You are helping me build the following AI agent workflow.

## Goal
Jira Ticket Quality Review and Coaching: Monitors new Jira Stories, Bugs, and Tasks for quality, posting coaching comments only when scores fall below a configurable threshold.

## Specification
- What it does: Get immediate, actionable coaching comments on your Jira Stories, Bugs, and Tasks when their quality score drops below your set threshold. The system automatically analyzes new tickets using OpenRouter to provide structured feedback that helps you improve ticket clarity before they move forward.
- Trigger: Triggered by an event (Event · on new Jira issue)
- Autonomy: Fully hands-off
- Expected setup effort: about an afternoon
- Tools/services involved:
  - Jira Software: Trigger and comment posting
  - OpenRouter Chat Model: Quality evaluation and scoring
  - Code: Parsing JSON response

## Known pitfalls, handle each one explicitly in your implementation
1. Long ticket descriptions may exceed context windows, truncate or summarize input before sending to OpenRouter.
2. Ambiguous scoring criteria lead to inconsistent results, define explicit weighted rules in the system prompt.

## Reference implementation
https://n8n.io/workflows/16693 (workflow template)
Fetch and inspect this before building. If it matches my stack, adapt it;
if not, rebuild the pattern with my tools.

## Process requirements
1. Before building: ask me which of the listed tools I actually use and
   what my platform is (n8n / Make / code / other). Do not assume.
2. Adapt the pattern to my answers; do not force the reference stack.
3. Address every pitfall above; tell me how you handled each.
4. Provide a test plan I can run before letting this touch real data.
5. Ask before any step that sends messages, modifies data, or spends money.

Source: https://usecasesforagents.com/use-case/jira-ticket-quality-review-and-coaching/ via usecasesforagents.com