Scan signals, evaluate gaps, and build cabinet SKILL.md files.
npx skills add https://github.com/orenmagid/claude-cabinet --skill seed-orenmagid
Please help me install this Agent Skill. Skill: seed Source: https://github.com/orenmagid/claude-cabinet/tree/main/templates/skills/seed Command: npx skills add https://github.com/orenmagid/claude-cabinet --skill seed-orenmagid
Seed solves the problem of systematically building and maintaining a tailored cabinet of domain experts for a project. Activated during seed to recruit new cabinet members. It helps teams ensure expertise is available when needed, rather than reactive hiring after incidents.
Run /seed to detect gaps, evaluate coverage, and collaboratively build cabinet members for your project.
High-intent search queries and answers about installing and using this skill.
Building a team of domain experts involves scanning project signals, evaluating coverage gaps, and collaboratively authoring expert profiles. This phase-driven approach ensures expertise is proactively recruited during onboarding rather than reactively added after incidents.
Maintaining project expertise coverage requires ongoing evaluation of technical signals and risk gaps against existing domain experts. A collaborative lifecycle process updates expert profiles to keep governance and technical alignment current throughout development.
Identifying domain expertise gaps during onboarding involves scanning project signals and evaluating existing coverage. This detects missing technical knowledge and governance requirements, allowing teams to target and build specific expert profiles.
Yes, you can co-create custom expert profiles with governance hooks. Teams collaboratively author standardized template files that reflect project-specific risks and integrate with committee lifecycle guidance to manage domain expertise.
Collaborative cabinet creation works with ongoing development phases by applying a phase-driven workflow. It continuously scans signals and evaluates gaps to keep domain expertise aligned with evolving technology and project risks.
A phase-driven workflow for building domain expertise prevents knowledge gaps by systematically scanning signals and evaluating coverage. This proactive approach ensures expertise is available before incidents occur, unlike reactive hiring.
OneWave-AIAssemble 2-3 expert personas to collaboratively analyze any topic.
NeuroAIHubGuides reviewers through structured verification of AI-generated skill documentation for accuracy and GitHub submission.
WeihanLiCollect placeholder values and update the project constitution file.
hieutrtrGenerates Claude Code skill files (.claude/commands/*.md from detected domain evidence.
