise_kind_any: Optional[List[str]] = None
 
@@ -492,21 +496,29 @@ def suggest_progression_path(
             cur, goal_query, semantic_brief
         )
 
-    path_target_profile, first_intent_summary, path_intent = _build_path_target_profile(
-        cur,
-        goal_query=goal_query,
-        semantic_brief=semantic_brief,
-        include_llm_intent=body.include_llm_intent,
-    )
-
     roadmap_first = bool(body.roadmap_first)
-    include_roadmap = roadmap_first or body.include_roadmap_preview
+    roadmap_only = bool(body.roadmap_only)
+    include_roadmap = roadmap_first or body.include_roadmap_preview or roadmap_only
     progression_roadmap: Optional[Dict[str, Any]] = None
     roadmap_ctx: Optional[ProgressionRoadmapContext] = None
-    roadmap_unfilled: List[Tuple[int, StageSpecArtifact]] = []
-    roadmap_gap_offers: List[Dict[str, Any]] = []
+    roadmap_edited = False
 
-    if include_roadmap:
+    if body.roadmap_override is not None:
+        try:
+            roadmap_ctx = roadmap_context_from_override(
+                goal_query,
+                max_steps=max_steps,
+                semantic_brief=semantic_brief,
+                override=body.roadmap_override,
+            )
+        except ValueError as exc:
+            raise HTTPException(status_code=400, detail=str(exc)) from exc
+        progression_roadmap = progression_roadmap_to_api_dict(roadmap_ctx)
+        progression_roadmap["roadmap_edited"] = True
+        roadmap_edited = True
+        max_steps = int(roadmap_ctx.max_steps)
+        roadmap_first = True
+    elif include_roadmap:
         roadmap_ctx = run_progression_roadmap_pipeline(
             goal_query,
             max_steps=max_steps,
@@ -516,6 +528,37 @@ def suggest_progression_path(
         )
         progression_roadmap = progression_roadmap_to_api_dict(roadmap_ctx)
 
+    if roadmap_only:
+        return {
+            "goal_query": goal_query,
+            "max_steps_requested": max_steps,
+            "steps": [],
+            "step_count": 0,
+            "target_profile_summary": None,
+            "semantic_brief_summary": brief_to_summary_dict(semantic_brief),
+            "semantic_llm_applied": semantic_llm_applied,
+            "query_intent_summary": {},
+            "progression_graph_id": body.progression_graph_id,
+            "path_qa": None,
+            "gap_fill_offers": [],
+            "progression_roadmap": progression_roadmap,
+            "roadmap_first": False,
+            "roadmap_only": True,
+            "roadmap_edited": roadmap_edited,
+            "roadmap_unfilled_count": 0,
+            "retrieval_phase": "roadmap_only",
+        }
+
+    path_target_profile, first_intent_summary, path_intent = _build_path_target_profile(
+        cur,
+        goal_query=goal_query,
+        semantic_brief=semantic_brief,
+        include_llm_intent=body.include_llm_intent,
+    )
+
+    roadmap_unfilled: List[Tuple[int, StageSpecArtifact]] = []
+    roadmap_gap_offers: List[Dict[str, Any]] = []
+
     used: Set[int] = set()
     steps: List[Dict[str, Any]] = []
     planned_ids: List[int] = []
@@ -721,6 +764,8 @@ def suggest_progression_path(
         retrieval_parts.append("gap_fill_offers")
     if include_roadmap:
         retrieval_parts.append("roadmap_preview")
+    if roadmap_edited:
+        retrieval_parts.append("roadmap_edited")
     if roadmap_unfilled:
         retrieval_parts.append("roadmap_unfilled")
 
@@ -738,6 +783,8 @@ def suggest_progression_path(
         "gap_fill_offers": gap_fill_offers,
         "progression_roadmap": progression_roadmap,
         "roadmap_first": roadmap_first,
+        "roadmap_only": False,
+        "roadmap_edited": roadmap_edited,
         "roadmap_unfilled_count": len(roadmap_unfilled),
         "retrieval_phase": "+".join(retrieval_parts),
     }

backend/planning_progression_roadmap.py

@@ -109,6 +109,13 @@ class StageSpecArtifact(BaseModel):
     anti_patterns: List[str] = Field(default_factory=list)
 
 
+class RoadmapOverridePayload(BaseModel):
+    """Vom Trainer bearbeitete Major Steps — steuert Retrieval ohne erneute Roadmap-KI."""
+
+    major_steps: List[MajorStep] = Field(..., min_length=2, max_length=10)
+    stage_specs: Optional[List[StageSpecArtifact]] = None
+
+
 class ProgressionRoadmapContext(BaseModel):
     goal_query: str
     max_steps: int = Field(ge=2, le=10, default=5)
@@ -551,6 +558,80 @@ def build_stage_specs(
     return specs
 
