Update Dockerfile and requirements for improved dependency management

- Added `tzdata` installation in the Dockerfile to support time zone handling in Linux environments.
- Increased `PIP_DEFAULT_TIMEOUT` and added retry logic for pip installations to enhance reliability during dependency installation.
- Updated `requirements.txt` to conditionally include `tzdata` for Windows platforms, ensuring compatibility across different operating systems.

backend/Dockerfile

@@ -2,14 +2,16 @@ FROM python:3.12-slim
 
 WORKDIR /app
 
-# Install system dependencies
+# Install system dependencies (tzdata für zoneinfo/ZoneInfo unter Linux)
 RUN apt-get update && apt-get install -y \
     postgresql-client \
+    tzdata \
     && rm -rf /var/lib/apt/lists/*
 
 # Copy requirements and install dependencies
 COPY requirements.txt .
-RUN pip install --no-cache-dir -r requirements.txt
+ENV PIP_DEFAULT_TIMEOUT=120
+RUN pip install --no-cache-dir --retries 5 -r requirements.txt
 
 # Copy application code
 COPY . .

backend/planning_exercise_form_context.py

@@ -6,7 +6,7 @@ Wird als ``planning_context_json`` in Übungs-Prompts (summary, skills, instruct
 from __future__ import annotations
 
 import json
-from typing import Any, Dict, List, Mapping, Optional
+from typing import Any, Dict, List, Mapping, Optional, Sequence
 
 _MAX_JSON_CHARS = 6000
 _MAX_STRING = 800
@@ -85,6 +85,163 @@ def planning_context_prompt_variables(
     }
 
