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-generatornpx skills add Alenryuichi/openmemory-plus --skill resume-generatorgit 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-generator)<a href="https://agentmods.dev/skills/alenryuichi/openmemory-plus/resume-generator"><img src="https://agentmods.dev/badge/skills/alenryuichi/openmemory-plus/resume-generator.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 | $0.00067 | $0.04027 |
| Opus 5 | $0.00034 | $0.02014 |
| Sonnet 5 | $0.00013 | $0.00805 |
| Haiku 4.5 | $0.00007 | $0.00403 |
Grade A, and why
resume-generator 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 — 420 lines — stays where its author put it; the contents beside it link to each section on GitHub.
简历生成 Skill (Agent-First)
目的
一句话触发全流程：用户只需说"帮我针对这个 JD 生成简历"，Agent 自动完成 JD 解析 → 项目匹配 → 内容优化 → 质量检查 → 面试准备。
Agent-First 原则:
- 零配置：不需要用户编辑任何配置文件
- 自动决策：Agent 自动选择项目、优化内容、检查质量
- 主动服务：Agent 主动提问补全信息、输出面试准备
- 透明可控：展示决策依据，用户可纠正
核心原则:
- 项目维度优先:
work只保留简要职责，projects包含完整内容- XYZ 公式: "Accomplished [X] as measured by [Y] by doing [Z]"
- 注意: STAR 格式适用于面试回答，不适用于简历 bullet points
触发条件
当用户请求包含以下关键词时激活：
- "生成简历" / "帮我生成简历"
- "针对这个 JD 生成简历"
- "提取项目经验"
- "创建 resume"
- "整理工作经历"
- "更新简历"
- 用户提供 JD 并暗示需要简历
🤖 Agent-First 自动化流程
端到端流程（用户只需一句话）
用户: "帮我针对这个 JD 生成简历：[JD 内容]"
↓
┌─────────────────────────────────────────────────────┐
│ Step 1: JD 自动解析 │
│ - 提取关键词（必需技能、优先技能、软技能） │
│ - 识别公司类型（大厂/创业/外企） │
│ - 推断风格偏好（详细/简洁） │
├─────────────────────────────────────────────────────┤
│ Step 2: 项目自动匹配 │
│ - 计算每个项目与 JD 的匹配度 │
│ - 选择 Top 3-5 最相关项目 │
│ - 相关项目详写（3-5 bullets），不相关项目简写（1-2） │
├─────────────────────────────────────────────────────┤
│ Step 3: 内容自动优化 │
│ - 自动注入 JD 关键词 │
│ - 自动替换弱动词为强动词 │
│ - 自动修复 ATS 不友好元素 │
├─────────────────────────────────────────────────────┤
│ Step 4: 质量自动检查（生成后自动执行） │
│ - 输出匹配度报告 │
│ - 输出健康度评分 │
│ - 输出优化建议 │
├─────────────────────────────────────────────────────┤
│ Step 5: 面试准备自动附带 │
│ - 预测可能的面试问题 │
│ - 标记深挖点 │
│ - 生成 STAR 回答建议 │
└─────────────────────────────────────────────────────┘
↓
输出: 简历 + 匹配报告 + 面试准备
JD 自动解析（内置能力）
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 · 420 lines · 67 tokens per session scan A a471af7bbdaf
resume-generator is a skill published in the GitHub repository Alenryuichi/openmemory-plus (20 stars, last pushed 6mo ago), licensed MIT. It adds 67 tokens to every session and 4,027 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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