Ralphinho RFC Pipeline

SkillAI & models

RFC-driven multi-agent DAG execution pattern with quality gates, merge queues, and work unit orchestration.

Available today. Use it from your connected AI after setup.

Connect ahel once, and every AI you use reads what you have installed.

Then ask your AI: use the Ralphinho RFC Pipeline skill

What this skill tells your AI

The instructions your AI receives, as published by jamkris/everything-gemini-code in skills/ralphinho-rfc-pipeline/SKILL.md and read by ahel’s review.

Inspired by 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
  • retry with updated constraints

Outputs

  • RFC execution log
  • unit scorecards
  • dependency graph snapshot
  • integration risk summary

Signals

GitHub stars
87
Forks
22
Last commit
May 2026
Advanced
Catalog kind
skill
Gateway key
ralphinho-rfc-pipeline
Source
github.com/jamkris/everything-gemini-code