RFE Creator

Claude Code skills for creating, reviewing, and submitting RFEs to the RHAIRFE Jira project.

Inspired by the PRD/RFE workflow in ambient, which established the pipeline pattern and multi-perspective review concept.

Quick Start

# RFE Pipeline
/rfe.create     # Write a new RFE from a problem statement
/rfe.review     # Review, improve, and auto-revise RFEs
/rfe.split      # Split an oversized RFE into right-sized pieces
/rfe.submit     # Submit new or update existing RFEs in Jira
/rfe.speedrun   # Full pipeline end-to-end with minimal interaction
/rfe.auto-fix   # Batch review+revise+split pipeline (non-interactive)

# Improve an existing Jira RFE
/rfe.review RHAIRFE-1234      # Fetch, review, and auto-revise
/rfe.split RHAIRFE-1234       # Fetch and split an oversized RFE
/rfe.speedrun RHAIRFE-1234    # Fetch, review, revise, and update in one step

# Batch operations
/rfe.speedrun --input batch.yaml --headless --dry-run              # Batch create + review from YAML
/rfe.speedrun --input batch.yaml --headless --announce-complete    # Print completion marker for CI
/rfe.auto-fix --jql "project = RHAIRFE AND ..."         # Batch review from JQL query
/rfe.auto-fix RHAIRFE-1234 RHAIRFE-5678                 # Batch review explicit IDs

# Maintenance
/rfe-creator.update-deps   # Force update vendored dependencies

Pipeline

New RFEs

/rfe.create → /rfe.review → /rfe.submit

/rfe.review auto-revises issues it finds (up to 2 cycles). You can also edit artifacts manually between steps.

/rfe.speedrun runs the full pipeline with reasonable defaults and minimal interaction.

Existing Jira RFEs

/rfe.review RHAIRFE-1234 → /rfe.submit

Or in one step: /rfe.speedrun RHAIRFE-1234

Batch Operations

Create and review multiple RFEs from a YAML file:

/rfe.speedrun --headless --dry-run --input batch.yaml

YAML format:

- prompt: "Users need to verify model signatures at serving time"
  priority: Critical
  labels: [candidate-3.5]
- prompt: "TrustyAI operator crashes on large clusters"
  priority: Major

Review a batch of existing Jira RFEs:

/rfe.auto-fix --jql "project = RHAIRFE AND status = New" --limit 20
/rfe.auto-fix RHAIRFE-1234 RHAIRFE-5678 RHAIRFE-9012

Auto-fix processes in batches (default 5), handles review, revision, splitting, retry, and report generation.

Strategy Pipeline

The strategy skills have moved to a dedicated repo: ederign/strat-creator.

Pipeline Steps

1. Create: Describe your need. The skill asks clarifying questions and produces RFEs. Supports --headless to skip questions (for batch/CI use).
2. Review: Scores RFEs against the assess-rfe rubric, checks technical feasibility, and auto-revises issues. Accepts Jira keys to review existing RFEs. Supports --headless for non-interactive use.
3. Split: Decompose an oversized RFE into right-sized pieces. Runs review on new RFEs, self-corrects right-sizing (up to 3 cycles), and checks scope coverage. Supports --headless.
4. Auto-fix: Batch pipeline that orchestrates review + revision + split + retry across many RFEs. Accepts explicit IDs or a --jql query. Processes in configurable batches (--batch-size N, default 5). Generates run reports and HTML review reports.
5. Submit: Creates new RHAIRFE tickets or updates existing ones in Jira. Supports --dry-run to validate without writing to Jira.
6. Speedrun: End-to-end pipeline (create → auto-fix → submit). Supports --input <yaml> for batch creation, --headless for CI, --announce-complete for completion signaling, --dry-run to skip Jira writes, and --batch-size N.

Editing Between Steps

All artifacts are written to artifacts/. You can edit any file between steps:

• Edit an RFE in artifacts/rfe-tasks/RFE-001-*.md, then re-run /rfe.review
• Re-run /rfe.create to start over from scratch

assess-rfe Integration

Skills automatically bootstrap the assess-rfe plugin from GitHub on first use:

• During creation: The rubric is exported to artifacts/rfe-rubric.md and used to guide clarifying questions.
• During review: /rfe.review invokes assess-rfe for rubric scoring.
• Without network access: The skills still work — creation uses built-in questions, review runs only the technical feasibility check.

Run /rfe-creator.update-deps to force-refresh to the latest version.

Architecture Context

For RHOAI work, the technical feasibility and strategy reviews use architecture context from opendatahub-io/architecture-context. This is fetched automatically via sparse checkout on first use.

Jira Integration

Submission uses the Jira REST API directly via Python scripts (not the MCP server). Set these environment variables:

export JIRA_SERVER=https://your-site.atlassian.net
export JIRA_USER=your-email@example.com
export JIRA_TOKEN=your-api-token

The Atlassian MCP server is used for read operations (fetching issues, comments) when available, with a REST API fallback.

CI / Headless Mode

All orchestrator skills support --headless for non-interactive use in CI pipelines. Combined with --dry-run, you can validate the full pipeline without Jira writes:

claude -p "/rfe.speedrun --headless --dry-run --input batch.yaml"

Add --announce-complete to print a FULL RUN COMPLETE marker when the pipeline finishes — useful for CI harnesses that need a reliable completion signal:

claude -p "/rfe.speedrun --headless --announce-complete --input batch.yaml"

Flag persistence: parsed arguments are written to tmp/*.yaml config files so they survive context compression during long batch runs.