Agent Skills

5 hours ago

Agent Skills is a framework to enforce senior engineer practices (like specs, tests, reviews) in AI coding agents, which typically skip these steps by default.
Skills are markdown workflows with frontmatter, injected contextually—focusing on actionable steps with exit criteria, not reference documentation.
The project organizes twenty skills around six SDLC phases: Define, Plan, Build, Verify, Review, Ship, plus a /code-simplify command.
Five key design principles: process over prose, anti-rationalization tables, non-negotiable verification, progressive disclosure, and scope discipline.
Skills incorporate Google engineering practices (e.g., Hyrum's Law, test pyramid, 100-line PRs) to ensure reliability at scale.
Usage modes include installing via marketplace, dropping markdown into tools, or reading skills as specifications for team practices.
Anti-rationalization tables preempt excuses for skipping workflows, a pattern applicable to human teams to prevent engineering decay.
Verification requires concrete evidence (e.g., tests passing, reviewer sign-off)—'seems right' is insufficient for task completion.
Progressive disclosure loads only relevant skills into context to maintain performance, avoiding token overload.
The framework targets long-running agents, where skipped steps accumulate into major issues, and is portable across AI coding tools.

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