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rfe-creator

A comprehensive Claude Code skill suite for the full lifecycle of Requests for Enhancement (RFEs) in the RHAIRFE Jira project. Covers creation from problem statements, multi-phase rubric-based review with technical feasibility checks and auto-revision, intelligent splitting of oversized RFEs, batch auto-fix at scale, and deterministic submission to Jira. A speedrun skill chains the whole pipeline end-to-end (create → auto-fix → submit) for a single idea, a set of existing Jira keys, or a YAML batch of ideas.

The plugin uses a shared artifact convention -- all skills read from and write to an artifacts/ directory, with YAML frontmatter (managed exclusively via scripts/frontmatter.py) carrying structured metadata on every task and review file. Jira write operations go through deterministic Python scripts (REST API + Basic Auth) rather than LLM tool-calling, so the exact sequence of API calls is reproducible; read operations prefer the Atlassian MCP server and fall back to the REST API. Long-running orchestrators persist state to tmp/ via scripts/state.py so they survive context-compression boundaries. A dependency on the assess-rfe plugin provides the scoring rubric, bootstrapped automatically on first use.

Plugin Details

Pipeline

rfe-creator pipeline

Dependencies

Skills

Skill Description Invocable
/rfe.create Generate new RFEs from problem statements
/rfe.review Score and improve RFEs with auto-revision
/rfe.split Decompose oversized RFEs into appropriately-scoped pieces
/rfe.submit Push RFEs to Jira
/rfe.speedrun Execute the full RFE pipeline end-to-end
/rfe.auto-fix Batch review, revise, and split operations
/rfe-creator.update-deps Update vendored dependencies
/architecture-review Reviews strategy features for architectural correctness — dependencies, integration patterns, component interactions internal
/feasibility-review Reviews strategy features for technical feasibility and effort estimate credibility internal
/rfe-feasibility-review Reviews RFEs for technical feasibility, blockers, and strategy alignment internal
/scope-review Reviews strategy features for right-sizing and bounded scope internal
/testability-review Reviews strategy features for testability and measurable acceptance criteria internal

Installation

Claude Code

/plugin install rfe-creator@opendatahub-skills

OpenAI Codex — add the marketplace, then enable rfe-creator from the /plugins browser:

codex plugin marketplace add opendatahub-io/skills-registry

Architecture

The RFE skills (rfe.*) form the requirements pipeline. rfe.speedrun is the top-level orchestrator: it invokes rfe.create, rfe.auto-fix, and rfe.submit as sub-skills and never duplicates their work, persisting the ID list and flags between phases so the run is resumable.

rfe.review is the central review orchestrator and is deliberately content-blind -- it never reads RFE bodies into its own context. Instead it launches parallel waves of sub-agents (fetch, assess, feasibility, review, revise), reads only YAML frontmatter via scripts/frontmatter.py, checks file existence via Glob, and polls for wave completion with scripts/check_review_progress.py (sleeping for the reported NEXT_POLL interval). Rubric assessment is delegated to the assess-rfe plugin (a dedicated rfe-scorer subagent), and per-RFE technical feasibility is delegated to the rfe-feasibility-review sub-agent. Failing RFEs are auto-revised and re-assessed for up to two cycles.

rfe.auto-fix wraps the same building blocks into a non-interactive pipeline state machine (scripts/pipeline_state.py) with phased dispatch (fetch → bootstrap → assess → feasibility → review → revise → re-assess → split). It drives a strict next-action / launch_wave / wait-for-wave loop, processes IDs in configurable batches, and supports snapshot-based incremental fetch (scripts/snapshot_fetch.py) for resume and reprocessing. rfe.split decomposes oversized RFEs via parallel split agents, re-reviews the children through rfe.review, and runs a one-cycle right-sizing self-correction loop.

Review artifacts follow a fixed layout under artifacts/ (rfe-tasks/, rfe-originals/, rfe-reviews/), and scripts/frontmatter.py rebuild-index regenerates rfes.md. Architecture context is fetched from opendatahub-io/architecture-context into .context/architecture-context/ and used by the feasibility fork to ground assessments in real platform components and APIs; human-authored overlays under overlays/ take precedence over the generated docs. Note that architecture-review, feasibility-review, scope-review, and testability-review are forked strategy reviewers (they read artifacts/strat-tasks/ and assess refined strategy features) shared with the strategy workflow; rfe-feasibility-review is the RFE-specific reviewer wired into rfe.review.