Implement Phase C3 Enhancements for Progression Path Suggestion

- Incremented version to 0.8.185, reflecting the implementation of Phase C3 features.
- Introduced the `POST /api/planning/progression-path-suggest` endpoint for generating exercise progression paths.
- Enhanced the ExerciseProgressionGraphPanel with a new ExerciseProgressionPathBuilder for reviewing and saving paths.
- Updated changelog to document the new capabilities in planning AI functionality.

.claude/docs/working/PLANNING_EXERCISE_SUGGEST_CONTEXT.md

@@ -2,7 +2,7 @@
 
 **Version:** 0.2  
 **Datum:** 2026-05-23  
-**Status:** P0–P2 ✅ · Phase A/B/B2 ✅ · **Phase C1 ✅** (Graph auto-match + variantenbewusste Nachfolger) · C2–C3 geplant  
+**Status:** P0–P2 ✅ · Phase A/B/B2 ✅ · **Phase C1–C3 ✅** (Progressionsgraph + Varianten + Pfad-Builder)  
 **Bezüge:** `AI_PLANNING_KI_MULTISTAGE_FORECAST.md` · `AI_PROMPT_TARGET_ARCHITECTURE.md` · `SKILL_SCORING_SPEC.md` · `TRAINING_FRAMEWORK_SPEC.md` §3 (Progressionsgraph)
 
 ---
@@ -190,7 +190,7 @@ Wenn `hits` leer oder Trainer wählt „Mit KI anlegen“:
 | **B** | Text-Signale guidance/Rahmen-Ziele | ✅ **0.8.181** |
 | **C1** | Graph auto-match + variantenbewusste Nachfolger | ✅ **0.8.183** |
 | **C2** | Varianten in Trefferliste / Picker | ✅ **0.8.184** |
-| **C3** | Graph-Builder (Ziel → Pfad → speichern) | 🔲 |
+| **C3** | Graph-Builder (Ziel → Pfad → speichern) | ✅ **0.8.185** |
 | **D** | Neu-Anlage: Pack an `suggestExerciseAi` | 🔲 |
 
 ---
@@ -211,7 +211,7 @@ Wenn `hits` leer oder Trainer wählt „Mit KI anlegen“:
 - **Ungespeicherte Plan-Änderungen:** ✅ Client übergibt `planned_exercise_ids[]` aus Formular (TrainingUnitEditPage).
 - **Progressionsgraph-ID:** ✅ Auto-Match vom Anker (**C1**); manuelle Auswahl in UI noch offen.
 - **Anker-Variante:** ✅ Client + DB (**C1**); Picker wählt Variante bei Treffer (**C2** — Dropdown + Graph-Vorschlag).
-- **Graph-Builder:** Ziel eingeben → aufbauende Übungen → in Graph speichern (**C3**) — Compound-Nutzen über viele Pläne.
+- **Graph-Builder (C3):** Ziel → Pfad vorschlagen → in Graph speichern — ✅ **0.8.185**
 - **Varianten-Suche:** Library-Picker nutzt `include_variants`; Planungs-KI rankt primär **Übungsebene** — Varianten-Expansion nur gezielt (**C2**).
 - **Enrichment:** Superadmin-Tool für Skills; Datenqualität der Bibliothek entscheidend für Profil-Score.
 - **LLM-Intent:** ✅ P1 Szenario-Pipeline + `planning_exercise_search_intent` (Migration 073).
@@ -427,6 +427,23 @@ Treffer: optional `hits[].suggested_variant_id`.
 
 ---
 
-## 21. Phase C3 — Graph-Builder (Roadmap, offen)
+## 21. Phase C3 — Graph-Builder (0.8.185) ✅
 
