Four AI Agent Skills That Make Coding Workflows Sharper

AI coding agents are often discussed as though they are a single tool: ask for code, receive code. In practice, useful agent work has stages. You need different behavior when the request is unclear, when a design has to survive scrutiny, when implementation is underway, and when work must move into a new session. Trying to solve all four stages with one large prompt usually produces a compromise. The agent may be verbose while you need execution, eager while you need questions, or unable to resume work because the important context is buried in chat history. This article covers four skills that address those distinct problems: Caveman for concise execution communication, Superpowers for structured development, grill-me for pressure-testing a proposal, and handoff for transferring the live thread to a fresh agent or session. They are complementary. The goal is not to add more ceremony to every edit. It is to apply the smallest useful constraint at the moment it prevents the most waste. The four failure modes of AI-assisted development 1. The agent starts coding before the work is understood A request such as “add organization roles” hides decisions about membership, permission scope, migrations, audit trails, errors, and rollout. An agent can produce a plausible patch before any of those choices are explicit. 2. The agent agrees instead of challenging Helpful assistants tend to accept a framing. That is dangerous when the framing is a proposal rather than a settled requirement. You need an interview that exposes dependencies and asks what could fail. 3. The agent talks too much during routine work Once a direction is approved, long explanations can become friction. During debugging, review follow-ups, and small implementation loops, the useful output is usually a finding, a change, validation, and a risk note. 4. Context is lost at a session boundary A new agent with no context repeats discovery. A new agent with a full transcript has to find the current state among