 
+def normalize_major_steps_for_override(
+    major_steps: Sequence[MajorStep],
+    *,
+    max_steps: int,
+) -> List[MajorStep]:
+    """Indizes 0…n-1, mindestens 2, höchstens max_steps Major Steps."""
+    cleaned: List[MajorStep] = []
+    for raw in list(major_steps)[:max_steps]:
+        goal = (raw.learning_goal or "").strip()
+        phase = (raw.phase or "vertiefung").strip().lower()
+        if not goal:
+            continue
+        cleaned.append(
+            MajorStep(
+                index=len(cleaned),
+                phase=phase,
+                learning_goal=goal,
+                consolidates=list(raw.consolidates or []),
+                rationale=(raw.rationale or "").strip(),
+            )
+        )
+    if len(cleaned) < 2:
+        raise ValueError("Mindestens zwei Major Steps mit Lernziel nötig")
+    for i, step in enumerate(cleaned):
+        step.index = i
+    return cleaned
+
+
+def roadmap_context_from_override(
+    goal_query: str,
+    *,
+    max_steps: int,
+    semantic_brief: PlanningSemanticBrief,
+    override: RoadmapOverridePayload,
+) -> ProgressionRoadmapContext:
+    """Phase F4: bearbeitete Roadmap → stage_specs → Retrieval (ohne LLM-Roadmap)."""
+    majors = normalize_major_steps_for_override(override.major_steps, max_steps=max_steps)
+    effective_max = len(majors)
+    goal_analysis = build_goal_analysis(goal_query, semantic_brief)
+    stage_specs: List[StageSpecArtifact]
+    if override.stage_specs and len(override.stage_specs) >= effective_max:
+        stage_specs = []
+        for i, spec in enumerate(override.stage_specs[:effective_max]):
+            stage_specs.append(
+                StageSpecArtifact(
+                    major_step_index=i,
+                    learning_goal=(spec.learning_goal or majors[i].learning_goal).strip(),
+                    load_profile=list(spec.load_profile or []),
+                    exercise_type=(spec.exercise_type or "").strip(),
+                    success_criteria=list(spec.success_criteria or []),
+                    anti_patterns=list(spec.anti_patterns or []),
+                )
+            )
+        if not all(s.exercise_type for s in stage_specs):
+            rebuilt = build_stage_specs(majors, goal_analysis=goal_analysis)
+            for i, spec in enumerate(stage_specs):
+                if not spec.exercise_type:
+                    spec.exercise_type = rebuilt[i].exercise_type
+                if not spec.load_profile:
+                    spec.load_profile = list(rebuilt[i].load_profile)
+    else:
+        stage_specs = build_stage_specs(majors, goal_analysis=goal_analysis)
+
+    return ProgressionRoadmapContext(
+        goal_query=goal_query.strip(),
+        max_steps=effective_max,
+        semantic_brief=brief_to_summary_dict(semantic_brief),
+        goal_analysis=goal_analysis,
+        roadmap=RoadmapArtifact(major_steps=majors),
+        stage_specs=stage_specs,
+        pipeline_phase="roadmap_v1_edited",
+    )
+
+
 def run_progression_roadmap_pipeline(
     goal_query: str,
     *,
@@ -652,6 +733,9 @@ __all__ = [
     "MicroObjective",
     "ProgressionRoadmapContext",
     "RoadmapArtifact",
+    "RoadmapOverridePayload",
+    "normalize_major_steps_for_override",
+    "roadmap_context_from_override",
     "StageSpecArtifact",
     "build_goal_analysis",
     "build_roadmap_unfilled_gap_specs",

backend/routers/planning_exercise_suggest.py

@@ -71,6 +71,7 @@ def post_progression_path_suggest(
         body.include_llm_intent
         or body.include_llm_path_qa
         or body.include_ai_gap_fill
+        or body.include_llm_roadmap
     )
     club_id = resolve_club_id_for_probe(tenant) if uses_ai else None
     if uses_ai:

backend/tests/test_planning_progression_roadmap.py

@@ -14,6 +14,9 @@ from planning_progression_roadmap import (
     run_progression_roadmap_pipeline,
     stage_spec_exercise_kind_filter,
     stage_spec_retrieval_query,
+    normalize_major_steps_for_override,
+    roadmap_context_from_override,
+    RoadmapOverridePayload,
 )
 from planning_exercise_semantics import build_semantic_brief
 