-Ziel eingeben → aufbauende Übungen vorschlagen → nach Review in Graph speichern (`POST …/edges/sequence`). Nutzen über viele Pläne hinweg.
+**API:** `POST /api/planning/progression-path-suggest`
+
+| Feld | Bedeutung |
+|------|-----------|
+| `query` | Ziel / Entwicklungsrichtung (Freitext, min. 3 Zeichen) |
+| `max_steps` | 2–10, Default 5 |
+| `progression_graph_id` | optional — Graph-Kontext für Nachfolger ab Schritt 2 |
+| `include_llm_intent` | LLM nur Schritt 1 (Budget) |
+
+**Response:** `steps[]` mit `exercise_id`, `variant_id`, `title`, `reasons`, `variants`; `retrieval_phase: …+path_builder`.
+
+**Algorithmus:** Iterativ Hybrid-Ranking — Schritt 1 aus Zielprofil, Folgeschritte mit Anker = letzte Übung, ohne Duplikate.
+
+**UI:** `ExerciseProgressionPathBuilder` im Progressionsgraph-Panel — Review, Varianten, `POST …/edges/sequence`.
+
+---
+
+## 22. Backlog (offen)

backend/planning_exercise_path_builder.py

@@ -0,0 +1,252 @@
+"""
+Planungs-KI Phase C3: Pfad-Vorschläge für Progressionsgraphen.
+
+Ziel-Freitext → iterative Hybrid-Suche (Schritt 1 mit optional LLM-Profil, Folgeschritte deterministisch).
+"""
+from __future__ import annotations
+
+from typing import Any, Dict, List, Optional, Set, Tuple
+
+from fastapi import HTTPException
+from pydantic import BaseModel, Field
+
+from tenant_context import TenantContext, library_content_visibility_sql
+from planning_exercise_profiles import PlanningTargetProfile
+from planning_exercise_retrieval import run_multistage_planning_retrieval
+from planning_exercise_target_pipeline import build_planning_target_with_query_pipeline
+from planning_exercise_progression import apply_progression_context_to_pack
+from planning_exercise_suggest import (
+    INTENT_SUGGEST_NEXT,
+    _enrich_planning_hits_with_variant_meta,
+    _intent_weights,
+    _load_skill_ids_for_exercise,
+    _normalize_query,
+    resolve_planning_exercise_intent,
+)
+from routers.training_planning import _has_planning_role
+
+
+class ProgressionPathSuggestRequest(BaseModel):
+    query: str = Field(..., min_length=3, max_length=2000)
+    max_steps: int = Field(default=5, ge=2, le=10)
+    include_llm_intent: bool = True
+    progression_graph_id: Optional[int] = Field(default=None, ge=1)
+    exercise_kind_any: Optional[List[str]] = None
+
+
+def _pick_next_path_hit(
+    hits: List[Dict[str, Any]],
+    used_exercise_ids: Set[int],
+) -> Optional[Dict[str, Any]]:
+    for hit in hits:
+        eid = int(hit["id"])
+        if eid in used_exercise_ids:
+            continue
+        return hit
+    return None
+
+
+def _hit_to_path_step(hit: Dict[str, Any]) -> Dict[str, Any]:
+    raw_vid = hit.get("suggested_variant_id")
+    variant_id: Optional[int] = None
+    if raw_vid is not None:
+        try:
+            vid = int(raw_vid)
+            if vid > 0:
+                variant_id = vid
+        except (TypeError, ValueError):
+            variant_id = None
+    return {
+        "exercise_id": int(hit["id"]),
+        "variant_id": variant_id,
+        "title": hit.get("title"),
+        "summary": hit.get("summary"),
+        "score": hit.get("score"),
+        "reasons": list(hit.get("reasons") or []),
+        "variants": hit.get("variants") or [],
+        "suggested_variant_id": hit.get("suggested_variant_id"),
+        "suggested_variant_name": hit.get("suggested_variant_name"),
+    }
+
+
+def _run_path_step_retrieval(
+    cur,
+    *,
+    tenant: TenantContext,
+    goal_query: str,
+    step_index: int,
+    planned_ids: List[int],
+    anchor_id: Optional[int],
+    anchor_variant_id: Optional[int],
+    progression_graph_id: Optional[int],
+    include_llm_intent: bool,
+    exercise_kind_any: Optional[List[str]],
+) -> Tuple[List[Dict[str, Any]], PlanningTargetProfile, Dict[str, Any], str]:
+    pack: Dict[str, Any] = {
+        "unit_id": None,
+        "unit": {
+            "id": None,
+            "framework_slot_id": None,
+            "origin_framework_slot_id": None,
+        },
+        "unit_title": None,
+        "group_id": None,
+        "group_name": None,
+        "section_order_index": None,
+        "section_title": None,
+        "section_guidance_notes": goal_query if step_index == 0 else None,
+        "planned_exercise_ids": list(planned_ids),
+        "anchor_exercise_id": anchor_id,
+        "anchor_title": None,
+        "anchor_skill_ids": sorted(_load_skill_ids_for_exercise(cur, anchor_id)),
+        "group_recent_exercise_ids": [],
+        "context_mode": "progression_path",
+        "has_planning_reference": bool(planned_ids or anchor_id),
+    }
+    pack = apply_progression_context_to_pack(
+        cur,
+        tenant,
+        pack,
+        explicit_graph_id=progression_graph_id,
+        anchor_variant_id=anchor_variant_id,
+    )
+
+    if step_index == 0:
+        heuristic_intent = resolve_planning_exercise_intent(goal_query, "free_search")
+        step_query = goal_query
+    else:
