# Industry: Microsoft Research - SkillOpt

---
type: industry
company: Microsoft
team: Microsoft Research
title: "SkillOpt: Agent skills as trainable parameters"
url: https://www.microsoft.com/en-us/research/blog/skillopt-agent-skills-as-trainable-parameters/
source_name: Microsoft Research
source_type: research-blog
source_quality: official
published_at: 2026-06-30
collected_at: 2026-07-08
status: analyzed
topics:
  - agent
  - prompt-optimization
  - skill-learning
implementation_signals:
  - skill-files
  - validation-gating
  - bounded-edits
  - eval-loop
product_area:
  - agent-framework
models:
  -
tools:
  -
benchmarks:
  - six-benchmark-evaluation
related_papers:
  -
related_jobs:
  - 2026-07-08-deepseek-agent-hiring-wave-beijing-hangzhou
related_experiments:
  -
related_projects:
  -
evidence_level: high
relevance: high
---

## One-line Takeaway

Agent skill/prompt 文件可以被当成外部可训练参数，用评估循环优化，而不是靠人工随手改。

## What They Built or Claimed

Microsoft Research 提出 SkillOpt，将 skill editing 转成训练过程，在不改模型权重的情况下提升 Agent 行为可靠性。

## Technical Signals

- architecture: frozen target model + external skill file optimization。
- tool use:
- memory:
- evaluation: 六个 benchmark、七个目标模型、三种执行模式。
- safety: bounded text edits、validation gating、rejected-edit feedback、slow/meta updates。
- deployment: 可审计技能文件。
- data:

## Evidence Quality

官方研究博客给出实验覆盖和机制细节，证据强。

## Gaps for Us

- 可以把本项目的 `skills`、prompt、playbook 也纳入 eval-driven 优化。
