Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/alenryuichi/openmemory-plus/resume-optimizernpx skills add Alenryuichi/openmemory-plus --skill resume-optimizergit clone --depth 1 https://github.com/Alenryuichi/openmemory-plusWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/alenryuichi/openmemory-plus/resume-optimizer)<a href="https://agentmods.dev/skills/alenryuichi/openmemory-plus/resume-optimizer"><img src="https://agentmods.dev/badge/skills/alenryuichi/openmemory-plus/resume-optimizer.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00052 | $0.01517 |
| Opus 5 | $0.00026 | $0.00758 |
| Sonnet 5 | $0.00010 | $0.00303 |
| Haiku 4.5 | $0.00005 | $0.00152 |
Grade A, and why
resume-optimizer scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 227 lines — stays where its author put it; the contents beside it link to each section on GitHub.
简历优化 Skill (Agent-First)
目的
优化简历内容，提升 ATS 兼容性和内容质量。
Agent-First 原则:
- 自动执行：简历生成后自动执行健康度检查，无需用户请求
- 主动输出：自动输出匹配报告和优化建议
- 自动修复：可修复的问题自动处理，无需用户干预
触发条件
自动触发（Agent-First）
- 简历生成完成后自动执行
- 用户提供 JD 请求生成简历时自动执行
手动触发
当用户请求包含以下关键词时激活：
- "优化简历"
- "ATS 优化"
- "简历润色"
- "匹配 JD"
- "检查简历"
输入要求
必需输入
- 现有简历 (JSON/Markdown/文本)
可选输入（Agent 可自动推断）
- 目标职位描述 (JD) - Agent 从上下文获取
- 目标公司类型 (大厂/创业/外企) - Agent 自动识别
- 重点优化方向 - Agent 自动分析
优化维度
1. ATS 关键词优化
流程:
- 解析 JD 提取关键词
- 分析简历关键词覆盖率
- 建议添加缺失的关键词
输出格式:
## ATS 关键词分析
| JD 关键词 | 简历中出现 | 建议 |
|-----------|------------|------|
| Python | ✅ 2 次 | - |
| 数据分析 | ❌ | 添加到技能列表 |
| SQL | ✅ 1 次 | 增加使用场景描述 |
**当前覆盖率**: 65%
**目标覆盖率**: 80%+
2. 量化数据增强
检查清单:
- 每个项目至少 2 个量化指标
- 数字具体且可信
- 包含业务影响（金额/百分比）
优化建议格式:
## 量化优化建议
### 原文
"显著提升了系统性能"
### 优化后
"系统响应时间从 2s 降至 200ms，提升 90%"
### 需要补充的数据
- 具体提升幅度？
- 影响的用户量？
- 节省的成本？
3. 行动动词优化
推荐动词 (按强度):
| 级别 | 动词 |
|---|---|
| 领导 | 主导、负责、带领、推动 |
| 执行 | 设计、开发、实现、优化 |
| 协作 | 协调、对接、支持、参与 |
优化示例:
- ❌ "做了数据分析工作"
- ✅ "设计并实现数据分析 pipeline，处理 10TB+ 日志"
4. 格式规范检查
检查项:
- 无表格/图片 (ATS 不友好)
- 标准字体
- 清晰的层级结构
- 一致的日期格式
输出格式
优化报告
# 简历优化报告
## 总体评分: 75/100
## 优化建议
### 高优先级
1. [建议 1]
2. [建议 2]
### 中优先级
1. [建议 3]
## 优化后版本
[完整的优化后简历]
公司类型适配
字节跳动
- 强调: 数据驱动、快速迭代、增长
- 关键词: A/B 测试、DAU、转化率
阿里巴巴
- 强调: 业务理解、商业价值、大规模
- 关键词: GMV、亿级、商业化
腾讯
- 强调: 产品思维、用户体验
- 关键词: 用户增长、留存、体验优化
🤖 Agent-First 自动执行
自动健康度检查
简历生成后，Agent 自动执行以下检查：
---
## 📋 简历健康度: 82/100
| 维度 | 得分 | 说明 |
|------|------|------|
| ATS 友好度 | 95/100 | ✅ 格式规范 |
| XYZ 公式符合度 | 90/100 | ✅ 12/13 bullets 符合 |
| 量化程度 | 75/100 | ⚠️ 2 条 bullet 缺少数据 |
| 关键词匹配度 | 68/100 | ⚠️ 缺少 Kubernetes |
---
自动输出优化建议
---
## 💡 优化建议（自动生成）
### 🔴 高优先级
1. **项目 2 的 bullet 3** 缺少量化数据
- 当前: "优化了系统性能"
- 建议: "将系统响应时间从 2s 降至 200ms，提升 90%"
→ 需要你补充具体数据
2. **缺少 JD 关键词**: Kubernetes
- 建议: 在项目经验中添加 K8s 相关描述
### 🟡 中优先级
1. 动词可强化: "参与" → "主导"
### 🟢 已自动修复
- ✅ 日期格式已统一为 YYYY-MM-DD
- ✅ 特殊字符已移除
---
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- yesterday First seen · 227 lines · 52 tokens per session scan A 95675e29124b
resume-optimizer is a skill published in the GitHub repository Alenryuichi/openmemory-plus (20 stars, last pushed 6mo ago), licensed MIT. It adds 52 tokens to every session and 1,517 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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