+        heuristic_intent = INTENT_SUGGEST_NEXT
+        step_query = "nächste sinnvolle übung im pfad"
+
+    has_plan_ref = bool(pack.get("has_planning_reference")) or step_index > 0
+    pipeline_context = {
+        "unit_title": None,
+        "group_name": None,
+        "section_title": pack.get("section_title"),
+        "section_guidance_notes": pack.get("section_guidance_notes"),
+        "section_exercise_count": len(planned_ids),
+        "planned_count": len(planned_ids),
+        "anchor_title": pack.get("anchor_title"),
+        "anchor_exercise_id": pack.get("anchor_exercise_id"),
+        "last_section_exercise_title": None,
+        "progression_graph_id": pack.get("progression_graph_id"),
+        "unit_skill_profile": None,
+        "section_skill_profile": None,
+        "has_planning_reference": has_plan_ref,
+        "expectation_mode": "query_only" if step_index == 0 and not planned_ids else "planning_hybrid",
+    }
+
+    target_profile, intent, _scenario, query_intent_summary = build_planning_target_with_query_pipeline(
+        cur,
+        unit=pack["unit"],
+        planned_exercise_ids=pack["planned_exercise_ids"],
+        section_planned_exercise_ids=[],
+        anchor_exercise_id=pack.get("anchor_exercise_id"),
+        query=goal_query if step_index == 0 else step_query,
+        heuristic_intent=heuristic_intent,
+        include_llm_intent=include_llm_intent and step_index == 0,
+        context_summary=pipeline_context,
+        has_planning_reference=has_plan_ref,
+    )
+
+    weights = _intent_weights(intent)
+    profile_id = tenant.profile_id
+    role = tenant.global_role
+    vis_sql, vis_params = library_content_visibility_sql(
+        alias="e",
+        profile_id=profile_id,
+        role=role,
+        effective_club_id=tenant.effective_club_id,
+    )
+
+    hits, _skills_by_ex, _full_lib = run_multistage_planning_retrieval(
+        cur,
+        vis_sql=vis_sql,
+        vis_params=vis_params,
+        query=step_query if step_index > 0 else goal_query,
+        exercise_kind_any=exercise_kind_any,
+        target=target_profile,
+        intent=intent,
+        intent_weights=weights,
+        pack=pack,
+    )
+    hits = _enrich_planning_hits_with_variant_meta(cur, hits[:32])
+    return hits, target_profile, query_intent_summary, intent
+
+
+def suggest_progression_path(
+    cur,
+    *,
+    tenant: TenantContext,
+    body: ProgressionPathSuggestRequest,
+) -> Dict[str, Any]:
+    role = tenant.global_role
+    if not _has_planning_role(role):
+        raise HTTPException(status_code=403, detail="Nur Trainer dürfen Pfad-Vorschläge abrufen")
+
+    goal_query = _normalize_query(body.query)
+    if len(goal_query) < 3:
+        raise HTTPException(status_code=400, detail="Ziel-Anfrage: mindestens 3 Zeichen")
+
+    max_steps = int(body.max_steps)
+    used: Set[int] = set()
+    steps: List[Dict[str, Any]] = []
+    planned_ids: List[int] = []
+    anchor_id: Optional[int] = None
+    anchor_variant_id: Optional[int] = None
+    target_profile: Optional[PlanningTargetProfile] = None
+    first_intent_summary: Dict[str, Any] = {}
+
+    for step_index in range(max_steps):
+        hits, target_profile, query_intent_summary, _intent = _run_path_step_retrieval(
+            cur,
+            tenant=tenant,
+            goal_query=goal_query,
+            step_index=step_index,
+            planned_ids=planned_ids,
+            anchor_id=anchor_id,
+            anchor_variant_id=anchor_variant_id,
+            progression_graph_id=body.progression_graph_id,
+            include_llm_intent=body.include_llm_intent,
+            exercise_kind_any=body.exercise_kind_any,
+        )
+        if step_index == 0:
+            first_intent_summary = query_intent_summary
+
+        hit = _pick_next_path_hit(hits, used)
+        if not hit:
+            break
+
+        step = _hit_to_path_step(hit)
+        steps.append(step)
+        eid = int(step["exercise_id"])
+        used.add(eid)
+        planned_ids.append(eid)
+        anchor_id = eid
+        anchor_variant_id = step.get("variant_id")
+
+    if len(steps) < 2:
+        raise HTTPException(
+            status_code=422,
+            detail="Zu wenig passende Übungen für einen Pfad (mindestens 2 Schritte). Ziel präzisieren oder max_steps senken.",
+        )
+
+    target_profile_summary = target_profile.to_summary_dict(cur) if target_profile else None
+
+    return {
+        "goal_query": goal_query,
+        "max_steps_requested": max_steps,
+        "steps": steps,
+        "step_count": len(steps),
+        "target_profile_summary": target_profile_summary,
+        "query_intent_summary": first_intent_summary,
+        "progression_graph_id": body.progression_graph_id,
+        "retrieval_phase": "profile_v1+full_library+path_builder",
+    }
+
+
+__all__ = [
+    "ProgressionPathSuggestRequest",
+    "suggest_progression_path",
+    "_pick_next_path_hit",
+]

