ralphinho-rfc-pipeline
RFC-driven multi-agent DAG execution pattern with quality gates, merge queues, and work unit orchestration.
npx skills add mturac/everything-openai-codex --skill ralphinho-rfc-pipeline --agent codex
Same command for any agent — swap --agent for claude-code, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Ralphinho RFC Pipeline Inspired by [humanplane](https://github.com/humanplane) style RFC decomposition patterns and multi-unit orchestration workflows. Use this skill when a feature is too large for a single agent pass and must be split into independently verifiable work units. ## Pipeline Stages 1. RFC intake 2. DAG decomposition 3. Unit assignment 4. Unit implementation 5. Unit validation 6. Merge queue and integration 7. Final system verification ## Unit Spec Template Each work unit should include: - `id` - `depends_on` - `scope` - `acceptance_tests` - `risk_level` - `rollback_plan` ## Complexity Tiers - Tier 1: isolated file edits, deterministic tests - Tier 2: multi-file behavior changes, moderate integration risk - Tier 3: schema/auth/perf/security changes ## Quality Pipeline per Unit 1. research 2. implementation plan 3. implementation 4. tests 5. review 6. merge-ready report ## Merge Queue Rules - Never merge a unit with unresolved dependency failures. - Always rebase unit branches on latest integration branch. - Re-run integration tests after each queued merge. ## Recovery If a unit stalls: - evict from active queue - snapshot findings - regenerate narrowed unit scope -
- Pipeline Stages
- Unit Spec Template
- Complexity Tiers
- Quality Pipeline per Unit
- Merge Queue Rules
- Recovery
- Outputs
What does the ralphinho-rfc-pipeline skill do?
RFC-driven multi-agent DAG execution pattern with quality gates, merge queues, and work unit orchestration.
How do I install it?
Run `npx skills add mturac/everything-openai-codex --skill ralphinho-rfc-pipeline --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From mturac/everything-openai-codex, a repository with 84 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.