Compile a plain-language task into a concise, auditable Codex or Claude Code `/goal`, or explain why a normal prompt fits better. Use when the user asks to draft, formulate, rewrite, tighten, or create a goal for multi-step work that needs a durable objective, transcript-visible proof, constraints, bounded stop conditions, host-aware operation, and risk-based review depth.
Conduct a thorough alignment interview to deeply understand a task before starting work. Use when starting any non-trivial task — take-home exercises, ambiguous problems, design challenges, complex implementations, research questions — anything where shared understanding matters more than speed. Triggers on phrases like "interview me", "let's align on this", "before we start", "kick off this task", "probe me on this", "I have a take-home", "help me think through", "I want to align before we begin", or whenever the user signals they want a deep upfront context-gathering session before diving in. Err strongly toward triggering for any substantive new task — measure twice, cut once. Produces a written kickoff brief that becomes the shared foundation for the work.
Execute architectural refactoring from an assessment document with deterministic, chunked operations and aggressive verification at every step. Use when you have an architectural assessment, clean architecture review, refactoring recommendations, or seam-ripper output and need to actually perform the refactoring safely. Also use when asked to "refactor based on this assessment", "execute these architectural recommendations", "fix architectural drift", "refactor in chunks", or any request to systematically restructure a codebase according to a plan. Designed specifically to prevent the kind of agent drift that causes architectural problems in the first place.
Continuous formal verification of architectural constraints and code quality. Use when asked to verify, audit, or validate codebase integrity. Runs automatically via hooks on every edit (structural) and pre-commit (full). Catches ownership violations, boundary crossings, state machine bugs, and code smells that grep ratchets miss. Triggers: "verify", "formal verify", "check architecture", "audit code quality", "run verification", "/verify", "/verify --bootstrap", "/verify --grade".
Craft a high-quality prompt for a deep research agent (like ChatGPT Deep Research) through adaptive interviewing. Use when the user wants to research something but needs help formulating what to ask — when they say "I need to research X", "help me figure out what to ask about Y", "write a research prompt for Z", "I want to use deep research on...", or when they have a vague research need and want a precise, comprehensive prompt that will get excellent results from a research agent. Also use when the user mentions deep research, ChatGPT research, or preparing a query for an AI research tool.
First-principles simplification analysis for codebases. Methodically inventories what a codebase actually does, then asks whether each piece of complexity earns its keep. Use when asked to "simplify this codebase", "is this overengineered", "how could this be simpler", "reduce complexity", "first principles review", "essential complexity audit", "do we really need all this", or any request to rethink whether the current implementation is the simplest way to achieve its goals. Also useful when a codebase feels harder to work with than it should, when onboarding takes too long, or when changes that seem simple keep ballooning in scope.
This skill should be used when cleaning up codebases that have accumulated dead code, redundant implementations, and orphaned artifacts — especially codebases maintained by coding agents. Triggers on "find dead code", "clean up unused code", "remove redundant code", "prune this codebase", "dead code sweep", "code cleanup", or when a codebase has gone through multiple agent-driven refactors and likely contains overlooked remnants. Systematically identifies cruft, categorizes findings, and removes confirmed dead code with user approval.
Remove LLM-isms and AI writing patterns from text. This skill should be used when editing prose to sound less like AI output — removing overused words, fixing structural tells, and restoring natural human voice. Triggers: "de-slop", "remove AI writing", "humanize this", "sounds too AI", "LLM-isms", "AI slop", or when reviewing text that reads like chatbot output.