 
+def _major_index_from_step(step: Mapping[str, Any]) -> Optional[int]:
+    for key in ("roadmap_major_step_index", "major_step_index"):
+        raw = step.get(key)
+        if raw is None:
+            continue
+        try:
+            return int(raw)
+        except (TypeError, ValueError):
+            continue
+    return None
+
+
+def prior_path_steps_before_major(
+    steps: Sequence[Mapping[str, Any]],
+    major_idx: int,
+) -> List[Dict[str, Any]]:
+    """Pfadschritte mit kleinerem roadmap_major_step_index, sortiert."""
+    prior: List[Dict[str, Any]] = []
+    for step in steps:
+        mi = _major_index_from_step(step)
+        if mi is not None and mi < major_idx:
+            prior.append(dict(step))
+    prior.sort(key=lambda s: _major_index_from_step(s) or 0)
+    return prior
+
+
+def _step_display_fields(step: Mapping[str, Any]) -> Dict[str, Any]:
+    title = _trim_str(
+        step.get("title") or step.get("exercise_title"),
+        limit=200,
+    )
+    learning_goal = _trim_str(
+        step.get("roadmap_learning_goal") or step.get("learning_goal"),
+        limit=500,
+    )
+    summary = _trim_str(step.get("summary"), limit=400)
+    start_state = _trim_str(step.get("roadmap_start_state") or step.get("start_state"))
+    target_state = _trim_str(step.get("roadmap_target_state") or step.get("target_state"))
+    phase = _trim_str(step.get("roadmap_phase") or step.get("phase"))
+    criteria_raw = step.get("stage_success_criteria") or step.get("success_criteria") or []
+    criteria = [
+        t
+        for x in criteria_raw
+        if (t := _trim_str(x, limit=200))
+    ][:4]
+    out: Dict[str, Any] = {
+        "title": title,
+        "learning_goal": learning_goal,
+        "summary": summary,
+        "start_state": start_state,
+        "target_state": target_state,
+        "phase": phase,
+        "success_criteria": criteria or None,
+        "major_step_index": _major_index_from_step(step),
+    }
+    return {k: v for k, v in out.items() if v is not None and v != "" and v != []}
+
+
+def build_progression_entry_state(
+    *,
+    major_step_index: Optional[int] = None,
+    prior_steps: Sequence[Mapping[str, Any]] = (),
+    start_situation: Optional[str] = None,
+    current_stage_start: Optional[str] = None,
+) -> Dict[str, Any]:
+    """
+    Eingangszustand für eine Roadmap-Stufe: erreichte Voraussetzungen aus Vorstufen.
+    """
+    prior_compact = [_step_display_fields(s) for s in prior_steps]
+    prior_compact = [
+        p
+        for p in prior_compact
+        if any(p.get(k) for k in ("title", "learning_goal", "summary", "success_criteria"))
+    ]
+
+    achievements: List[str] = []
+    detail_lines: List[str] = []
+    for p in prior_compact:
+        if p.get("success_criteria"):
+            achievements.extend(p["success_criteria"])
+        elif p.get("learning_goal"):
+            achievements.append(p["learning_goal"])
+
+        label_parts: List[str] = []
+        if p.get("major_step_index") is not None:
+            label_parts.append(f"Stufe {int(p['major_step_index']) + 1}")
+        if p.get("phase"):
+            label_parts.append(f"({p['phase']})")
+        if p.get("title"):
+            label_parts.append(f"„{p['title']}\"")
+        prefix = " ".join(label_parts) if label_parts else "Vorstufe"
+        achieved = ""
+        if p.get("target_state"):
+            achieved = p["target_state"]
+        elif p.get("success_criteria"):
+            achieved = "; ".join(p["success_criteria"])
+        elif p.get("learning_goal"):
+            achieved = p["learning_goal"]
+        elif p.get("summary"):
+            achieved = p["summary"]
+        if achieved:
+            detail_lines.append(f"{prefix}: erreicht — {achieved}")
+
+    immediate_entry: Optional[str] = _trim_str(current_stage_start)
+    if not immediate_entry and prior_compact:
+        immediate = prior_compact[-1]
+        if immediate.get("target_state"):
+            immediate_entry = immediate["target_state"]
+        elif immediate.get("success_criteria"):
+            immediate_entry = "; ".join(immediate["success_criteria"])
+        elif immediate.get("learning_goal"):
+            immediate_entry = immediate["learning_goal"]
+        elif immediate.get("summary"):
+            immediate_entry = immediate["summary"]
+    elif not immediate_entry and start_situation:
+        immediate_entry = start_situation
+
+    entry_state = immediate_entry or start_situation
+    if prior_compact and start_situation and not immediate_entry:
+        detail_lines.insert(0, f"Ausgangsbasis Pfad: {start_situation}")
+
+    out: Dict[str, Any] = {}
+    if entry_state:
+        out["entry_state"] = _trim_str(entry_state, limit=1200)
+    if detail_lines:
+        out["entry_state_detail"] = _trim_str("\n".join(detail_lines), limit=2000)
+    if prior_compact:
+        out["prior_steps"] = prior_compact[:6]
+    if achievements:
+        out["prior_achievements"] = list(dict.fromkeys(achievements))[:8]
+    return out
+
+
+def enrich_gap_snapshot_with_entry_state(
+    snapshot: Mapping[str, Any],
+    *,
+    steps: Sequence[Mapping[str, Any]],
+    major_step_index: Optional[int],
+) -> Dict[str, Any]:
+    snap = dict(snapshot)
+    if major_step_index is None:
+        return snap
+    try:
+        mi = int(major_step_index)
+    except (TypeError, ValueError):
+        return snap
+    prior = prior_path_steps_before_major(steps, mi)
+    entry = build_progression_entry_state(
+        major_step_index=mi,
+        prior_steps=prior,
+        start_situation=snap.get("start_situation"),
+        current_stage_start=snap.get("stage_start_state"),
+    )
+    snap.update(entry)
+    return snap
+
+
 def build_progression_gap_snapshot(
     *,
     goal_analysis: Optional[Mapping[str, Any]] = None,
@@ -141,6 +298,8 @@ def build_progression_gap_snapshot(
         "stage_learning_goal": _trim_str(
             spec.get("learning_goal"), limit=1200
         ),
+        "stage_start_state": _trim_str(spec.get("start_state")),
+        "stage_target_state": _trim_str(spec.get("target_state")),
         "stage_phase": _trim_str(spec.get("phase")),
         "stage_exercise_type": _trim_str(spec.get("exercise_type")),
         "stage_load_profile": load_profile or None,
@@ -160,6 +319,7 @@ def build_progression_path_gap_planning_context(
     offer: Optional[Mapping[str, Any]] = None,
     neighbor_before: Optional[Mapping[str, Any]] = None,
     neighbor_after: Optional[Mapping[str, Any]] = None,
+    prior_path_steps: Optional[Sequence[Mapping[str, Any]]] = None,
     path_step_count: int = 0,
     major_step_count: Optional[int] = None,
     roadmap_phase: Optional[str] = None,
@@ -207,6 +367,14 @@ def build_progression_path_gap_planning_context(
         semantic_brief=semantic_brief,
     )
     ctx.update(snap)
+    if major_idx is not None and prior_path_steps:
+        ctx.update(
+            build_progression_entry_state(
+                major_step_index=major_idx,
+                prior_steps=list(prior_path_steps),
+                start_situation=ctx.get("start_situation"),
+            )
+        )
     if stage_learning_goal_override and stage_learning_goal_override.strip():
         ctx["stage_learning_goal"] = _trim_str(stage_learning_goal_override, limit=1200)
         ctx["roadmap_learning_goal"] = ctx["stage_learning_goal"]
@@ -216,8 +384,11 @@ def build_progression_path_gap_planning_context(
 