backend/routers/planning_exercise_suggest.py

@@ -6,6 +6,7 @@ from fastapi import APIRouter, Depends
 from db import get_db, get_cursor
 from tenant_context import TenantContext, get_tenant_context
 from planning_exercise_suggest import PlanningExerciseSuggestRequest, suggest_planning_exercises
+from planning_exercise_path_builder import ProgressionPathSuggestRequest, suggest_progression_path
 
 router = APIRouter(prefix="/api/planning", tags=["planning_exercise_suggest"])
 
@@ -18,3 +19,13 @@ def post_planning_exercise_suggest(
     with get_db() as conn:
         cur = get_cursor(conn)
         return suggest_planning_exercises(cur, tenant=tenant, body=body)
+
+
+@router.post("/progression-path-suggest")
+def post_progression_path_suggest(
+    body: ProgressionPathSuggestRequest,
+    tenant: TenantContext = Depends(get_tenant_context),
+):
+    with get_db() as conn:
+        cur = get_cursor(conn)
+        return suggest_progression_path(cur, tenant=tenant, body=body)

backend/tests/test_planning_exercise_path_builder.py

@@ -0,0 +1,25 @@
+"""Tests Planungs-KI Phase C3 — Pfad-Vorschläge."""
+from planning_exercise_path_builder import _pick_next_path_hit, _hit_to_path_step
+
+
+def test_pick_next_path_hit_skips_used():
+    hits = [{"id": 1, "title": "A"}, {"id": 2, "title": "B"}, {"id": 3, "title": "C"}]
+    assert _pick_next_path_hit(hits, {1})["id"] == 2
+    assert _pick_next_path_hit(hits, {1, 2, 3}) is None
+
+
+def test_hit_to_path_step_maps_variant():
+    step = _hit_to_path_step(
+        {
+            "id": 10,
+            "title": "Test",
+            "score": 0.8,
+            "reasons": ["Graph"],
+            "suggested_variant_id": 7,
+            "suggested_variant_name": "Leicht",
+            "variants": [{"id": 7, "variant_name": "Leicht"}],
+        }
+    )
+    assert step["exercise_id"] == 10
+    assert step["variant_id"] == 7
+    assert step["suggested_variant_name"] == "Leicht"

backend/version.py

@@ -1,6 +1,6 @@
 # Shinkan Jinkendo Version Information
 
-APP_VERSION = "0.8.184"
+APP_VERSION = "0.8.185"
 BUILD_DATE = "2026-05-23"
 DB_SCHEMA_VERSION = "20260531074"
 
