# 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>/`)