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.
git clone --depth 1 https://github.com/KaichenCurry/TabNexusWrote 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/rules/kaichencurry/tabnexus/resume-experience-template)<a href="https://agentmods.dev/rules/kaichencurry/tabnexus/resume-experience-template"><img src="https://agentmods.dev/badge/rules/kaichencurry/tabnexus/resume-experience-template/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/rules/kaichencurry/tabnexus/resume-experience-template"><img src="https://agentmods.dev/badge/rules/kaichencurry/tabnexus/resume-experience-template.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00043 | $0.04317 |
| Opus 5.5 | $0.00017 | $0.01727 |
| Sonnet 5.5 | $0.00009 | $0.00863 |
| Haiku 4.5 | $0.00004 | $0.00432 |
Grade A, and why
resume-experience-template 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 1mo ago.
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 — 366 lines — stays where its author put it; the contents beside it link to each section on GitHub.
中文简历经历优化规则
当用户要求生成、改写、审阅、补强或按 JD 定向调整中文简历时,遵循本规则。将每条经历写成一条可验证的价值证明:说明解决了什么问题、如何解决、产生了什么结果。真实性和可解释性高于“高级感”。
任务模式
先判断用户需要哪种结果,可组合执行:
- 从零生成:把口述、流水账或材料整理成简历经历。
- 改写优化:重写已有经历,压缩冗余并补齐逻辑。
- JD 定向:提取目标岗位的核心能力,以已有证据重新排序和措辞。
- 项目整理:输出项目名称、项目定位和 3–5 条简历描述。
- 简历审阅:检查结构、事实、表达、量化、商业价值和可面试性。
- 模板搭建:生成整份简历或单段经历的可填写模板。
核心工作流
1. 建立事实底稿
从用户材料中提取:
- 公司/组织、部门、岗位、时间、地点;
- 业务背景、目标、问题或约束;
- 用户本人承担的角色、动作和责任边界;
- 使用的方法、工具及实际使用程度;
- 交付物、采用情况、决策影响和商业结果;
- 规模、基线、对标、前后变化、时间周期;
- 目标岗位/JD 及能够对应的事实证据。
将信息分为:
- F1 用户明确确认:可直接写入成稿;
- F2 可合理推断但未确认:只能用于提问或给出方向,不能写成事实;
- F3 完全缺失:不得填充。
重点核验“主导/推动/负责”的责任边界、团队成果中的个人贡献、数据口径与比较基线、分析或方案的采用情况、商业结果的归因,以及工具和方法是否真实使用过。
不要把团队成果自动归为个人成果,不要把参与升级为主导,不要补造工具、产品能力、数据、对标或商业影响。不要把“提交报告”写成“驱动增长”,不要把“参加会议”写成“推动决策”,不要把“接触过工具”写成“熟练掌握”。
若材料只剩岗位名称,无法写出任何真实动作,先提出最多 3 个高价值问题。其他信息不足场景直接给出保守成稿,并在成稿后单列待补充证据,不要用臆测阻塞任务。
2. 确定定位与取舍
根据目标岗位和证据选择低重合的能力模块,不按每日事项罗列。专业能力约占 70%,沟通、推进等通用能力约占 30%。
优先保留:
- 与目标岗位匹配度高的能力;
- 工作量占比高、本人贡献清楚的事项;
- 有采用、决策或商业结果的高含金量产出。
删除重复、低价值、无法解释或与岗位无关的内容。不同经历尽量承担不同的能力证明。
存在多个合理方向时,明确区分:
- 最稳方向:完全由现有事实支持,重点体现职责、方法、交付和已验证结果;
- 最有竞争力方向:强调所有权、规模、采用或商业影响,但只有证据足够时才能写进成稿;否则说明需要补什么,不得先写成事实。
3. 搭建结果证据链
按以下顺序追溯价值,并写到最后一个已验证环节为止:
业务问题 → 分析/执行方法 → 关键发现或交付 → 被采用/落地 → 业务结果
结果层级从高到低:
- L4 商业结果:收入、成本、转化、留存、风险损失、订单等;
- L3 落地/决策结果:方案采纳、流程上线、覆盖团队、决策依据;
- L2 效率/质量结果:周期缩短、准确率提升、错误减少、自动化;
- L1 交付结果:报告、模型、方案、材料、数据库;
- L0 职责描述:仅说明做过什么。
优先使用最高且已获事实支持的层级,不得从 L1 跳写为 L3/L4。没有业务结果时,用真实交付、覆盖范围和使用去向收束。
仅使用有来源的数据。近似值只有在用户确认可用且能解释口径后,才使用“约”“近”等限定词。对标必须说明基线,例如目标值、改版前、同期、团队平均或有来源的同行口径。
若只确认报告被负责人阅读,写“供负责人评审/决策参考”,不要写“驱动战略调整”。若商业结果受多因素影响,使用“支持、助力、贡献于”等准确措辞,并能解释个人贡献。
4. 撰写经历
先写一句职责概括,再按能力模块写 Bullet Point:
- 最新或最重要经历写 3–4 条,不超过 4 条;早期或低相关经历可写 2 条;
- 项目经历写 3–5 条;
- 每条使用“有力动词 + 具体动作/方法 + 已验证结果”;
- 尽量形成“问题—方法—结果”闭环,一句话只证明一个核心能力;
- 把工具嵌入业务场景,写清使用程度、处理对象和支持的结果;
- 使用准确的所有权动词:主导、负责、搭建、推动、产出、优化、识别、拆解、验证;动词强度必须与事实一致;
- 使用易懂、具体、可追问的语言,删除黑话、中英混杂、长难句和主观评价。
不要机械统一句式,不要强行给每条添加夸张数字。没有数字时,优先写范围、交付和真实应用。
可参考以下骨架,但优先自然表达:
负责/主导【业务问题或目标】,通过【方法/工具/关键动作】,实现/形成【结果】。针对【问题】,搭建/拆解【方法】,识别【发现】,推动/支持【已验证应用或结果】。为解决【问题】,使用【工具的具体功能】处理【规模/对象】,产出【交付】,用于【真实场景】。
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.
- 1mo ago First seen · 366 lines · 43 tokens per session scan A 7235f723f562
resume-experience-template is a cursor rule published in the GitHub repository KaichenCurry/TabNexus (3 stars, last pushed 2mo ago), licensed MIT. It adds 43 tokens to every session and 4,317 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-08-30.
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