 
 __all__ = [
+    "build_progression_entry_state",
     "build_progression_gap_snapshot",
     "build_progression_path_gap_planning_context",
+    "enrich_gap_snapshot_with_entry_state",
+    "prior_path_steps_before_major",
     "compact_planning_context_json",
     "planning_context_prompt_variables",
     "sanitize_planning_context_for_ai",

backend/planning_exercise_path_ai_fill.py

@@ -12,7 +12,12 @@ from ai_prompt_job import run_exercise_form_ai_suggestion
 from exercise_ai import strip_html_to_plain
 
 from planning_exercise_path_qa import find_step_pair_index
-from planning_exercise_form_context import build_progression_gap_snapshot
+from planning_exercise_form_context import (
+    build_progression_entry_state,
+    build_progression_gap_snapshot,
+    enrich_gap_snapshot_with_entry_state,
+    prior_path_steps_before_major,
+)
 from planning_exercise_semantics import PlanningSemanticBrief, brief_to_summary_dict
 
 _logger = logging.getLogger("shinkan.planning_exercise_path_ai_fill")
@@ -47,6 +52,8 @@ def _build_stage_ai_context(
     spec: Mapping[str, Any],
     step_before: Optional[Mapping[str, Any]] = None,
     step_after: Optional[Mapping[str, Any]] = None,
+    prior_steps: Optional[Sequence[Mapping[str, Any]]] = None,
+    start_situation: Optional[str] = None,
 ) -> ExerciseFormAiPromptContext:
     """KI-Kontext für unbesetzte Roadmap-Stufe (keine Brücke zwischen falschen Array-Indizes)."""
     gap = dict(spec.get("gap") or {})
@@ -59,11 +66,26 @@ def _build_stage_ai_context(
         or ""
     ).strip()
     title = (spec.get("title_hint") or f"{topic} — {phase}").strip()[:280]
+    major_idx = spec.get("roadmap_major_step_index")
+    entry: Dict[str, Any] = {}
+    if prior_steps is not None and major_idx is not None:
+        entry = build_progression_entry_state(
+            major_step_index=major_idx,
+            prior_steps=prior_steps,
+            start_situation=start_situation,
+        )
+
     goal_parts = [
         f"Planungsziel: {goal_query}",
         f"Roadmap-Stufe ({phase}): {learning_goal}",
         "Erstelle eine Übung, die dieses Stufen-Lernziel erfüllt — keine generische Brücken-Übung.",
     ]
+    if entry.get("entry_state"):
+        goal_parts.append(
+            f"Eingangszustand (erreichte Voraussetzungen): {entry['entry_state']}"
+        )
+    if entry.get("entry_state_detail") and entry.get("entry_state_detail") != entry.get("entry_state"):
+        goal_parts.append(f"Bisheriger Pfad:\n{entry['entry_state_detail']}")
     if step_before:
         goal_parts.append(
             f"Vorherige Stufe im Pfad: „{(step_before.get('title') or '').strip()}“"
@@ -106,6 +128,7 @@ def try_suggest_ai_stage_step(
     except (TypeError, ValueError):
         return None
     step_before, step_after = _resolve_neighbor_steps_by_major_index(steps, mi)
+    prior_steps = prior_path_steps_before_major(steps, mi)
     gap = dict(spec.get("gap") or {})
     if not gap.get("expected_phase"):
         gap["expected_phase"] = spec.get("phase") or "vertiefung"
@@ -119,6 +142,7 @@ def try_suggest_ai_stage_step(
         spec=spec,
         step_before=step_before,
         step_after=step_after,
+        prior_steps=prior_steps,
     )
     try:
         ai_payload = run_exercise_form_ai_suggestion(cur, ctx=ctx)
@@ -440,8 +464,16 @@ def build_gap_fill_goal_text(
         f"Planungsziel (gesamter Pfad): {goal_query}",
         f"Hauptthema: {snap.get('primary_topic') or topic}",
     ]
-    if snap.get("start_situation"):
+    if snap.get("entry_state"):
+        parts.append(
+            f"Eingangszustand (erreichte Voraussetzungen aus Vorstufen): {snap['entry_state']}"
+        )
+    if snap.get("entry_state_detail") and snap.get("entry_state_detail") != snap.get("entry_state"):
+        parts.append(f"Bisheriger Pfad:\n{snap['entry_state_detail']}")
+    if snap.get("start_situation") and not snap.get("entry_state"):
         parts.append(f"Voraussetzung / Ausgangslage (Progression): {snap['start_situation']}")
+    elif snap.get("start_situation") and snap.get("prior_steps"):
+        parts.append(f"Ausgangsbasis des gesamten Pfads: {snap['start_situation']}")
     if snap.get("target_state"):
         parts.append(f"Gesamtziel der Progression: {snap['target_state']}")
     if snap.get("roadmap_notes"):
@@ -525,6 +557,14 @@ def build_gap_fill_offer(
         step_a = steps[idx] if idx < len(steps) else None
         step_b = steps[idx + 1] if idx + 1 < len(steps) else None
     offer_id = f"{spec.get('source')}-{idx}-{uuid.uuid4().hex[:8]}"
+    enriched_snapshot = dict(roadmap_snapshot) if roadmap_snapshot else {}
+    major_raw = spec.get("roadmap_major_step_index")
+    if major_raw is not None:
+        enriched_snapshot = enrich_gap_snapshot_with_entry_state(
+            enriched_snapshot,
+            steps=steps,
+            major_step_index=major_raw,
+        )
     goal_for_ai = ""
     if brief and goal_query:
         goal_for_ai = build_gap_fill_goal_text(
@@ -533,9 +573,9 @@ def build_gap_fill_offer(
             spec=spec,
             step_a=step_a,
             step_b=step_b,
-            roadmap_snapshot=roadmap_snapshot,
+            roadmap_snapshot=enriched_snapshot or None,
         )
-    ctx_preview = dict(roadmap_snapshot) if roadmap_snapshot else None
+    ctx_preview = enriched_snapshot or None
     offer: Dict[str, Any] = {
         "offer_id": offer_id,
         "source": spec.get("source"),