@@ -29,7 +29,7 @@ MODULE_VERSIONS = {
     "skill_profiles": "1.0.0",  # Phase 3: gewichtetes Fähigkeiten-Profil + skill-discovery/suggestions
     "methods": "0.1.0",
     "exercises": "2.37.0",  # Planungs-KI P1: Szenario-Pipeline + Query-Intent-Overlay
-    "planning_exercise_suggest": "0.12.0",  # Phase C2: Varianten in Treffern + Übernahme
+    "planning_exercise_suggest": "0.13.0",  # Phase C3: progression-path-suggest für Graph-Builder
     "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
@@ -44,6 +44,14 @@ MODULE_VERSIONS = {
 }
 
 CHANGELOG = [
+    {
+        "version": "0.8.185",
+        "date": "2026-05-23",
+        "changes": [
+            "Planungs-KI Phase C3: POST /api/planning/progression-path-suggest — Ziel → aufbauender Übungspfad.",
+            "Progressionsgraph-UI: KI-Pfad-Builder mit Review und Speichern via edges/sequence.",
+        ],
+    },
     {
         "version": "0.8.184",
         "date": "2026-05-23",

docs/HANDOVER.md

@@ -1,7 +1,7 @@
 # Shinkan Jinkendo – Entwicklungsstand & Handover
 
 **Stand:** 2026-05-23  
-**App-Version / DB-Schema:** App **`0.8.184`** (Planungs-KI Phase C2); DB **`20260531074`** — maßgeblich **`backend/version.py`**.
+**App-Version / DB-Schema:** App **`0.8.185`** (Planungs-KI Phase C3); DB **`20260531074`** — 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**.
 
@@ -103,7 +103,7 @@ Das Schema ist gegenüber dem Code zurück: Migration **`022_skills_schema_compl
 | **B** | Text-Signale aus guidance/Rahmen-Zielen (`planning_text_signals`) | ✅ **0.8.181** |
 | **C1** | Progressionsgraph auto-match + variantenbewusste Nachfolger | ✅ **0.8.183** |
 | **C2** | Varianten in Trefferliste / Picker-Auswahl | ✅ **0.8.184** |
-| **C3** | Graph-Builder: Ziel → Pfad vorschlagen → in Graph speichern | 🔲 |
+| **C3** | Graph-Builder: Ziel → Pfad vorschlagen → in Graph speichern | ✅ **0.8.185** |
 | **D** | `planning_context` an `suggestExerciseAi` (Neu-Anlage) | 🔲 |
 
 **Backend:** `planning_exercise_suggest.py`, `planning_exercise_retrieval.py`, `planning_exercise_profiles.py`, `planning_exercise_target_pipeline.py`, `planning_exercise_progression.py` · Router `POST /api/planning/exercise-suggest`
@@ -247,10 +247,10 @@ Das Schema ist gegenüber dem Code zurück: Migration **`022_skills_schema_compl
 
 ### Planungs-KI (priorisiert)
 
-1. **C3 — Graph-Builder:** Modus „Pfad zum Ziel“ → sequenzielle Vorschläge → `POST …/edges/sequence` nach Review.
-2. **Graph-Auswahl UI:** Dropdown neben Auto-Match; Rahmen-Slot mit Default-Graph verknüpfen.
-3. **Enrichment:** Skills für Kern-Übungen nachziehen (sonst schwaches Profil-Ranking).
-4. **D — Neu-Anlage:** `planning_context_json` an `POST /api/exercises/ai/suggest`.
+1. **Graph-Auswahl UI:** Dropdown neben Auto-Match; Rahmen-Slot mit Default-Graph verknüpfen.
+2. **Enrichment:** Skills für Kern-Übungen nachziehen (sonst schwaches Profil-Ranking).
+3. **D — Neu-Anlage:** `planning_context_json` an `POST /api/exercises/ai/suggest`.
+4. **C3 Feinschliff:** Einzelschritte im Pfad manuell ersetzen (Picker); Pfad an bestehende Kette anhängen.
 
