grilling
๐ฏSkillfrom mattpocock/skills
Installation
npx vibeindex add mattpocock/skills --skill grillingnpx skills add mattpocock/skills --skill grilling~/.claude/skills/grilling/SKILL.mdA model-invoked productivity skill that interviews users relentlessly about a plan or design until every branch of the decision tree is resolved. It serves as the reusable interview loop behind the grill-me and grill-with-docs skills.
Overview
A model-invoked skill from Matt Pocock's "Skills For Real Engineers" collection that implements a relentless interview loop. When activated, the agent asks the user detailed, probing questions about their plan or design until every branch of the decision tree is resolved. It is the reusable engine behind the user-invoked /grill-me (general productivity) and /grill-with-docs (engineering with domain modeling) skills.
Key Features
- Decision Tree Resolution: Systematically walks through every branch of a plan or design, asking targeted questions until ambiguity is eliminated and the full scope is clear
- Alignment Before Action: Addresses the most common failure mode in AI-assisted development by ensuring the agent fully understands what to build before writing any code
- Composable Architecture: Works as a building block for other skills, providing the interview discipline that
/grill-meand/grill-with-docsbuild upon with additional features like domain modeling and ADR documentation - Model-Agnostic Design: Works with any LLM and is designed to be small, easy to adapt, and composable with other skills in the collection
Who is this for?
- Engineers who find that AI agents often misunderstand requirements and want a structured way to align before implementation begins
- Teams using Matt Pocock's skill collection who need the core interview loop for custom workflows or skill compositions
- Developers who want to reduce wasted iterations by thoroughly exploring requirements upfront rather than discovering gaps mid-implementation
Same repository
mattpocock/skills(57 items)
SKILL.md
More from this repository10
Interviews the user relentlessly about every aspect of a plan or design until reaching shared understanding, resolving each branch of the decision tree one by one. Provides recommended answers for each question and explores the codebase when answers can be found there.
A skill that runs a grilling session to challenge your plan against an existing domain model, sharpen terminology by building a shared language in CONTEXT.md, and document architectural decisions in ADRs before coding begins.
An agent skill from Matt Pocock's engineering toolkit that helps improve your codebase architecture. Part of a composable skill set designed to fix common AI coding agent failure modes through structured, experience-based workflows.
Enforces test-driven development with a strict red-green-refactor loop using vertical slices (one test then one implementation at a time), emphasizing behavior verification through public interfaces rather than implementation-coupled testing.
Initial setup skill for Matt Pocock's engineering skills collection that configures your backlog manager, triage labels, and documentation paths for AI coding agents.
A collection of small, composable engineering skills designed for real development workflows, featuring grilling sessions for requirement alignment, issue tracking integration, and task triage with customizable labels.
A composable skill for triaging issues and tickets using configurable labels, designed to fix common failure modes in AI coding agents by providing structured backlog management workflows.
A collection of composable engineering skills by Matt Pocock designed to fix common AI coding agent failure modes: misalignment via grilling sessions, verbosity through shared domain language (CONTEXT.md), code quality with TDD red-green-refactor loops, and codebase entropy with architecture improvement workflows.
A model-invoked engineering skill that actively builds and sharpens a project's domain model by challenging terms against the glossary, stress-testing with edge-case scenarios, and updating CONTEXT.md and ADRs inline.
A model-invoked engineering skill that provides shared discipline and vocabulary for designing deep modules: maximizing behavior behind small interfaces, placing code at clean seams, and ensuring testability through public APIs.