القائمة
RFC-driven multi-agent DAG execution pattern with quality gates, merge queues, and work unit orchestration.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
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-driven multi-agent DAG execution pattern with quality gates, merge queues, and work unit orchestration. |
| metadata | {"origin":"ECC"} |
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.
Each work unit should include:
iddepends_onscopeacceptance_testsrisk_levelrollback_planIf a unit stalls: