Automates the full lifecycle of RFEs: guides creation with clarifying questions, reviews for architectural correctness, feasibility, scope, and testability, splits oversized items, and submits to Jira — with batch processing and headless CI support.
Submit or update RFEs in Jira. Creates new RHAIRFE tickets for new RFEs, or updates existing tickets for RFEs fetched from Jira. Use after /rfe.review.
Reviews strategy features for scope — is each strategy right-sized, does the effort match the scope, should anything be split?
Reviews strategy features for architectural correctness — dependencies, integration patterns, component interactions.
Reviews strategy features for technical feasibility, implementation complexity, and effort estimate credibility.
Force update all vendored dependencies — assess-rfe skills and architecture context. Use when you want the latest versions.
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Sign in to claimBased on adoption, maintenance, documentation, and repository signals. Not a security audit or endorsement.
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.
# 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
/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.
/rfe.review RHAIRFE-1234 → /rfe.submit
Or in one step: /rfe.speedrun RHAIRFE-1234
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.
The strategy skills have moved to a dedicated repo: ederign/strat-creator.
--headless to skip questions (for batch/CI use).--headless for non-interactive use.--headless.--jql query. Processes in configurable batches (--batch-size N, default 5). Generates run reports and HTML review reports.--dry-run to validate without writing to Jira.--input <yaml> for batch creation, --headless for CI, --announce-complete for completion signaling, --dry-run to skip Jira writes, and --batch-size N.All artifacts are written to artifacts/. You can edit any file between steps:
artifacts/rfe-tasks/RFE-001-*.md, then re-run /rfe.review/rfe.create to start over from scratchSkills automatically bootstrap the assess-rfe plugin from GitHub on first use:
artifacts/rfe-rubric.md and used to guide clarifying questions./rfe.review invokes assess-rfe for rubric scoring.Run /rfe-creator.update-deps to force-refresh to the latest version.
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.
npx claudepluginhub ikredhat/skills-registry --plugin rfe-creatorDeveloper productivity tools for Python packaging, CI/CD debugging, and workflow automation. Includes skills for analyzing package build complexity, resolving dependencies, finding licenses, debugging GitLab pipelines, reviewing ADRs, and more.
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Site generation skills for the OpenDataHub Skills Registry
Assess RFEs against quality criteria using a structured rubric
Orchestrator skills, agent prompts, and state management for the Jira autofix pipeline
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