@@ -90,6 +93,40 @@ def test_build_roadmap_unfilled_gap_specs():
     assert specs[0]["phase"] == "anwendung"
 
 
+def test_normalize_major_steps_reindexes():
+    majors = normalize_major_steps_for_override(
+        [
+            MajorStep(index=9, phase="einstieg", learning_goal="Einstieg", consolidates=[]),
+            MajorStep(index=8, phase="perfektion", learning_goal="Ziel", consolidates=[]),
+        ],
+        max_steps=5,
+    )
+    assert len(majors) == 2
+    assert majors[0].index == 0
+    assert majors[1].index == 1
+
+
+def test_roadmap_context_from_override():
+    brief = build_semantic_brief("Mae Geri Perfektion")
+    override = RoadmapOverridePayload(
+        major_steps=[
+            MajorStep(index=0, phase="einstieg", learning_goal="Mae Geri Einstieg", consolidates=[]),
+            MajorStep(index=1, phase="grundlage", learning_goal="Stand und Hüfte", consolidates=[]),
+            MajorStep(index=2, phase="perfektion", learning_goal="Präzision unter Belastung", consolidates=[]),
+        ]
+    )
+    ctx = roadmap_context_from_override(
+        "Mae Geri Perfektion",
+        max_steps=5,
+        semantic_brief=brief,
+        override=override,
+    )
+    assert ctx.pipeline_phase == "roadmap_v1_edited"
+    assert len(ctx.roadmap.major_steps) == 3
+    assert len(ctx.stage_specs) == 3
+    assert ctx.stage_specs[1].learning_goal == "Stand und Hüfte"
+
+
 def test_api_dict_exposes_prompt_slug_catalog():
     ctx = run_progression_roadmap_pipeline("Mae Geri", max_steps=3, include_llm_roadmap=False)
     api = progression_roadmap_to_api_dict(ctx)

backend/version.py

@@ -1,6 +1,6 @@
 # Shinkan Jinkendo Version Information
 
-APP_VERSION = "0.8.206"
+APP_VERSION = "0.8.207"
 BUILD_DATE = "2026-06-07"
 DB_SCHEMA_VERSION = "20260606086"
 
@@ -38,7 +38,7 @@ MODULE_VERSIONS = {
     "skill_profiles": "1.0.0",  # Phase 3: gewichtetes Fähigkeiten-Profil + skill-discovery/suggestions
     "methods": "0.1.0",
     "exercises": "2.37.1",  # KI-Endpoints: feature_usage nach ai_calls consume
-    "planning_exercise_suggest": "0.18.0",  # F3: roadmap_first Retrieval pro stage_spec
+    "planning_exercise_suggest": "0.19.0",  # F4: roadmap_only + roadmap_override, editierbare Roadmap-UI
     "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
@@ -53,6 +53,15 @@ MODULE_VERSIONS = {
 }
 
 CHANGELOG = [
+    {
+        "version": "0.8.207",
+        "date": "2026-06-07",
+        "changes": [
+            "Phase F4: Roadmap-Review — roadmap_only, roadmap_override auf progression-path-suggest.",
+            "UI: Major Steps editierbar (Phase, Lernziel, Reihenfolge) vor Übungs-Match.",
+            "Zwei-Schritt-Flow: Roadmap vorschlagen → Übungen matchen.",
+        ],
+    },
     {
         "version": "0.8.206",
         "date": "2026-06-07",

docs/HANDOVER.md

@@ -1,7 +1,7 @@
 # Shinkan Jinkendo – Entwicklungsstand & Handover
 
 **Stand:** 2026-06-07  
-**App-Version / DB-Schema:** App **`0.8.206`** (Planungs-KI Phase F3); DB **`20260606086`** — maßgeblich **`backend/version.py`**.
+**App-Version / DB-Schema:** App **`0.8.207`** (Planungs-KI Phase F4); DB **`20260606086`** — maßgeblich **`backend/version.py`**.
 
 Diese Datei ist die **Einstiegs-Doku für neue Chat-Sessions**: Anforderungen im Detail stehen in `.claude/docs/` (siehe unten); hier der **implementierte Stand**, **Medien-Meilenstein** und **sinnvolle nächste Schritte**.
 