backend/planning_exercise_path_builder.py

@@ -535,6 +535,9 @@ def _annotate_roadmap_step(
         step["roadmap_start_state"] = stage_spec.start_state.strip()
     if (stage_spec.target_state or "").strip():
         step["roadmap_target_state"] = stage_spec.target_state.strip()
+    if stage_spec.success_criteria:
+        step["success_criteria"] = list(stage_spec.success_criteria)
+        step["stage_success_criteria"] = list(stage_spec.success_criteria)
     step["roadmap_match_source"] = "stage_spec"
     if skill_expectations:
         step["skill_expectations"] = skill_expectations

backend/requirements.txt

@@ -10,5 +10,5 @@ bcrypt==4.1.3
 slowapi==0.1.9
 psycopg2-binary==2.9.9
 python-dateutil==2.9.0
-tzdata>=2024.1  # ZoneInfo (Europe/Berlin) auch unter Windows
+tzdata>=2024.1; sys_platform == "win32"  # ZoneInfo lokal; Linux/Docker: apt tzdata
 sqlparse>=0.5.0  # Migrationen: Statements splitten (Fallback ohne psql)

backend/tests/test_planning_exercise_form_context.py

@@ -1,7 +1,9 @@
 """Tests Planungs-KI Phase D — planning_context für suggestExerciseAi."""
 from planning_exercise_form_context import (
+    build_progression_entry_state,
     build_progression_gap_snapshot,
     build_progression_path_gap_planning_context,
+    enrich_gap_snapshot_with_entry_state,
     planning_context_prompt_variables,
     sanitize_planning_context_for_ai,
 )
@@ -80,6 +82,47 @@ def test_gap_planning_context_carries_snapshot_fields():
     assert ctx["stage_learning_goal"] == "Stufenziel"
 
 
+def test_build_progression_entry_state_from_prior_steps():
+    entry = build_progression_entry_state(
+        major_step_index=2,
+        prior_steps=[
+            {
+                "roadmap_major_step_index": 0,
+                "title": "Schritt-Stand",
+                "roadmap_phase": "einstieg",
+                "success_criteria": ["stabile Grundstellung"],
+            },
+            {
+                "roadmap_major_step_index": 1,
+                "title": "Mawashi Vorbereitung",
+                "roadmap_target_state": "Hüfte dreht vor dem Knie",
+                "roadmap_phase": "grundlage",
+            },
+        ],
+        start_situation="Anfänger ohne Kumite-Erfahrung",
+        current_stage_start="Hüfte dreht vor dem Knie, sicherer Stand",
+    )
+    assert entry["entry_state"] == "Hüfte dreht vor dem Knie, sicherer Stand"
+    assert "Mawashi Vorbereitung" in entry["entry_state_detail"]
+    assert "stabile Grundstellung" in entry["prior_achievements"][0]
+
+
+def test_enrich_gap_snapshot_with_entry_state():
+    snap = enrich_gap_snapshot_with_entry_state(
+        {"start_situation": "Basis", "stage_learning_goal": "Rhythmen"},
+        steps=[
+            {
+                "roadmap_major_step_index": 0,
+                "title": "A",
+                "success_criteria": ["Timing erkannt"],
+            }
+        ],
+        major_step_index=1,
+    )
+    assert snap["entry_state"] == "Timing erkannt"
+    assert snap["prior_steps"][0]["title"] == "A"
+
+
 def test_gap_planning_context_trainer_supplements_and_stage_override():
     ctx = build_progression_path_gap_planning_context(
         goal_query="Kumite",

backend/tests/test_planning_exercise_path_ai_fill.py

@@ -169,6 +169,36 @@ def test_build_gap_fill_offer_roadmap_unfilled_uses_major_step_neighbors():
     assert "Stufen-Lernziel" in offer["goal_for_ai"] or "Roadmap-Stufe" in offer["goal_for_ai"]
 
 
+def test_build_gap_fill_offer_includes_entry_state_from_prior_steps():
+    brief = build_semantic_brief("Kumite Beinarbeit")
+    steps = [
+        {
+            "roadmap_major_step_index": 0,
+            "title": "Schritt A",
+            "roadmap_target_state": "gleichmäßige Distanz",
+            "success_criteria": ["Partnerabstand stabil"],
+        },
+        {"roadmap_major_step_index": 2, "title": "Schritt C"},
+    ]
+    offer = build_gap_fill_offer(
+        spec={
+            "source": "roadmap_unfilled",
+            "phase": "vertiefung",
+            "title_hint": "Rhythmen",
+            "roadmap_major_step_index": 1,
+        },
+        steps=steps,
+        goal_query="Kumite Beinarbeit",
+        brief=brief,
+        roadmap_snapshot={
+            "start_situation": "Steppbewegung",
+            "stage_learning_goal": "variable Rhythmen",
+        },
+    )
+    assert offer["context_preview"]["entry_state"] == "gleichmäßige Distanz"
+    assert "Eingangszustand" in offer["goal_for_ai"]
+
+
 def test_build_gap_fill_offer_exposes_context_preview():
     brief = build_semantic_brief("Kumite Beinarbeit")
     offer = build_gap_fill_offer(

frontend/src/components/ProgressionGraphEditor.jsx

@@ -16,6 +16,7 @@ import {
 } from '../utils/exerciseAiQuickCreate'
 import {
   buildPathGapPlanningContextForAi,
+  buildSlotGapGoalForAi,
   gapOfferContextDisplayLines,
   initialStageLearningGoalFromOffer,
 } from '../utils/planningContextForExerciseAi'
@@ -527,15 +528,23 @@ export default function ProgressionGraphEditor({ graphId, embedded = false, onSa
 
   const slotOfferContext = (slotIndex) => {
     const slot = draft?.slots?.[slotIndex]
-    if (!slot) return null
+    if (!draft || !slot) return null
+    const goalForAi =
+      buildSlotGapGoalForAi(draft, slotIndex, { goalQuery: draft.goalQuery }) ||
+      slot.learning_goal
+    const priorSlot =
+      slotIndex > 0 && draft.slots[slotIndex - 1]
+        ? draft.slots[slotIndex - 1]
+        : null
     return {
       offer_id: `slot-${slotIndex}`,
       title_hint: slot.primary?.exerciseTitle || slot.learning_goal,
       roadmap_major_step_index: slot.majorStepIndex,
       phase: slot.phase,
       source: 'roadmap_unfilled',
-      goal_for_ai: slot.learning_goal,
-      sketch: slot.learning_goal,
+      goal_for_ai: goalForAi,
+      sketch: goalForAi,
+      from_title: priorSlot?.primary?.exerciseTitle || null,
     }
   }
 

frontend/src/utils/planningContextForExerciseAi.js

@@ -2,6 +2,8 @@
  * Planungs-KI Phase D: strukturierter Kontext für suggestExerciseAi.
  */
 
