prioritize

name	prioritize
description	Analyzes backlog and suggests fix order using AI prioritization
allowed-tools	mcp__*, Read, Glob, Grep, Task
argument-hint	[--limit <N>] [--output <path>]

Prioritize

Input: $ARGUMENTS = optional --limit <N> (default: 20), --output <path> (default: stdout)

Configuration

Read Automation Config from CLAUDE.md section ## Automation Config:

• Issue Tracker: Type, Instance, Project, Bug query
• Optional: Feature Workflow → Feature query
• Optional: Metrics → for historical data

0. MCP pre-flight check

Before any pipeline operation, verify MCP tool availability:

• Read Type from Automation Config (Issue Tracker section)
• Check that at least one mcp__* tool matching the tracker type is accessible
• If not accessible → STOP with: "Cannot connect to your {Type} issue tracker. Is the {Type} integration configured? Run /agent-flow:check-setup for diagnostics."

Orchestration

1. Fetch issues

Via MCP server (per Issue Tracker → Type), fetch open issues (Bug query + Feature query). Limit = --limit flag.

2. Enrich with history

If a metrics report exists (./reports/metrics.md or Metrics → Output from config), read per-area failure patterns and success rates.

3. Run priority-engine

Before dispatch, check Agent Overrides: follow ../../core/agent-override-injector.md for priority-engine overrides. You MUST invoke Task(subagent_type='agent-flow:priority-engine', model='opus'). DO NOT inline-execute. Context: list of issues + historical data (if available).

If priority-engine fails or returns an error, display: "Prioritization failed: {reason}" and stop.

4. Output

Display the agent's result. If --output is specified → write to file.

Rules

• Read-only — no changes to the issue tracker
• If no issues found → "No open issues found matching the query"
• Data is read via MCP servers