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- Updated the `CLAUDE.md` to reflect changes in the Progression Graph, including the new Ist-Stand and roadmap specifications. - Enhanced `PLANNING_EXERCISE_SUGGEST_CONTEXT.md` with detailed descriptions of the current state and features of the planning exercise. - Revised `PLANNING_PROGRESSION_ROADMAP_SPEC.md` to document the implementation status of various phases and their corresponding migrations. - Incremented application version to 0.8.217 to incorporate recent updates and improvements in the planning context and roadmap functionalities.
125 lines
5.2 KiB
Markdown
125 lines
5.2 KiB
Markdown
# Planungs-KI — Produkt-Roadmap
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**Stand:** 2026-05-22
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**App-Version:** **0.8.217** — maßgeblich `backend/version.py`
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Diese Roadmap ergänzt die **Architektur-Refaktor-Roadmap** (`UMSETZUNGSPLAN_ROADMAP.md`) und gilt **nur für KI-gestützte Trainingsplanungsunterstützung**.
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**Ist-Stand Progressionsgraph (detailliert):** `PLANNING_PROGRESSION_GRAPH_KI.md`
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**Leit-Spec:** `.claude/docs/working/PLANNING_PROGRESSION_ROADMAP_SPEC.md`
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---
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## Strategische Entscheidung (verbindlich)
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1. **Progressionsgraph:** Planung **vom Ziel rückwärts** (Roadmap-first), nicht Bibliothek-first.
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2. **Keine Gruppenanalyse** im Graphen — Kontext = Zieltext, Thema, Schrittanzahl, optional Graph-Kanten.
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3. **Trainingsplanung** (Einheit, Rahmen, Abschnitt): eigene Pipeline später, **mit** Gruppenkontext — siehe `AI_PLANNING_KI_MULTISTAGE_FORECAST.md` S0–S4.
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4. **Orchestrierung:** Workflow-**lite** jetzt (`planning_progression_roadmap.py`); Mitai Workflow-Engine **später**, wenn 2–3 Pipelines stabil sind.
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---
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## Phasen-Übersicht
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| Phase | Domäne | Kurzbeschreibung | Status |
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|-------|--------|------------------|--------|
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| P0–P2 | Übungssuche | Kontext-Pack, Hybrid-Score, LLM-Rerank | ✅ |
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| A–C2 | Übungssuche | Voll-Library, Graph, Varianten | ✅ |
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| C3 | Progressionsgraph | Pfad-Builder (retrieval-first) | ✅ |
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| E–E3 | Progressionsgraph | Semantik, QA, Lücken-Angebote | ✅ |
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| **F0–F4** | Progressionsgraph | Roadmap-Pipeline, LLM, roadmap-first, UI Review | ✅ **0.8.204–209** |
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| **F5–F9** | Progressionsgraph | Start/Ziel, Gap-Prep, Skill-Expectations, Persistenz | ✅ **0.8.210–217** |
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| D | Übungs-Neuanlage | `planning_context` an `suggestExerciseAi` | ✅ **0.8.208** |
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| **UX** | Progressionsgraph | Wizard/Stepper statt Scroll-UI | 🔲 |
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| G | Trainingsplanung | Kontext-Pack Gruppe/Historie, S0–S4 | 🔲 |
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| H | Plattform | Mitai-Workflow-Engine (optional) | 🔲 Backlog |
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---
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## Phase F — Progressions-Roadmap (aktiver Fokus)
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### F0 — Foundation (0.8.204)
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- [x] Spec `PLANNING_PROGRESSION_ROADMAP_SPEC.md`
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- [x] Modul `planning_progression_roadmap.py` (Pydantic, Pipeline-Skeleton)
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- [x] Migration **078** Prompt-Slugs (Zielanalyse, Roadmap)
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- [x] API: `include_roadmap_preview` auf `progression-path-suggest`
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- [x] Doku: HANDOVER, PLANNING_EXERCISE_SUGGEST_CONTEXT, MULTISTAGE_FORECAST
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### F1 — Deterministische Roadmap
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- [x] Phase A aus Semantic Brief
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- [x] Phase B: `micro_objectives` aus `development_arc` + Konsolidierung auf N
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- [x] Phase C: heuristische `stage_specs`
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- [ ] pytest für Konsolidierung