@@ -109,6 +109,7 @@ Das Schema ist gegenüber dem Code zurück: Migration **`022_skills_schema_compl
 | **E3** | `gap_fill_offers`, Off-Topic-Strip, voller KI-Call bei Lücken | ✅ **0.8.203** |
 | **F0–F2** | Roadmap-Pipeline + LLM-Prompts (078/079) | ✅ **0.8.205** |
 | **F3** | `roadmap_first` — Retrieval pro `stage_spec` | ✅ **0.8.206** |
+| **F4** | Roadmap-Review UI + `roadmap_override` | ✅ **0.8.207** |
 | **D** | `planning_context` an `suggestExerciseAi` (Neu-Anlage) | 🔲 |
 
 **Architektur-Entscheidung (2026-06-07):** Progressionsgraph = **Roadmap-first** (Ziel → Major Steps → Übungs-Match). **Keine Gruppenanalyse** im Graphen. Mitai Workflow-Engine **später** — jetzt `planning_progression_roadmap.py`. Spec: **`.claude/docs/working/PLANNING_PROGRESSION_ROADMAP_SPEC.md`** · Roadmap: **`docs/architecture/PLANNING_KI_ROADMAP.md`**

docs/architecture/PLANNING_KI_ROADMAP.md

@@ -64,10 +64,11 @@ Diese Roadmap ergänzt die **Architektur-Refaktor-Roadmap** (`UMSETZUNGSPLAN_ROA
 - [x] Gap-Angebote für unbesetzte Roadmap-Stufen (`roadmap_unfilled`)
 - [ ] QA/Lücken vollständig an Roadmap koppeln (Brücken optional reduzieren)
 
-### F4 — UI
+### F4 — UI (0.8.207)
 
-- [ ] Roadmap-Review im `ExerciseProgressionPathBuilder`
-- [ ] Major Steps editierbar vor Übungs-Match
+- [x] Roadmap-Review im `ExerciseProgressionPathBuilder`
+- [x] Major Steps editierbar (Phase, Lernziel, Reihenfolge) vor Übungs-Match
+- [x] API `roadmap_only` + `roadmap_override`
 
 ---
 

frontend/src/components/ExerciseProgressionPathBuilder.jsx

@@ -35,6 +35,10 @@ function mapApiStepToRow(step) {
     aiSuggestion: step?.ai_suggestion || null,
     semanticScore: step?.semantic_score,
     isOffTopic: false,
+    roadmapMajorStepIndex:
+      step?.roadmap_major_step_index != null ? Number(step.roadmap_major_step_index) : null,
+    roadmapPhase: step?.roadmap_phase || null,
+    roadmapLearningGoal: step?.roadmap_learning_goal || null,
   }
 }
 
@@ -63,6 +67,36 @@ const OFFER_SOURCE_LABELS = {
 
 const PATH_STEPS_HARD_MAX = 10
 
+const ROADMAP_PHASES = ['einstieg', 'grundlage', 'vertiefung', 'anwendung', 'perfektion']
+
+function mapMajorStepsFromApi(apiRoadmap) {
+  const raw = apiRoadmap?.roadmap?.major_steps
+  if (!Array.isArray(raw)) return []
+  return raw.map((s, i) => ({
+    index: i,
+    phase: s.phase || 'vertiefung',
+    learning_goal: (s.learning_goal || '').trim(),
+    consolidates: Array.isArray(s.consolidates) ? s.consolidates : [],
+    rationale: s.rationale || '',
+  }))
+}
+
+function reindexMajorSteps(rows) {
+  return rows.map((row, i) => ({ ...row, index: i }))
+}
+
+function majorStepsToOverridePayload(rows) {
+  return {
+    major_steps: reindexMajorSteps(rows).map((row) => ({
+      index: row.index,
+      phase: row.phase || 'vertiefung',
+      learning_goal: row.learning_goal.trim(),
+      consolidates: row.consolidates || [],
+      rationale: row.rationale || '',
+    })),
+  }
+}
+
 /** Einfügen wächst den Pfad; Ersetzen (replace_step_index) nicht. */
 function offerGrowsPath(offer) {
   const replaceIdx = offer?.replace_step_index
@@ -121,7 +155,6 @@ export default function ExerciseProgressionPathBuilder({
   const [goalQuery, setGoalQuery] = useState('')
   const [maxSteps, setMaxSteps] = useState(5)
   const [segmentNotes, setSegmentNotes] = useState('')
-  const [loading, setLoading] = useState(false)
   const [saving, setSaving] = useState(false)
   const [error, setError] = useState('')
   const [targetSummary, setTargetSummary] = useState(null)
@@ -130,6 +163,11 @@ export default function ExerciseProgressionPathBuilder({
   const [pathSteps, setPathSteps] = useState([])
   const [gapFillOffers, setGapFillOffers] = useState([])
   const [progressionRoadmap, setProgressionRoadmap] = useState(null)
+  const [editableMajorSteps, setEditableMajorSteps] = useState([])
+  const [roadmapDirty, setRoadmapDirty] = useState(false)
+  const [loadingRoadmap, setLoadingRoadmap] = useState(false)
+  const [loadingMatch, setLoadingMatch] = useState(false)
+  const loading = loadingRoadmap || loadingMatch
   const [focusAreas, setFocusAreas] = useState([])
   const [skillsCatalog, setSkillsCatalog] = useState([])
   const [generatingOfferId, setGeneratingOfferId] = useState(null)
@@ -173,6 +211,52 @@ export default function ExerciseProgressionPathBuilder({
     setPathSteps((prev) => (prev.length <= 2 ? prev : prev.filter((_, i) => i !== idx)))
   }, [])
 
