处理面试记录,包含面试大纲规划、提问提取与纠错、总结和知识图谱存储
npx skills add https://github.com/cafe3310/public-agent-skills --skill interview-processor
本技能用于辅助面试官在面试前后进行高效的结构化处理,沉淀招聘成果并客观自评提问水平。它可以配合 memories-off 技能的 memocli 将生成数据安全写入本地知识图谱。
本技能依赖本地优先的知识库管理工具 memocli。
本 Skill 依赖 memories-off 库进行实体管理与长期记忆。在执行任何任务前,您必须先查阅并完整遵循当前目录下的 memories-off-declare.md 声明文档,以获取其定义的实体类型规范及封装的子过程操作细节。
同时,为了简化指令的输入长度,建议您在 ~/.config/memocli/config.yaml 中配置全局路径别名,并使用简写别名(如 -p work 或 -p life)来运行本技能涉及的所有 memocli 指令。
为了保证面试总结中“团队介绍”以及对面试官表现的自评贴合面试官当前的最新岗位和团队,本技能采用知识图谱优先的动态画像加载机制:
memocli search-entities "我自己的面试官画像" -p <path>
## 当前岗位与团队 和 ## 个人面试偏好),以其内容作为当前的背景上下文。 echo "## 当前岗位与团队
[在此写明您当前的岗位、所负责团队及核心业务介绍]
## 个人面试偏好
[在此写明您的面试风格、提问原则和关注特质]" | memocli create-entity -p <path> -e "我自己的面试官画像" -t "个人画像" --content-stdin --reason "初始化面试官个人画像"
本技能在本地知识库中管理四类实体。为符合 memories-off 规范,所有实体必须遵循严格的 H1 (# 实体名) 与 H2 (## 章节名) 标题层级,禁止使用 H3 及以下标题。
我自己的面试官画像个人画像## 当前岗位与团队## 个人面试偏好[领域/岗位]专家面试风格-YYYYMMDD (例如:iOS专家面试风格-20250718)面试方法论## 面试风格与偏好(列出面试官在该领域的提问原则、风格和追问逻辑)[领域/岗位]面试问题集-YYYYMMDD (例如:大模型产品面试问题集-20250729)面试问题集## 核心认知与边界理解## 系统设计与成本控制target_methodology 指向其对应的 [领域/岗位]专家面试风格-YYYYMMDD 实体。对[候选人姓名]的面试总结 (YYYY-MM-DD)面试记录## 基本信息(候选人姓名、招聘类型 [校招/社招/数字马力]、岗位名称、面试日期)## 面试反馈(建议层级、优势、不足、结论)## 沟通记录与评价(核心讨论话题列表与候选人回答情况评估)## 面试官表现自评(以专家级别对面试官提问、追问、得体度做出的客观改进要求)use_style 指向对应 面试方法论 实体。use_question_set 指向对应 面试问题集 实体。candidate 指向候选人个人实体(若有)。当用户触发本技能时,请根据当前所处的面试阶段,跳转至对应的子任务执行详细指南:
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Take cafe3310/interview-processor from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.