Evaluate kaizen issues by validating problem framing, scope, and evidence.
npx skills add https://github.com/Garsson-io/kaizen --skill kaizen-evaluate
Please help me install this Agent Skill. Skill: kaizen-evaluate Source: https://github.com/Garsson-io/kaizen/tree/main/.agents/skills/kaizen-evaluate Command: npx skills add https://github.com/Garsson-io/kaizen --skill kaizen-evaluate
Evaluate kaizen issues before implementation to ensure the problem statement is accurate, gather supporting incidents, and define a concrete scope and plan for admin alignment.
Run kaizen-evaluate to generate a concrete evaluation plan and prepare it for admin approval.
High-intent search queries and answers about installing and using this skill.
Validating problem scope requires evaluating the claimed issue to confirm it exists, gathering supporting incidents, and defining concrete success criteria. This ensures the problem framing is accurate and testable before development begins.
Gathering incident evidence involves accumulating data from commits, issues, and discussions to quantify impact. This process assesses observability to determine what data proves or disproves the problem, ensuring inputs remain auditable for future iterations.
Forming an implementation plan involves crafting a high-level path with defined success criteria after validating the problem scope. This produces artifacts suitable for admin review and decision-making, preparing the plan for subsequent implementation.
Kaizen evaluation is needed during admin planning when you must align stakeholders before work begins. It confirms the problem statement is accurate, gathers evidence, and defines a concrete scope to prepare artifacts for admin approval.
Kaizen evaluation includes an observability assessment to determine what data would prove or disprove the problem. If prior data is lacking, the evaluation identifies the specific evidence needed to validate the claimed issue before proceeding.
Evaluating kaizen issues before implementation ensures the problem framing is accurate and evidence supports the recommended path. This prevents wasted effort on unvalidated scopes and keeps all inputs auditable for future iterations.
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