+  const patchMajorStep = useCallback((idx, patch) => {
+    setEditableMajorSteps((prev) =>
+      reindexMajorSteps(prev.map((row, i) => (i === idx ? { ...row, ...patch } : row))),
+    )
+    setRoadmapDirty(true)
+  }, [])
+
+  const moveMajorStep = useCallback((idx, dir) => {
+    setEditableMajorSteps((prev) => {
+      const j = idx + dir
+      if (j < 0 || j >= prev.length) return prev
+      const next = [...prev]
+      const t = next[idx]
+      next[idx] = next[j]
+      next[j] = t
+      return reindexMajorSteps(next)
+    })
+    setRoadmapDirty(true)
+  }, [])
+
+  const removeMajorStep = useCallback((idx) => {
+    setEditableMajorSteps((prev) => {
+      if (prev.length <= 2) return prev
+      return reindexMajorSteps(prev.filter((_, i) => i !== idx))
+    })
+    setRoadmapDirty(true)
+  }, [])
+
+  const addMajorStep = useCallback(() => {
+    setEditableMajorSteps((prev) => {
+      if (prev.length >= PATH_STEPS_HARD_MAX) return prev
+      const phase = ROADMAP_PHASES[Math.min(prev.length, ROADMAP_PHASES.length - 1)]
+      return reindexMajorSteps([
+        ...prev,
+        {
+          index: prev.length,
+          phase,
+          learning_goal: '',
+          consolidates: [],
+          rationale: '',
+        },
+      ])
+    })
+    setRoadmapDirty(true)
+  }, [])
+
   const moveStep = useCallback((idx, dir) => {
     setPathSteps((prev) => {
       const j = idx + dir
@@ -376,7 +460,33 @@ export default function ExerciseProgressionPathBuilder({
     }
   }
 
