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Interview the user relentlessly about a plan or design until reaching shared understanding, resolving each branch of the decision tree one at a time. Use for ambiguous or complex collaborative specs before any code is written.
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Based on SOC occupation classification
| name | grill-me |
| description | Interview the user relentlessly about a plan or design until reaching shared understanding, resolving each branch of the decision tree one at a time. Use for ambiguous or complex collaborative specs before any code is written. |
| scope | universal |
| ported-from | global skill |
Interview the user relentlessly about every aspect of this plan until you reach shared understanding. Walk down each branch of the design tree, resolving dependencies between decisions one by one. The goal is a spec with no unresolved forks, not a polite conversation.
Stop when every branch that affects the implementation has a decision behind it.
Index of this project's coding-practice rules — architecture, coding style, testing, security, git workflow, task management, context layering, anti-slop, performance, and language-specific rules. The thin always-on essentials live in .claude/rules/common/house-rules.md; the full per-topic detail ships under .claude/rules/reference/ and .claude/rules/<lang>/ and is read on demand. Invoke this skill when you need the project's standards for a task, then open the specific rule file it points to.
Thin methodology for end-to-end tests of critical user journeys — define journeys by risk, use semantic locators and condition-based waits, quarantine flaky tests with a tracked reason, and capture artifacts on failure. Use when adding or stabilizing E2E coverage; the e2e-runner agent applies it in depth.
Design rigorous evaluations and benchmarks for AI agents, developer tools, retrieval systems, and repository-scale automation. Covers task selection, contamination control, metric choice tied to engineering decisions, and statistical validity. Use when asked to design an eval/benchmark, critique an existing benchmark, choose metrics for an agent or RAG system, or decide whether a measured improvement is real. NOT for running an existing performance-benchmark suite or a per-feature acceptance checklist, or one-off model spot-checks.
Decision framework for choosing between regex and LLM when parsing structured text — start with regex, add LLM only for low-confidence edge cases. Use when parsing quizzes, forms, invoices, or documents with repeating structure and cost matters.
Review a repository for long-term architectural leverage rather than code quality — system structure, module boundaries, dependency graph, coupling, and drift. Produces a ranked set of highest-ROI improvements with evidence, effort, and risk. Use when asked to review the architecture, assess a codebase's structure/design, find where complexity is concentrated, or decide what to refactor next. NOT for style, naming, formatting, or line-level bugs (use a code-review skill for those).
Analyze a software system, AI pipeline, or research direction by surfacing its underlying structure — invariants, hidden abstractions, weak assumptions, and the single highest-leverage direction — rather than optimizing local implementations. Use when asked to step back and think about a system's design at a conceptual level, find leverage points, evaluate a research direction for compounding impact, or decide where to invest for the long term. NOT for reviewing a specific repo's architecture (use repo-architecture-review) or local code cleanup.
