agentic-engineering

# Agentic Engineering Refactor a skill or workflow so the model can execute more work with less human steering, while preserving verification, provenance, and control of high-risk actions. ## Agent Understanding Model Use the Karpathy-style LLM OS mapping as the design baseline: | OS concept | Agent workflow meaning | |---|---| | LLM = CPU | The model runs the next reasoning/action loop. | | Context = RAM | Load only the state needed for the next step. | | Wiki/logs = Disk | Durable knowledge lives outside chat memory. | | Tools = System calls | Actions must have contracts, permissions, and evidence. | | Loop = Scheduler | Plan -> Act -> Observe -> Iterate controls work. | The agent is a process with state, tools, permissions, and write-back duties. Do not design skills as advice pages; design them as executable control loops. ## Usage Template **Prompt** ```text Use agentic-engineering to revise this skill/workflow. Make it model-native, concise, autonomous, verifiable, and long-horizon capable. ``` **Use Case** - Improving an existing skill, SOP, command, agent workflow, or multi-agent plan. **Expected Result** - A compact agentic workflow with clear defaults, state checkpoints, verification gates, and escalation rules. **Output Example** - A revised `SKILL.md` with model assumptions, autonomous execution loop, quality gates, and anti-patterns. **Verification Case** - A fresh agent can run the workflow without repeated clarification and can prove completion with ...