 ### Allgemein
 

frontend/src/api/planning.js

@@ -84,6 +84,14 @@ export async function suggestPlanningExercises(body = {}) {
   })
 }
 
+/** Planungs-KI Phase C3: aufbauender Übungspfad für Progressionsgraphen. */
+export async function suggestProgressionPath(body = {}) {
+  return request('/api/planning/progression-path-suggest', {
+    method: 'POST',
+    body: JSON.stringify(body),
+  })
+}
+
 /** Rahmen-Slot → geplante Einheit (tiefe Kopie, origin_framework_slot_id). */
 export async function createTrainingUnitFromFrameworkSlot(data) {
   return request('/api/training-units/from-framework-slot', {

frontend/src/components/ExerciseProgressionGraphPanel.jsx

@@ -9,6 +9,7 @@ import SkillProfilePanel from './skills/SkillProfilePanel'
 import { useAuth } from '../context/AuthContext'
 import { getTenantClubDependencyKey } from '../utils/activeClub'
 import ExercisePickerModal from './ExercisePickerModal'
+import ExerciseProgressionPathBuilder from './ExerciseProgressionPathBuilder'
 import { EXERCISE_VISIBILITY_FIELD_LABEL } from '../constants/exerciseGovernanceLabels'
 
 const VIS_OPTIONS = [
@@ -696,6 +697,13 @@ export default function ExerciseProgressionGraphPanel({
             defaultExpanded
             artifactType="progression_graph"
           />
+          <ExerciseProgressionPathBuilder
+            graphId={selectedGraphId}
+            disabled={busy}
+            onSaved={async () => {
+              await refreshEdges(selectedGraphId)
+            }}
+          />
           <div className="card" style={{ marginBottom: '12px' }}>
             <h3 style={{ marginTop: 0, fontSize: '1rem' }}>Sequenz / Reihe anlegen</h3>
             <p style={{ fontSize: '12px', color: 'var(--text3)', marginTop: 0 }}>

