一个 DeepSeek Harness (DSH) 技能：让 agent 在沟通中实时构建用户画像（熟练度 / 偏好 / 领域差异 / 当前状态），并按画像动态匹配回答的深度、术语密度与步骤粒度。
设计原则：
skills/dsh-buddy/
├── SKILL.md # 主文件：决策表 + 红线（模型常驻上下文）
├── references/patterns.md # 升降档手法 / 类比构造 / 装唐完整规程（按需加载）
└── evals/trigger-queries.json # 触发评测集（train/validation 60/40 防过拟合）
用户可控：随时问「你现在把我当什么水平？」即可导出画像快照；纠正即时生效；画像是会话内的，不落盘、不持久化。
node --test # 技能完整性 + 评测集结构校验（零依赖）
修改 SKILL.md 的 description 后按 agentskills.io 方法跑触发评测：train 集指导修改、validation 集只做泛化验证。
独立使用：
dsh plugin --profile <名> add github:oneinitAI/dsh-buddy
或随 ThunderForge 全家桶（已内置本技能）。
MIT
FAQs
dsh-buddy — 用户画像自适应表达技能：实时构建用户熟练度/偏好/领域画像，动态匹配回答深度与术语密度
We found that dsh-buddy demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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