Ralphinho RFC Pipeline
SkillAI & modelsRFC-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.
No other account needed.
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
- RFC intake
- DAG decomposition
- Unit assignment
- Unit implementation
- Unit validation
- Merge queue and integration
- Final system verification
Unit Spec Template
Each work unit should include:
iddepends_onscopeacceptance_testsrisk_levelrollback_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
- research
- implementation plan
- implementation
- tests
- review
- 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