Check whether a feature, fix, or refactor actually works using existing tests, build checks, direct commands, or manual validation. Get a concise account of the evidence, including what passed, what failed, and what remains unverified.
Verify that a change really works before you claim completion
Review the instructions the agent will receive before you install.
Use this skill when the user wants confidence that a feature, fix, or refactor actually works.
Turn vague “it should work” claims into concrete evidence.
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Write or revise instructions, specifications, tickets, and skill files so an agent can act without prior chat history. Make rules checkable, explain their reasons, define completion criteria, and keep authoritative information in one place.
Simplify bloated AI-generated code by removing dead code, duplicated logic, and unnecessary layers while checking that existing behavior stays intact. Use a limited cleanup scope, verify changes with relevant checks, or request a review without edits.
Review module relationships and change history to identify structural improvements in a codebase. Receive ranked candidates with file references, suggested changes, and risks, plus a recommendation for where to start, without modifying code.
Send questions, review requests, or implementation tasks to a supported local AI command-line tool through one shared command. The wrapper handles provider-specific options and saves the result as a Markdown file for later reference.
Turn an unclear idea into linked decision tickets on a repository’s issue tracker. Work through one decision per session, record outcomes and dependencies, and prepare a brief defining the objective, scope, and exclusions for delivery.
Turn a short product idea into working code through requirements analysis, technical planning, implementation, testing, and review. It coordinates agents across these stages, fixes detected problems, and reports assumptions that still need a human decision.