+import { slotsAsPathStepRows } from './progressionGraphDraft.js'
+
 export function buildPickerPlanningContextForAi({
   planningContextSummary = null,
   planningContext = null,
@@ -30,6 +32,109 @@ export function buildPickerPlanningContextForAi({
   return Object.fromEntries(Object.entries(ctx).filter(([, v]) => v != null && v !== ''))
 }
 
+function majorIndexFromStep(step) {
+  const raw = step?.roadmap_major_step_index ?? step?.roadmapMajorStepIndex
+  if (raw == null || !Number.isFinite(Number(raw))) return null
+  return Number(raw)
+}
+
+function priorPathStepsBeforeMajor(pathSteps, majorIdx) {
+  if (majorIdx == null || !Number.isFinite(Number(majorIdx))) return []
+  const mi = Number(majorIdx)
+  return (pathSteps || [])
+    .filter((s) => {
+      const idx = majorIndexFromStep(s)
+      return idx != null && idx < mi
+    })
+    .sort((a, b) => (majorIndexFromStep(a) || 0) - (majorIndexFromStep(b) || 0))
+}
+
+function stepDisplayFields(step) {
+  if (!step) return null
+  const title = String(step.title || step.exerciseTitle || '').trim()
+  const learningGoal = String(
+    step.roadmap_learning_goal || step.roadmapLearningGoal || step.learning_goal || '',
+  ).trim()
+  const phase = String(step.roadmap_phase || step.roadmapPhase || step.phase || '').trim()
+  const startState = String(step.roadmap_start_state || step.start_state || '').trim()
+  const targetState = String(step.roadmap_target_state || step.target_state || '').trim()
+  const criteria = Array.isArray(step.success_criteria)
+    ? step.success_criteria.map((x) => String(x || '').trim()).filter(Boolean).slice(0, 4)
+    : []
+  const majorStepIndex = majorIndexFromStep(step)
+  const out = {
+    title: title || null,
+    learning_goal: learningGoal || null,
+    start_state: startState || null,
+    target_state: targetState || null,
+    phase: phase || null,
+    success_criteria: criteria.length ? criteria : null,
+    major_step_index: majorStepIndex,
+  }
+  const hasData = Object.values(out).some((v) => v != null && v !== '')
+  return hasData ? out : null
+}
+
+export function buildProgressionEntryState({
+  majorStepIndex = null,
+  priorSteps = [],
+  startSituation = '',
+  currentStageStart = '',
+} = {}) {
+  const priorCompact = (priorSteps || [])
+    .map(stepDisplayFields)
+    .filter(Boolean)
+
+  const achievements = []
+  const detailLines = []
+  for (const p of priorCompact) {
+    if (Array.isArray(p.success_criteria) && p.success_criteria.length) {
+      achievements.push(...p.success_criteria)
+    } else if (p.learning_goal) {
+      achievements.push(p.learning_goal)
+    }
+
+    const labelParts = []
+    if (p.major_step_index != null) labelParts.push(`Stufe ${p.major_step_index + 1}`)
+    if (p.phase) labelParts.push(`(${p.phase})`)
+    if (p.title) labelParts.push(`„${p.title}"`)
+    const prefix = labelParts.length ? labelParts.join(' ') : 'Vorstufe'
+    const achieved =
+      p.target_state ||
+      (Array.isArray(p.success_criteria) && p.success_criteria.length
+        ? p.success_criteria.join('; ')
+        : '') ||
+      p.learning_goal ||
+      ''
+    if (achieved) detailLines.push(`${prefix}: erreicht — ${achieved}`)
+  }
+
+  let entryState = (currentStageStart || '').trim()
+  if (!entryState && priorCompact.length) {
+    const immediate = priorCompact[priorCompact.length - 1]
+    entryState =
+      immediate.target_state ||
+      (Array.isArray(immediate.success_criteria) && immediate.success_criteria.length
+        ? immediate.success_criteria.join('; ')
+        : '') ||
+      immediate.learning_goal ||
+      ''
+  } else if (!entryState && (startSituation || '').trim()) {
+    entryState = startSituation.trim()
+  }
+
+  if (priorCompact.length && (startSituation || '').trim() && !entryState) {
+    detailLines.unshift(`Ausgangsbasis Pfad: ${startSituation.trim()}`)
+  }
+
+  const out = {}
+  if (entryState) out.entry_state = entryState
+  if (detailLines.length) out.entry_state_detail = detailLines.join('\n')
+  if (priorCompact.length) out.prior_steps = priorCompact.slice(0, 6)
+  if (achievements.length) out.prior_achievements = [...new Set(achievements)].slice(0, 8)
+  return out
+}
+
 function stageSpecForMajorIndex(progressionRoadmap, majorIdx) {
   if (majorIdx == null || !progressionRoadmap) return null
   const specs = progressionRoadmap?.stage_specs
@@ -56,14 +161,25 @@ export function buildPathGapPlanningContextForAi({
   stageLearningGoalOverride = '',
   gapTrainerSupplements = '',
 } = {}) {
-  const afterIdx = Number(offer?.insert_after_index)
-  const stepA = Number.isFinite(afterIdx) && afterIdx >= 0 ? pathSteps[afterIdx] : null
-  const stepB =
-    Number.isFinite(afterIdx) && afterIdx >= 0 ? pathSteps[afterIdx + 1] : null
   const majorIdxRaw =
     offer?.roadmap_major_step_index ?? offer?.gap?.roadmap_major_step_index
   const majorIdx =
     majorIdxRaw != null && Number.isFinite(Number(majorIdxRaw)) ? Number(majorIdxRaw) : null
+  const priorSteps = majorIdx != null ? priorPathStepsBeforeMajor(pathSteps, majorIdx) : []
+  const afterIdx = Number(offer?.insert_after_index)
+  const stepA =
+    priorSteps.length > 0
+      ? priorSteps[priorSteps.length - 1]
+      : Number.isFinite(afterIdx) && afterIdx >= 0
+        ? pathSteps[afterIdx]
+        : null
+  const stepB =
+    majorIdx != null
+      ? (pathSteps || []).find((s) => majorIndexFromStep(s) === majorIdx + 1) ||
+        (Number.isFinite(afterIdx) && afterIdx >= 0 ? pathSteps[afterIdx + 1] : null)
+      : Number.isFinite(afterIdx) && afterIdx >= 0
+        ? pathSteps[afterIdx + 1]
+        : null
   const majorStep =
     majorIdx != null && editableMajorSteps[majorIdx] ? editableMajorSteps[majorIdx] : null
   const stageSpec = stageSpecForMajorIndex(progressionRoadmap, majorIdx)
@@ -92,6 +208,13 @@ export function buildPathGapPlanningContextForAi({
     )
   }
 
