Review existing agentic code, designs, or plans through the lens of the 17 canonical antipatterns. Diagnose what's likely to fail in production. Use whenever the user asks you to review their agent code, asks "what's wrong with this design," is debugging mysterious failures, or wants a second opinion on an architecture. Also use proactively when you notice any of the 17 antipatterns in a conversation, even if the user didn't ask for review.
Audit an agentic product against the 24-point Definition of Done before launch. Covers context, tools, permissions, reliability, evals, observability, security, cost, the Loop License, and measurement science (judge calibration, retrieval metrics, ground-truth provenance, drift, human oversight) — the minimum bar for production. Use whenever the user is preparing to launch / ship / deploy an agentic product, asks "is this production-ready," wants a pre-launch checklist, or is doing a code review before going live.
Master skill for building production-grade agentic products — software systems where part of the process is dynamically directed by LLMs within deterministic architecture with explicit trust boundaries. Use this skill whenever the user mentions building an agent, agentic product, agentic workflow, AI agent, multi-agent system, agent loop, agent harness, or asks how to design, architect, ship, or harden any system with LLM-driven decision-making. Also use when they reference frameworks like LangGraph, CrewAI, OpenAI Agents SDK, Claude Agent SDK, Pydantic AI, AutoGen, or when they want to add tools, memory, evals, or human-in-the-loop to an LLM system. This is the entry point — it routes to specialized sub-skills for architecture, context engineering, harness, tools/MCP, memory, durable execution, evals, framework choice, production readiness, and antipattern review.
Build, implement, review, or harden a SINGLE production-grade agent — its contract, schemas, tools and permission tiers, durable state, guardrails, traces, and evals. Use when the user wants to create one agent (not a multi-agent product), implement an agent runner, add tools/memory/evals to an existing agent, or review whether one agent is production-ready. For multi-agent products, orchestration, or framework selection, use the agentic-product-architect skill instead. The full operational standard this skill applies is AGENT_STANDARD.md (bundled with this skill); copy-paste artifacts are in templates/.
Make agents survive crashes, timeouts, restarts, and human waits — using Temporal, Inngest, Restate, or LangGraph's checkpointer. Cover the Workflow + Activity pattern, pause/resume semantics, retry policies, and when to retrofit (answer: before your first long-running agent goes to production). Use whenever the user mentions long-running agents, multi-hour tasks, pause/resume, retry on failure, agent crashing mid-flight, state persistence, Temporal, Inngest, Restate, or asks how to handle reliability over hours/days.
The AgenticProduct paved road — how to stand up and wire the family's reference implementations so each surface of the standard is satisfied out of the box. Covers AgenticMind (knowledge & memory over MCP), AgenticOps (runtime & fleet operations), AgenticPerformance/APL (evals & observability over OpenTelemetry), AgenticGateway (model & cost plane — one OpenAI-compatible key, eval-sourced routing, cost circuit breakers), and AgenticAssurance/AAL (red-team security assurance). Use whenever the user asks "what should I actually use to build this", wants the batteries-included stack, wants to install or wire our tools, wants conformance without assembling every surface by hand, or mentions AgenticMind / AgenticOps / AgenticPerformance / AgenticGateway / AgenticAssurance. The standard stays vendor-neutral (Principle 2) — this is the recommended paved road, not a mandate; bring-your-own is always fine.
Engineer what goes into the LLM context window — system prompts, retrieved docs, tool schemas, conversation history, memory, examples. Apply the four operations write/select/compress/isolate to manage context as a finite resource. Enforce the 40% rule on context utilization. Use whenever the user is designing system prompts, debugging quality degradation in long conversations, hitting context limits, managing per-step retrieval, dealing with sub-agent context isolation, or asking about "context engineering" / "prompt engineering" / CLAUDE.md / AGENTS.md / instruction files.
Build the evaluation discipline that separates production agentic products from demos — error analysis on real traces, the three-level eval pyramid (code assertions / LLM-as-judge / human review), binary judge outputs calibrated against human labels, and CI gates that block regression. Based on the Husain/Shankar methodology. Use whenever the user mentions evals, evaluation, LLM-as-judge, hallucination testing, regression testing for AI, quality measurement, error analysis, "how do I know if my agent works," failure modes, or grading agent outputs.