frontend/src/components/ExerciseProgressionPathBuilder.jsx

@@ -0,0 +1,313 @@
+/**
+ * Planungs-KI Phase C3: Ziel → Übungspfad vorschlagen → in Progressionsgraph speichern.
+ */
+import React, { useCallback, useState } from 'react'
+import api from '../utils/api'
+
+function emptyPathStep() {
+  return { exerciseId: null, exerciseTitle: '', variantId: null, variants: [], reasons: [] }
+}
+
+function mapApiStepToRow(step) {
+  const variants = Array.isArray(step?.variants) ? step.variants : []
+  const rawVid = step?.variant_id ?? step?.suggested_variant_id ?? null
+  const variantId =
+    rawVid != null && Number.isFinite(Number(rawVid)) && Number(rawVid) > 0 ? Number(rawVid) : null
+  return {
+    exerciseId: step?.exercise_id != null ? Number(step.exercise_id) : null,
+    exerciseTitle: (step?.title || '').trim() || (step?.exercise_id ? `Übung #${step.exercise_id}` : ''),
+    variantId,
+    variants,
+    reasons: Array.isArray(step?.reasons) ? step.reasons : [],
+  }
+}
+
+export default function ExerciseProgressionPathBuilder({
+  graphId,
+  disabled = false,
+  onSaved,
+}) {
+  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)
+  const [pathSteps, setPathSteps] = useState([])
+
+  const patchStep = useCallback((idx, patch) => {
+    setPathSteps((prev) => prev.map((row, i) => (i === idx ? { ...row, ...patch } : row)))
+  }, [])
+
+  const removeStep = useCallback((idx) => {
+    setPathSteps((prev) => (prev.length <= 2 ? prev : prev.filter((_, i) => i !== idx)))
+  }, [])
+
+  const moveStep = useCallback((idx, dir) => {
+    setPathSteps((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 next
+    })
+  }, [])
+
+  const suggestPath = async () => {
+    const q = (goalQuery || '').trim()
+    if (q.length < 3) {
+      alert('Ziel-Anfrage: mindestens 3 Zeichen.')
+      return
+    }
+    if (!graphId) {
+      alert('Zuerst einen Graphen wählen.')
+      return
+    }
+    setLoading(true)
+    setError('')
+    try {
+      const res = await api.suggestProgressionPath({
+        query: q,
+        max_steps: Number(maxSteps),
+        include_llm_intent: true,
+        progression_graph_id: Number(graphId),
+      })
+      const rows = (Array.isArray(res?.steps) ? res.steps : []).map(mapApiStepToRow)
+      if (rows.length < 2) {
+        throw new Error('Zu wenig Schritte im Vorschlag.')
+      }
+      setPathSteps(rows)
+      setTargetSummary(res?.target_profile_summary || null)
+      if (!segmentNotes.trim() && q) setSegmentNotes(q.slice(0, 400))
+    } catch (e) {
+      console.error(e)
+      setError(e.message || 'Pfad-Vorschlag fehlgeschlagen')
+      setPathSteps([])
+      setTargetSummary(null)
+    } finally {
+      setLoading(false)
+    }
+  }
+
+  const savePathToGraph = async () => {
+    if (!graphId) {
+      alert('Zuerst einen Graphen wählen.')
+      return
+    }
+    const steps = pathSteps.filter((s) => s.exerciseId != null)
+    if (steps.length < 2) {
+      alert('Mindestens zwei Schritte mit Übung nötig.')
+      return
+    }
+    const n = steps.length - 1
+    const noteRaw = segmentNotes.trim()
+    const segment_notes = Array.from({ length: n }, (_, i) => {
+      const reasons = (steps[i + 1]?.reasons || []).slice(0, 2).join(' · ')
+      if (reasons) return reasons
+      return noteRaw || null
+    })
+
+    setSaving(true)
+    setError('')
+    try {
+      await api.createExerciseProgressionSequence(Number(graphId), {
+        steps: steps.map((s) => ({
+          exercise_id: s.exerciseId,
+          variant_id: s.variantId || null,
+        })),
+        segment_notes,
+      })
+      setPathSteps([])
+      setTargetSummary(null)
+      if (typeof onSaved === 'function') await onSaved()
+      alert(`${n} Nachfolger-Kante(n) aus KI-Pfad gespeichert.`)
+    } catch (e) {
+      console.error(e)
+      setError(e.message || 'Speichern fehlgeschlagen')
+    } finally {
+      setSaving(false)
+    }
+  }
+
+  return (
+    <div
+      className="card"
+      style={{
+        marginBottom: '12px',
+        borderColor: 'color-mix(in srgb, var(--accent) 35%, var(--border))',
+      }}
+    >
+      <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 aufbauende Übungsreihe vor. Nach Review als
+        Nachfolger-Ketten in den aktiven Graph speichern (über mehrere Trainingspläne hinweg nutzbar).
+      </p>
+      <div style={{ display: 'flex', flexWrap: 'wrap', gap: '10px', alignItems: 'flex-end' }}>
+        <div className="form-row" style={{ flex: '2 1 240px', marginBottom: 0 }}>
+          <label className="form-label">Ziel / Entwicklungsrichtung</label>
+          <input
+            className="form-input"
+            value={goalQuery}
+            onChange={(e) => setGoalQuery(e.target.value)}
+            placeholder="z. B. sichere Reaktion im Partnertraining aufbauen …"
+            disabled={disabled || loading || saving}
+          />
+        </div>
+        <div className="form-row" style={{ flex: '0 1 120px', marginBottom: 0 }}>
+          <label className="form-label">Schritte</label>
+          <input
+            type="number"
+            min={2}
+            max={10}
+            className="form-input"
+            value={maxSteps}
+            onChange={(e) => setMaxSteps(Math.max(2, Math.min(10, Number(e.target.value) || 5)))}
+            disabled={disabled || loading || saving}
+          />
+        </div>
+        <button
+          type="button"
+          className="btn btn-primary"
+          disabled={disabled || loading || saving || !graphId}
