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A dev.to post describes grill-me, a manually invoked skill that interviews you one question at a time and inspects the repo instead of asking you to recall it, before code exists.
The Engineer · Build desk
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A post on dev.to describes grill-me, a manually invoked agent skill whose only job is to interview you about a plan or design until the important decision tree is resolved [1]. It matters because the expensive failure in agent-assisted development is rarely bad syntax: a request sounds clear enough, the agent starts implementing, and the details get settled by whichever model output appears first [2].
The diagnosis is the useful part. Phrases like "add roles", "cache this endpoint" and "support collaboration" each carry several linked product, data, security and rollout choices, and teams find out later [3]. Nobody made those decisions; they were absorbed by the first draft of the code.
The mechanics are deliberately small. The skill asks one question at a time, supplies a recommended answer, and waits for feedback before continuing [4]. The rule that does the most work is the one about evidence: if an answer can be discovered by inspecting the codebase, the agent is supposed to investigate rather than ask you to recreate repository facts from memory [5]. Before asking whether an endpoint follows REST or RPC conventions, it should read the existing endpoints [18]. Anyone who has watched an assistant ask "what is your current auth model?" while sitting inside the repository will recognise the saving.
Sequencing is the second design choice. According to the post, a long questionnaire feels efficient but usually is not, because it asks about details before the premise is fixed and the answers end up inconsistent; a sequential interview lets dependencies resolve in order [9]. The worked example is organisation-level roles: first, whether roles are global or scoped per organisation, which determines whether membership is a separate domain entity; then whether permissions are static bundles or configurable; only then API shape, migration, admin UI and audit [10].
The question set clusters into five areas: scope, data and state, authorization, failure modes, and rollout [19]. Scope asks what behaviour changes, who can trigger it, who can observe it, and what is explicitly out, which is what stops a small feature becoming a platform redesign [13]. State asks which transitions are legal, and whether resending an invitation mints a new token, extends expiry, or reuses the record, which are product rules with storage consequences [14]. Authorization asks who may act, at which boundary, and what a denial reveals, on the argument that it is not a closing middleware detail [15]. Failure modes cover the email already attached to a member, delivery failures, two admins acting at once, and client retries [16]. Rollout may well conclude that nothing special is needed, but consciously [17].
The post is explicit about what this is not: not a general implementation workflow, and not a replacement for a specification, issue breakdown, tests, or code review [6]. It is a pressure test for a direction not yet settled enough to build, on the view that teams often need an agent to push back rather than agree, since a conventional assistant leans toward accepting the first plausible framing [7][8].
Two things to watch. The skill is manually invoked, so it only helps operators who already suspect their plan is soft, and the sample prompt front-loads the outcome, affected users, constraints, existing artifacts and the specific decision at issue [11][12]. And the post reports a practice, not a result: there are no measured outcomes in it [20]. The test worth running is whether the recorded settled decisions and open risks [12] survive into the implementation the agent then writes.
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A dev.to post titled "Use grill-me to Pressure-Test an AI Implementation Plan Before Code" describes grill-me as a manually invoked skill that interviews the user about a plan or design until the important decision tree is resolved.
The skill asks one question at a time, supplies a recommended answer, and waits for feedback before continuing.
If an answer can be discovered by inspecting the codebase, the agent should investigate rather than ask the user to recreate repository facts from memory.
grill-me is not a general implementation workflow and does not replace a specification, an issue breakdown, tests or code review; it is a pressure test for a direction not yet sufficiently settled to build.
The best grilling sessions begin with the desired outcome, the affected users, known constraints, existing artifacts and the decision the user wants help making; do not ask "what should we build?" when the real question is which of two approaches fits this codebase.
The sample prompt states a goal (organization owners can invite members by email), constraints (existing RBAC model, no public workspace enumeration, invitations must be revocable), instructs the agent to inspect the relevant auth and membership code first, and to ask one question at a time, recommend an answer, explain the trade-off, and record settled decisions and open risks.
Evidence-backed comparisons of source perspectives and observed adoption signals. Read the methodology
Which Builder, Operator, and Investor concerns the observed source mix emphasized—not a truth score.
Evidence, demonstrated adoption, hype gap, incentives, and confidence are assessed independently, each on its own current evidence. How these are measured.
One self-published practitioner account, internally specific but unmeasured
A single dev.to post supplies all evidence. Its descriptive content is concrete and checkable (named skill, verbatim prompt, five enumerated question categories, explicit non-goals), which is worth something, but there is no second source, no evaluation, and no measured outcome anywhere in the cluster. The efficacy claims — that assistants accept the first plausible framing, that sequential interviews beat questionnaires, that this prevents costly rework — rest on author reasoning alone.
No adoption signal supplied
The cluster contains no release, install, download, deployment, team-usage or disclosure data for grill-me. The only external reference is a mention that AI Hero relays Matt Pocock now generally recommending domain-model as a starting point, which speaks to recommendation sequencing rather than measured uptake of this skill. Nothing here supports an adoption score.
Mildly overstated: bounded claims, but benefits asserted without measurement
The post is unusually disciplined for advocacy — it states that grill-me is not a general workflow, does not replace spec, issues, tests or review, and that existing codebase patterns can be wrong — which keeps the gap small. It nonetheless presents causal benefits (prevents costly rework, avoids inconsistent answers, counteracts assistant agreeableness) as settled when the cluster offers no outcome data or adoption evidence, so claims sit slightly ahead of what is demonstrated.
Practitioner advocacy for a named skill on a self-publishing platform
The observable incentive is attention and credibility: an author-published how-to promoting a specifically named skill and workflow on dev.to, with no editorial review layer, and citing AI Hero and Matt Pocock as authority for the surrounding workflow sequence. The cluster discloses no commercial relationship, pricing, sponsorship or ownership of grill-me, so the distortion pressure is advocacy-shaped rather than vendor-financial, and is partly offset by the post's own scope disclaimers.
Low: single publisher, descriptive claims solid, efficacy and adoption unknown
Confidence in what the post says is high — it is quoted directly and self-consistent. Confidence in the story's substantive assertion, that this interrogation step improves agent-assisted builds, is low: one publisher, one author, no corroboration, no adoption signal and no measurement. The assessment can therefore be trusted about the practice's shape and not about its impact.
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1 article · August 15, 2026