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RFC駆動の複数エージェントDAG実行パターン、品質ゲート、マージキュー、ワークユニットオーケストレーション。
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Based on SOC occupation classification
Instinct-based learning system that observes sessions via hooks, creates atomic instincts with confidence scoring, and evolves them into skills/commands/agents. v2.1 adds project-scoped instincts to prevent cross-project contamination.
Suggests manual context compaction at logical intervals to preserve context through task phases rather than arbitrary auto-compaction.
Suggests manual context compaction at logical intervals to preserve context through task phases rather than arbitrary auto-compaction.
Best practices for Remotion - Video creation in React. 29 domain-specific rules covering 3D, animations, audio, captions, charts, transitions, and more.
Verification loop for Django projects: migrations, linting, tests with coverage, security scans, and deployment readiness checks before release or PR.
Project-specific skill template covering architecture, patterns, testing, and deployment guidance.
| name | ralphinho-rfc-pipeline |
| description | RFC駆動の複数エージェントDAG実行パターン、品質ゲート、マージキュー、ワークユニットオーケストレーション。 |
| origin | ECC |
humanplane スタイルのRFC分解パターンと複数ユニットオーケストレーションワークフローにインスパイア。
単一エージェントパスでは大きすぎる機能を独立して検証可能なワークユニットに分割する必要がある場合、このスキルを使用します。
各ワークユニットに含める：
iddepends_onscopeacceptance_testsrisk_levelrollback_planユニットが stall した場合：