Keep code changes small by checking whether they are needed, reusing existing helpers, and choosing standard or available tools first. It guides planning and implementation while preserving validation, security, accessibility, and the functionality the user requested.
YAGNI-ladder coding discipline for writing changes — existence-first, reuse before writing, dependency ladder, shortest correct diff, with non-negotiables that must never be minimized away
Review the instructions the agent will receive before you install.
Use this skill to apply a minimal-code (YAGNI-ladder) discipline while planning and writing code changes. It keeps diffs small and obviously correct without sacrificing the checks that keep users safe.
This skill complements ai-slop-cleaner: that skill cleans slop after it lands; this one keeps it from being written.
Use this skill when:
Do not use this skill when:
Existence first. Ask whether the change needs to exist at all; skip work that serves only a speculative future need.
Reuse before writing. Search the codebase for an existing helper, type, or pattern before writing anything new. Never copy a helper that already lives a few files away — reuse it or extract it to one shared location.
Dependency ladder. Reach for the standard library first, then platform-native capability, then an already-installed dependency. Hand-written code is the last resort. Do not introduce a new dependency when a few lines of code suffice, unless the user explicitly requested or approved it.
Understand before minimizing. Read the affected code and follow its execution path first. Ship the shortest correct diff once the problem is understood — code you never write never breaks.
Boring over clever. Prefer boring, obviously-correct code over clever code.
Mark the ceiling. Record deliberate simplifications with a short comment naming the accepted limit and what would justify replacing it.
Root cause, not symptoms. Fix bugs at the root cause shared by every caller, not with a separate patch for each reported symptom.
Never minimize away:
Before reporting completion, confirm:
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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.