-  const suggestPath = async () => {
+  const applyPathMatchResponse = (res, q) => {
+    const qa = res?.path_qa || null
+    const rawRows = (Array.isArray(res?.steps) ? res.steps : []).map(mapApiStepToRow)
+    const rows =
+      Array.isArray(qa?.stripped_off_topic_steps) && qa.stripped_off_topic_steps.length > 0
+        ? rawRows
+        : applyOffTopicFlags(rawRows, qa)
+    if (rows.length < 2) {
+      throw new Error('Zu wenig Schritte im Vorschlag.')
+    }
+    setPathSteps(rows)
+    setTargetSummary(res?.target_profile_summary || null)
+    setSemanticBrief(res?.semantic_brief_summary || null)
+    setPathQa(qa)
+    setGapFillOffers(
+      Array.isArray(res?.gap_fill_offers)
+        ? res.gap_fill_offers
+        : Array.isArray(qa?.gap_fill_offers)
+          ? qa.gap_fill_offers
+          : [],
+    )
+    setProgressionRoadmap(res?.progression_roadmap || null)
+    setRoadmapDirty(false)
+    if (!segmentNotes.trim() && q) setSegmentNotes(q.slice(0, 400))
+  }
+
+  const suggestRoadmap = async () => {
     const q = (goalQuery || '').trim()
     if (q.length < 3) {
       alert('Ziel-Anfrage: mindestens 3 Zeichen.')
@@ -386,55 +496,85 @@ export default function ExerciseProgressionPathBuilder({
       alert('Zuerst einen Graphen wählen.')
       return
     }
-    setLoading(true)
+    setLoadingRoadmap(true)
     setError('')
     try {
       const res = await api.suggestProgressionPath({
         query: q,
         max_steps: Number(maxSteps),
+        include_llm_intent: false,
+        include_path_qa: false,
+        include_llm_path_qa: false,
+        include_path_reorder: false,
+        include_ai_gap_fill: false,
+        include_roadmap_preview: true,
+        include_llm_roadmap: true,
+        roadmap_only: true,
+        progression_graph_id: Number(graphId),
+      })
+      const majors = mapMajorStepsFromApi(res?.progression_roadmap)
+      if (majors.length < 2) {
+        throw new Error('Roadmap hat zu wenig Major Steps.')
+      }
+      setEditableMajorSteps(majors)
+      setMaxSteps(majors.length)
+      setProgressionRoadmap(res?.progression_roadmap || null)
+      setSemanticBrief(res?.semantic_brief_summary || null)
+      setPathSteps([])
+      setTargetSummary(null)
+      setPathQa(null)
+      setGapFillOffers([])
+      setRoadmapDirty(false)
+    } catch (e) {
+      console.error(e)
+      setError(e.message || 'Roadmap-Vorschlag fehlgeschlagen')
+      setEditableMajorSteps([])
+      setProgressionRoadmap(null)
+    } finally {
+      setLoadingRoadmap(false)
+    }
+  }
+
+  const matchExercisesFromRoadmap = async () => {
+    const q = (goalQuery || '').trim()
+    if (q.length < 3) {
+      alert('Ziel-Anfrage: mindestens 3 Zeichen.')
+      return
+    }
+    if (!graphId) {
+      alert('Zuerst einen Graphen wählen.')
+      return
+    }
+    const validSteps = editableMajorSteps.filter((s) => (s.learning_goal || '').trim().length >= 3)
+    if (validSteps.length < 2) {
+      alert('Mindestens zwei Major Steps mit Lernziel (je 3+ Zeichen) nötig.')
+      return
+    }
+    setLoadingMatch(true)
+    setError('')
+    try {
+      const override = majorStepsToOverridePayload(validSteps)
+      const res = await api.suggestProgressionPath({
+        query: q,
+        max_steps: validSteps.length,
         include_llm_intent: true,
         include_path_qa: true,
         include_llm_path_qa: true,
         include_path_reorder: true,
         include_ai_gap_fill: true,
         include_roadmap_preview: true,
-        include_llm_roadmap: true,
+        include_llm_roadmap: false,
         roadmap_first: true,
+        roadmap_override: override,
         progression_graph_id: Number(graphId),
       })
-      const qa = res?.path_qa || null
-      const rawRows = (Array.isArray(res?.steps) ? res.steps : []).map(mapApiStepToRow)
-      const rows =
-        Array.isArray(qa?.stripped_off_topic_steps) && qa.stripped_off_topic_steps.length > 0
-          ? rawRows
-          : applyOffTopicFlags(rawRows, qa)
-      if (rows.length < 2) {
-        throw new Error('Zu wenig Schritte im Vorschlag.')
-      }
-      setPathSteps(rows)
-      setTargetSummary(res?.target_profile_summary || null)
-      setSemanticBrief(res?.semantic_brief_summary || null)
-      setPathQa(qa)
-      setGapFillOffers(
-        Array.isArray(res?.gap_fill_offers)
-          ? res.gap_fill_offers
-          : Array.isArray(qa?.gap_fill_offers)
-            ? qa.gap_fill_offers
-            : [],
-      )
-      setProgressionRoadmap(res?.progression_roadmap || null)
-      if (!segmentNotes.trim() && q) setSegmentNotes(q.slice(0, 400))
+      applyPathMatchResponse(res, q)
+      setMaxSteps(validSteps.length)
     } catch (e) {
       console.error(e)
-      setError(e.message || 'Pfad-Vorschlag fehlgeschlagen')
-      setPathSteps([])
-      setTargetSummary(null)
-      setSemanticBrief(null)
-      setPathQa(null)
-      setGapFillOffers([])
-      setProgressionRoadmap(null)
+      setError(e.message || 'Übungs-Match fehlgeschlagen')
     } finally {
-      setLoading(false)
+      setLoadingMatch(false)
     }
   }
 
