技能提炼
Tomer 把个人代理的完整工作流摆了出来，用大模型写 skill 小模型执行，这条蒸馏思路比调 prompt 高级，想认真跑本地代理的人该盯一下。
“技能提炼”是一种知识转移方法，由前沿大模型（如 Opus 4.7、GPT-5.1、Gemini 3 Pro）负责撰写并优化标准化的 SKILL.md 流程文件。然后，本地运行的小模型（如 Qwen 35B、Gemma 26B）直接执行这些文件。此过程不同于压缩模型权重的知识蒸馏、训练权重的指令微调或检索事实的 RAG，其核心是提取并转移操作流程，让小模型按步骤执行，从而形成前沿模型作教师、小模型作执行者的循环。
I’ve been using state-of-the-art models to teach small models running on my computer how I work.
My personal agent, based on Pi, runs my inbox, my deal pipeline, my blog publishing, my calendar, & my research. It looks less like a chatbot & more like a small operating system.
The first layer is QMD, a local markdown knowledge base of about eighty workflow files in ~/memories. Before answering any procedural question, the agent searches QMD for the right playbook.
The second layer is Skills, atomic SKILL.md files that describe one job each. The skills are written by a frontier model. So are the evaluations that grade them. The same system writes, tests, and rewrites each skill until accuracy converges. It also checks recall against QMD, so the right keywords always surface the right skill.
The third layer is the Agent Loop, a model running Plan → Tool Call → Observe → Refine, calling out to seventeen Rust APIs & a handful of MCP integrations.
One of the techniques I’ve started to use is skill distillation. A frontier model, Opus 4.7, GPT-5.1, Gemini 3 Pro, authors & refines the skill files. A smaller model, Qwen 35B or Gemma 26B running locally, executes them. The teacher transfers procedural knowledge to the student through markdown. The skill is inspectable, versionable, & hot-swappable.
This is fundamentally different from classical knowledge distillation, which compresses a big model’s soft probability outputs into a smaller model’s weights. It’s different from instruction tuning, which bakes behavior into weights through prompt-response pairs. It’s different from RAG, which retrieves facts.
Skill distillation retrieves procedures. The smaller model doesn’t have to know how to evaluate a company. It just has to know how to follow the steps.
Every night a system runs through historical logs to understand what new skills should be generated, mirroring the loop that Pete Koomen described at Y Combinator earlier this week.
The frontier model becomes a teacher. The library becomes the company’s institutional knowledge. The student becomes whichever model happens to be cheapest this quarter.