+  const entryState = buildProgressionEntryState({
+    majorStepIndex: majorIdx,
+    priorSteps,
+    startSituation: start,
+    currentStageStart: stageSpec?.start_state || '',
+  })
+
   const ctx = {
     source: 'progression_path_gap_fill',
     goal_query: (goalQuery || '').trim() || null,
@@ -109,6 +232,8 @@ export function buildPathGapPlanningContextForAi({
     roadmap_notes: notes,
     stage_learning_goal:
       (stageLearningGoalOverride || '').trim() || stageSpec?.learning_goal || null,
+    stage_start_state: stageSpec?.start_state || null,
+    stage_target_state: stageSpec?.target_state || null,
     gap_trainer_supplements: (gapTrainerSupplements || '').trim() || null,
     stage_phase: stageSpec?.phase || majorStep?.phase || null,
     stage_exercise_type: stageSpec?.exercise_type || null,
@@ -125,8 +250,9 @@ export function buildPathGapPlanningContextForAi({
       ? ga.success_criteria.slice(0, 4)
       : null,
     skill_hints: skillHints.length ? skillHints : null,
-    neighbor_before_title: stepA?.exerciseTitle || offer?.from_title || null,
-    neighbor_after_title: stepB?.exerciseTitle || offer?.to_title || null,
+    neighbor_before_title: stepA?.exerciseTitle || stepA?.title || offer?.from_title || null,
+    neighbor_after_title: stepB?.exerciseTitle || stepB?.title || offer?.to_title || null,
+    ...entryState,
     path_step_count: Array.isArray(pathSteps) ? pathSteps.length : 0,
     major_step_count:
       editableMajorSteps?.length ||
@@ -148,7 +274,14 @@ export function gapOfferContextDisplayLines(offer, fallbackParams = null) {
     const v = String(value || '').trim()
     if (v) lines.push({ label, value: v })
   }
-  push('Ausgangslage (Pfad)', raw.start_situation)
+  push('Eingangszustand (Vorstufen)', raw.entry_state)
+  if (raw.entry_state_detail && raw.entry_state_detail !== raw.entry_state) {
+    push('Bisheriger Pfad', raw.entry_state_detail)
+  }
+  if (Array.isArray(raw.prior_achievements) && raw.prior_achievements.length) {
+    push('Erreichte Voraussetzungen', raw.prior_achievements.slice(0, 6).join(' · '))
+  }
+  push('Ausgangslage (gesamter Pfad)', raw.start_situation)
   push('Gesamtziel (Pfad)', raw.target_state)
   push('Ergänzungen', raw.roadmap_notes)
   push('Stufen-Lernziel', raw.stage_learning_goal || raw.roadmap_learning_goal)
@@ -173,6 +306,43 @@ export function gapOfferContextDisplayLines(offer, fallbackParams = null) {
   return lines
 }
 