@@ -477,6 +617,8 @@ export default function ExerciseProgressionPathBuilder({
       setPathQa(null)
       setGapFillOffers([])
       setProgressionRoadmap(null)
+      setEditableMajorSteps([])
+      setRoadmapDirty(false)
       if (typeof onSaved === 'function') await onSaved()
       const msg =
         skippedAi > 0
@@ -501,8 +643,8 @@ export default function ExerciseProgressionPathBuilder({
     >
       <h3 style={{ marginTop: 0, fontSize: '1rem' }}>KI: Pfad zum Ziel</h3>
       <p style={{ fontSize: '12px', color: 'var(--text3)', marginTop: 0, lineHeight: 1.45 }}>
-        Ziel in Freitext formulieren — die Planungs-KI schlägt eine semantisch passende, aufbauende Reihenfolge vor,
-        prüft Lücken (ggf. Brücken-Übungen) und optional per LLM-QS. Fehlende Schritte können mit KI als Übung angelegt werden.
+        Zuerst didaktische Roadmap vorschlagen und anpassen, dann Übungen je Major Step aus der Bibliothek matchen.
+        Lücken können mit KI als Übung angelegt werden.
       </p>
       <div style={{ display: 'flex', flexWrap: 'wrap', gap: '10px', alignItems: 'flex-end' }}>
         <div className="form-row" style={{ flex: '2 1 240px', marginBottom: 0 }}>
@@ -531,9 +673,30 @@ export default function ExerciseProgressionPathBuilder({
           type="button"
           className="btn btn-primary"
           disabled={disabled || loading || saving || !graphId}
-          onClick={suggestPath}
+          onClick={suggestRoadmap}
         >
-          {loading ? 'Vorschlag …' : 'Pfad vorschlagen'}
+          {loadingRoadmap ? 'Roadmap …' : 'Roadmap vorschlagen'}
+        </button>
+        <button
+          type="button"
+          className="btn btn-secondary"
+          disabled={
+            disabled ||
+            loading ||
+            saving ||
+            !graphId ||
+            editableMajorSteps.length < 2
+          }
+          onClick={matchExercisesFromRoadmap}
+          title={
+            editableMajorSteps.length < 2
+              ? 'Zuerst Roadmap vorschlagen'
+              : roadmapDirty
+                ? 'Roadmap wurde bearbeitet — erneut matchen'
+                : 'Bibliothek je Major Step durchsuchen'
+          }
+        >
+          {loadingMatch ? 'Match …' : roadmapDirty ? 'Übungen neu matchen' : 'Übungen matchen'}
         </button>
       </div>
 
@@ -565,7 +728,7 @@ export default function ExerciseProgressionPathBuilder({
         </div>
       ) : null}
 