+          onClick={suggestPath}
+        >
+          {loading ? 'Vorschlag …' : 'Pfad vorschlagen'}
+        </button>
+      </div>
+
+      {error ? (
+        <p className="form-error" style={{ marginTop: '10px' }}>
+          {error}
+        </p>
+      ) : null}
+
+      {targetSummary && pathSteps.length > 0 ? (
+        <div style={{ marginTop: '10px', display: 'flex', flexWrap: 'wrap', gap: '6px' }}>
+          {Array.isArray(targetSummary.focus_areas) &&
+            targetSummary.focus_areas.slice(0, 2).map((fa) => (
+              <span key={fa} className="exercise-tag">
+                Fokus: {fa}
+              </span>
+            ))}
+          {Array.isArray(targetSummary.top_skills) &&
+            targetSummary.top_skills.slice(0, 2).map((sk) => (
+              <span key={sk.skill_id} className="exercise-tag">
+                {sk.name}
+              </span>
+            ))}
+        </div>
+      ) : null}
+
+      {pathSteps.length > 0 ? (
+        <>
+          <div style={{ marginTop: '14px' }}>
+            {pathSteps.map((step, idx) => (
+              <div
+                key={`${step.exerciseId}-${idx}`}
+                style={{
+                  display: 'grid',
+                  gridTemplateColumns: 'repeat(auto-fit, minmax(200px, 1fr))',
+                  gap: '10px',
+                  alignItems: 'end',
+                  marginBottom: '12px',
+                  paddingBottom: '12px',
+                  borderBottom: idx < pathSteps.length - 1 ? '1px dashed var(--border)' : 'none',
+                }}
+              >
+                <div className="form-row" style={{ marginBottom: 0 }}>
+                  <label className="form-label">
+                    Schritt {idx + 1}
+                    {idx === 0 ? ' (Einstieg)' : idx === pathSteps.length - 1 ? ' (Zielnähe)' : ''}
+                  </label>
+                  <div style={{ fontSize: '13px' }}>
+                    <strong>{step.exerciseTitle}</strong>
+                    <span style={{ color: 'var(--text3)' }}> (#{step.exerciseId})</span>
+                  </div>
+                  {step.reasons?.length ? (
+                    <ul
+                      style={{
+                        margin: '6px 0 0',
+                        paddingLeft: '16px',
+                        fontSize: '11px',
+                        color: 'var(--accent-dark)',
+                      }}
+                    >
+                      {step.reasons.slice(0, 2).map((r) => (
+                        <li key={r}>{r}</li>
+                      ))}
+                    </ul>
+                  ) : null}
+                </div>
+                <div className="form-row" style={{ marginBottom: 0 }}>
+                  <label className="form-label">Variante</label>
+                  <select
+                    className="form-input"
+                    value={step.variantId ?? ''}
+                    onChange={(e) =>
+                      patchStep(idx, {
+                        variantId: e.target.value === '' ? null : parseInt(e.target.value, 10),
+                      })
+                    }
+                    disabled={!step.exerciseId}
+                  >
+                    <option value="">Gesamte Übung</option>
+                    {(step.variants || []).map((v) => (
+                      <option key={v.id} value={v.id}>
+                        {v.variant_name || `Variante #${v.id}`}
+                      </option>
+                    ))}
+                  </select>
+                </div>
+                <div style={{ display: 'flex', gap: '6px', flexWrap: 'wrap' }}>
+                  <button type="button" className="btn" style={{ fontSize: '12px', padding: '4px 8px' }} onClick={() => moveStep(idx, -1)}>
+                    ↑
+                  </button>
+                  <button type="button" className="btn" style={{ fontSize: '12px', padding: '4px 8px' }} onClick={() => moveStep(idx, 1)}>
+                    ↓
+                  </button>
+                  <button type="button" className="btn" style={{ fontSize: '12px', padding: '4px 8px' }} onClick={() => removeStep(idx)}>
+                    Entfernen
+                  </button>
+                </div>
+              </div>
+            ))}
+          </div>
+          <div className="form-row">
+            <label className="form-label">Notiz für Kanten (Fallback, optional)</label>
+            <textarea
+              className="form-input"
+              rows={2}
+              value={segmentNotes}
+              onChange={(e) => setSegmentNotes(e.target.value)}
+              placeholder="Wird pro Kante genutzt, wenn keine KI-Begründung vorliegt."
+            />
+          </div>
+          <div style={{ display: 'flex', flexWrap: 'wrap', gap: '8px' }}>
+            <button
+              type="button"
+              className="btn btn-primary"
+              disabled={disabled || saving || pathSteps.filter((s) => s.exerciseId).length < 2}
+              onClick={savePathToGraph}
+            >
+              {saving ? 'Speichern …' : 'Pfad in Graph speichern'}
+            </button>
+            <button
+              type="button"
+              className="btn btn-secondary"
+              disabled={loading || saving}
+              onClick={() => {
+                setPathSteps([])
+                setTargetSummary(null)
+              }}
+            >
+              Vorschlag verwerfen
+            </button>
+          </div>
+        </>
+      ) : null}
+    </div>
+  )
+}