feat: archive zoo_backup for home sync

2026-06-24 19:27:05 +02:00
parent 02844e4c4a
commit 038e546963
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+# Skill: generate-assessment
+
+Structured assessment document from Jira ticket and code analysis.
+
+## Invoked by
+
+📋 Planner mode
+
+## Required Inputs
+
+| Input | Source | Example |
+|-------|--------|---------|
+| `TICKET_KEY` | Jira issue key | `ESIDEPAISY-12081` |
+| `MODULE` | PAISY module name | `eau`, `eubp`, `svmeldungen` |
+
+## Output
+
+Markdown file: `docs/<MODULE>/<TICKET_KEY>/<TICKET_KEY>-assessment.md`
+
+## Steps
+
+### 1. Gather context from BigMind
+
+```python
+memory_search_facts("<MODULE> <relevant keywords>")
+memory_search_chunks("<MODULE> <relevant keywords>")
+memory_search_semantic("<natural language description of the problem>")
+```
+
+### 2. Read Jira ticket
+
+```python
+retrieve_ticket_details(TICKET_KEY)
+# Extract: summary, description, acceptance criteria (customfield_10510)
+# Note the Feature Link (customfield_10001) for traceability
+```
+
+### 3. Search ADP Wiki for domain context
+
+```python
+# Check BigMind index first
+memory_search_facts("<domain topic> adpdocs")
+
+# If page ID known, fetch directly
+set-wiki(uri="mcp://wikis/adpdocs.de.adp.com")
+get-page(title="<relevant page>")
+
+# If not known, search
+search-page(query="<domain keywords>")
+```
+
+### 4. Search Confluence for prior decisions
+
+```python
+search_confluence_by_cql("text ~ '<TICKET_KEY>' OR text ~ '<topic keywords>'")
+```
+
+### 5. Analyze affected source code
+
+- Identify the module entry point: `java/modules/cs-modules/<MODULE>/`
+- Read key classes mentioned in the ticket
+- Trace the data flow through the affected components
+- Note patterns used: AbstractMeldung, Datenbaustein, ServiceCenter, EMFactory, JAXB, etc.
+
+### 6. Generate assessment document
+
+Write `docs/<MODULE>/<TICKET_KEY>/<TICKET_KEY>-assessment.md` with this structure:
+
+```markdown
+# Assessment: <TICKET_KEY> — <Summary>
+
+**Datum:** <today>
+**Modul:** <MODULE>
+**Autor:** Patrick Plate / Roo (Planner)
+**Status:** Entwurf v1
+
+---
+
+## 1. Problemanalyse
+
+<What is the problem? Why does it need to be solved? Reference Jira ticket.>
+
+## 2. Betroffene Komponenten
+
+| Komponente | Pfad | Rolle |
+|-----------|------|-------|
+| <Class> | <path> | <what it does> |
+
+## 3. Ist-Zustand
+
+<How does the current code work? Data flow, key methods, patterns used.>
+
+## 4. Risikobewertung
+
+| Risiko | Wahrscheinlichkeit | Auswirkung | Mitigation |
+|--------|-------------------|------------|------------|
+| <risk> | Hoch/Mittel/Niedrig | <impact> | <mitigation> |
+
+## 5. Lösungsoptionen
+
+### Option A: <name>
+- **Beschreibung:** ...
+- **Vorteile:** ...
+- **Nachteile:** ...
+- **Aufwand:** ...
+
+### Option B: <name>
+- **Beschreibung:** ...
+- **Vorteile:** ...
+- **Nachteile:** ...
+- **Aufwand:** ...
+
+## 6. Empfehlung
+
+<Which option and why. Reference PAISY patterns, domain constraints, prior art.>
+
+## 7. Offene Fragen
+
+- [ ] <question 1>
+- [ ] <question 2>
+```
+
+### 7. Present to user
+
+- Show the assessment summary
+- Explicitly ask: **"Assessment v1 erstellt. GO / Feedback?"**
+- On feedback: revise and increment version (v2, v3, ...)
+
+### 8. Store findings in BigMind
+
+```python
+memory_store_fact(
+    category="codebase",
+    fact=f"{TICKET_KEY}: Assessment completed. <key finding summary>"
+)
+memory_append_chunk(
+    session_id=SESSION_ID,
+    content=f"Assessment for {TICKET_KEY}: <detailed findings>",
+    flag_reason="assessment findings"
+)
+```
+
+## Language
+
+- Document content: **German** (PAISY domain convention)
+- Technical terms (class names, patterns, tools): keep as-is in English
+- Section headers: German
+
+## Conventions
+
+- Date format: `dd.MM.yyyy` (German)
+- Version tracking: v1, v2, v3 in the Status field
+- File location: always under `docs/<MODULE>/<TICKET_KEY>/`
+- Reference existing plan docs if they exist (check `ls docs/<MODULE>/<TICKET_KEY>/`)