-      {progressionRoadmap?.roadmap?.major_steps?.length > 0 ? (
+      {editableMajorSteps.length > 0 ? (
         <div
           style={{
             marginTop: '12px',
@@ -575,25 +738,113 @@ export default function ExerciseProgressionPathBuilder({
             background: 'color-mix(in srgb, var(--accent) 6%, var(--surface))',
           }}
         >
-          <strong style={{ fontSize: '13px' }}>Didaktische Roadmap (Phase F)</strong>
-          <p style={{ fontSize: '12px', color: 'var(--text3)', margin: '6px 0 10px', lineHeight: 1.45 }}>
-            Ziel-zuerst-Planung: {progressionRoadmap.micro_objective_count ?? '?'} Zwischenziele →{' '}
-            {progressionRoadmap.major_step_count ?? progressionRoadmap.roadmap.major_steps.length} Major Steps.
-            {progressionRoadmap.llm_roadmap_applied
-              ? ' (KI-Prompts aus Admin-Konfiguration)'
-              : ' (heuristischer Fallback — KI-Prompts in ai_prompts)'}
-            . Übungen unten: je Major Step aus der Bibliothek (roadmap-first).
+          <div style={{ display: 'flex', flexWrap: 'wrap', gap: '8px', alignItems: 'center', marginBottom: '8px' }}>
+            <strong style={{ fontSize: '13px' }}>Didaktische Roadmap — bearbeiten</strong>
+            {roadmapDirty ? (
+              <span className="exercise-tag" style={{ borderColor: 'var(--danger)' }}>
+                Geändert — bitte erneut matchen
+              </span>
+            ) : pathSteps.length > 0 ? (
+              <span className="exercise-tag" style={{ borderColor: 'var(--accent)' }}>
+                Gematcht
+              </span>
+            ) : null}
+          </div>
+          <p style={{ fontSize: '12px', color: 'var(--text3)', margin: '0 0 10px', lineHeight: 1.45 }}>
+            {progressionRoadmap?.micro_objective_count != null
+              ? `${progressionRoadmap.micro_objective_count} Zwischenziele → `
+              : ''}
+            {editableMajorSteps.length} Major Steps
+            {progressionRoadmap?.llm_roadmap_applied
+              ? ' (KI-Roadmap)'
+              : progressionRoadmap
+                ? ' (heuristisch/KI)'
+                : ''}
+            . Phasen und Lernziele anpassen, dann „Übungen matchen“.
           </p>
-          <ol style={{ margin: 0, paddingLeft: '18px', fontSize: '13px', lineHeight: 1.5 }}>
-            {progressionRoadmap.roadmap.major_steps.map((step) => (
-              <li key={step.index} style={{ marginBottom: '6px' }}>
-                <span className="exercise-tag" style={{ marginRight: '6px' }}>
-                  {step.phase}
-                </span>
-                {step.learning_goal}
-              </li>
+          <div style={{ display: 'flex', flexDirection: 'column', gap: '10px' }}>
+            {editableMajorSteps.map((step, idx) => (
+              <div
+                key={`major-${idx}-${step.index}`}
+                style={{
+                  padding: '10px 12px',
+                  borderRadius: '8px',
+                  background: 'var(--surface2)',
+                  border: '1px solid var(--border)',
+                  display: 'grid',
+                  gridTemplateColumns: 'repeat(auto-fit, minmax(160px, 1fr))',
+                  gap: '10px',
+                  alignItems: 'end',
+                }}
+              >
+                <div className="form-row" style={{ marginBottom: 0, flex: '0 0 120px' }}>
+                  <label className="form-label">Stufe {idx + 1} · Phase</label>
+                  <select
+                    className="form-input"
+                    value={step.phase}
+                    onChange={(e) => patchMajorStep(idx, { phase: e.target.value })}
+                    disabled={disabled || loading || saving}
+                  >
+                    {ROADMAP_PHASES.map((p) => (
+                      <option key={p} value={p}>
+                        {p}
+                      </option>
+                    ))}
+                  </select>
+                </div>
+                <div className="form-row" style={{ marginBottom: 0, flex: '2 1 240px' }}>
+                  <label className="form-label">Lernziel</label>
+                  <input
+                    className="form-input"
+                    value={step.learning_goal}
+                    onChange={(e) => patchMajorStep(idx, { learning_goal: e.target.value })}
+                    placeholder="z. B. Grundstellung und Hüftmobilität für Mae Geri"
+                    disabled={disabled || loading || saving}
+                  />
+                </div>
+                <div style={{ display: 'flex', gap: '6px', flexWrap: 'wrap' }}>
+                  <button
+                    type="button"
+                    className="btn"
+                    style={{ fontSize: '12px', padding: '4px 8px' }}
+                    onClick={() => moveMajorStep(idx, -1)}
+                    disabled={disabled || loading || saving || idx === 0}
+                  >
+                    ↑
+                  </button>
+                  <button
+                    type="button"
+                    className="btn"
+                    style={{ fontSize: '12px', padding: '4px 8px' }}
+                    onClick={() => moveMajorStep(idx, 1)}
+                    disabled={disabled || loading || saving || idx >= editableMajorSteps.length - 1}
+                  >
+                    ↓
+                  </button>
+                  <button
+                    type="button"
+                    className="btn"
+                    style={{ fontSize: '12px', padding: '4px 8px' }}
+                    onClick={() => removeMajorStep(idx)}
+                    disabled={disabled || loading || saving || editableMajorSteps.length <= 2}
+                  >
+                    Entfernen
+                  </button>
+                </div>
+              </div>
             ))}
-          </ol>
+          </div>
+          {editableMajorSteps.length < PATH_STEPS_HARD_MAX ? (
+            <button
+              type="button"
+              className="btn btn-secondary"
+              style={{ marginTop: '10px', fontSize: '12px' }}
+              onClick={addMajorStep}
+              disabled={disabled || loading || saving}
+            >
+              Major Step hinzufügen
+            </button>
+          ) : null}
         </div>
       ) : null}
 
@@ -748,11 +999,18 @@ export default function ExerciseProgressionPathBuilder({
                 <div className="form-row" style={{ marginBottom: 0 }}>
                   <label className="form-label">
                     Schritt {idx + 1}
+                    {step.roadmapMajorStepIndex != null
+                      ? ` · Roadmap ${step.roadmapMajorStepIndex + 1}`
+                      : ''}
+                    {step.roadmapPhase ? ` (${step.roadmapPhase})` : ''}
                     {step.isOffTopic ? ' (themenfremd)' : ''}
                     {step.isAiProposal ? ' (KI-Neu)' : step.isBridge ? ' (Brücke)' : ''}
-                    {!step.isAiProposal && !step.isOffTopic && idx === 0 ? ' (Einstieg)' : ''}
-                    {!step.isAiProposal && idx === pathSteps.length - 1 ? ' (Zielnähe)' : ''}
                   </label>
+                  {step.roadmapLearningGoal ? (
+                    <p style={{ fontSize: '11px', color: 'var(--text3)', margin: '4px 0 0' }}>
+                      Ziel: {step.roadmapLearningGoal}
+                    </p>
+                  ) : null}
                   <div style={{ fontSize: '13px' }}>
                     <strong>{step.exerciseTitle}</strong>
                     {step.exerciseId ? (