+/** Zieltext für KI aus Slot-Kontext (Graph-Editor ohne API-Offer). */
+export function buildSlotGapGoalForAi(draft, slotIndex, { goalQuery = '' } = {}) {
+  const slot = draft?.slots?.[slotIndex]
+  if (!slot) return ''
+  const pathSteps = slotsAsPathStepRows(draft)
+  const majorIdx = slot.majorStepIndex
+  const priorSteps = priorPathStepsBeforeMajor(pathSteps, majorIdx)
+  const start = (draft.startSituation || '').trim()
+  const stageSpec =
+    majorIdx != null && draft.progressionRoadmap
+      ? stageSpecForMajorIndex(draft.progressionRoadmap, majorIdx)
+      : null
+  const entry = buildProgressionEntryState({
+    majorStepIndex: majorIdx,
+    priorSteps,
+    startSituation: start,
+    currentStageStart: stageSpec?.start_state || '',
+  })
+  const parts = [
+    goalQuery ? `Planungsziel (gesamter Pfad): ${goalQuery}` : '',
+    entry.entry_state
+      ? `Eingangszustand (erreichte Voraussetzungen): ${entry.entry_state}`
+      : start
+        ? `Ausgangslage (Pfad): ${start}`
+        : '',
+    entry.entry_state_detail && entry.entry_state_detail !== entry.entry_state
+      ? `Bisheriger Pfad:\n${entry.entry_state_detail}`
+      : '',
+    (slot.learning_goal || '').trim()
+      ? `Lernziel dieser Roadmap-Stufe: ${(slot.learning_goal || '').trim()}`
+      : '',
+    (slot.phase || '').trim() ? `Entwicklungsphase: ${slot.phase}` : '',
+    'Die Übung baut didaktisch auf den Vorstufen auf — Voraussetzungen explizit benennen, messbares Stufenziel.',
+  ].filter(Boolean)
+  return parts.join('\n\n').trim()
+}
+
 export function initialStageLearningGoalFromOffer(offer, fallbackParams = null) {
   const lines = gapOfferContextDisplayLines(offer, fallbackParams)
   const hit = lines.find((l) => l.label === 'Stufen-Lernziel')

frontend/src/utils/progressionGraphDraft.js

@@ -280,9 +280,15 @@ export function slotsAsPathStepRows(draft) {
   return (draft.slots || []).map((slot) => ({
     exerciseId: slot.primary?.exerciseId ?? null,
     exerciseTitle: slot.primary?.exerciseTitle || '',
+    title: slot.primary?.exerciseTitle || '',
+    roadmap_major_step_index: slot.majorStepIndex,
     roadmapMajorStepIndex: slot.majorStepIndex,
+    roadmap_phase: slot.phase,
     roadmapPhase: slot.phase,
+    roadmap_learning_goal: slot.learning_goal,
     roadmapLearningGoal: slot.learning_goal,
+    learning_goal: slot.learning_goal,
+    success_criteria: Array.isArray(slot.success_criteria) ? slot.success_criteria : [],
     isAiProposal: slot.primary?.kind === 'proposal',
   }))
 }