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### F2 — LLM Roadmap (0.8.205)
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- [x] Prompts **078/079** in `ai_prompts` — Code nur Slugs (`PROMPT_SLUG_*`)
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- [x] `include_llm_roadmap` + `load_and_render_ai_prompt` + JSON-Validierung
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- [x] Deterministischer Fallback wenn Prompt/OpenRouter fehlt
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- [ ] Response/UI: genutzte `prompt_slugs` sichtbar machen (Admin-Hinweis)
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### F3 — roadmap-first (0.8.206)
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- [x] Retrieval pro `major_step` + `stage_spec` statt iterativem Pfad-Bau
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- [x] Gap-Angebote für unbesetzte Roadmap-Stufen (`roadmap_unfilled`)
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- [x] QA/Lücken an Roadmap gekoppelt (`roadmap_first_lite`: keine Brücken/Reorder zwischen Major Steps)
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### F4 — UI (0.8.207)
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- [x] Roadmap-Review im `ExerciseProgressionPathBuilder`
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- [x] Major Steps editierbar (Phase, Lernziel, Reihenfolge) vor Übungs-Match
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- [x] API `roadmap_only` + `roadmap_override`
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### F5 — Start/Ziel (0.8.210–214)
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- [x] Strukturierte Felder `start_situation`, `target_state`, `roadmap_notes`
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- [x] Prompt **087** `planning_progression_start_target`
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- [x] Priorität: Trainer > KI > Regex (`resolve_roadmap_structured_input`)
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- [x] Zwei-Schritt-UI: „Start/Ziel analysieren“ / „Roadmap vorschlagen“
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### F6 — Gap-KI-Kontext (0.8.212–214)
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- [x] `ExerciseGapFillPrepModal` vor KI-Call
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- [x] `planning_exercise_form_context.py` — Gap-Snapshot, `context_preview`
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- [x] Migration **085** — `planning_context` in Übungs-Prompts
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### F7 — Fähigkeiten-Scoring (0.8.215–216)
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- [x] `planning_skill_expectations.py` (Scopes: `progression_stage`, `progression_path`)
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- [x] Pro-Stufe-Retrieval + `path_skill_expectations` + UI-Tags
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- [x] `expected_skills` in Gap-Fill
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### F8 — Stufen-Details UI (0.8.216)
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- [x] Editierbare `stage_specs` in `roadmap_override` (Belastung, Erfolgskriterien, Vermeiden)
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### F9 — Persistenz (0.8.217)
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- [x] Migration **088** — `planning_roadmap` JSONB am Graph
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- [x] Laden/Speichern über `GET/PUT` Graph + Sequenz-Endpoint
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### UX — UI-Überarbeitung (offen)
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- [ ] Wizard mit 4 Schritten (Ziel → Roadmap → Match → Lücken)
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- [ ] Progressive disclosure — Details in Panels, nicht alles gleichzeitig
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- [ ] Briefing: `PLANNING_PROGRESSION_GRAPH_KI.md` §10
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---
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## Abhängigkeiten
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| Von | Nach | Hinweis |
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|-----|------|---------|
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| F2 | Enrichment / Skills | Bessere Roadmap bei technikspezifischen Skills |
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| F3 | F2 | LLM-Roadmap oder stabile heuristische B |
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| G | F4 | Trainingsplanung kann Roadmap aus Graph referenzieren |
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| H | G + F4 | Workflow-Engine lohnt bei verzweigten Planungsflows |
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---
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## Pflege
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Bei Abschluss einer Teilphase: **`PLANNING_PROGRESSION_GRAPH_KI.md`** (Ist-Stand), diese Datei, `HANDOVER.md` §2.8, `PLANNING_EXERCISE_SUGGEST_CONTEXT.md` §24, Changelog in `version.py`.
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