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 skills add KaichenCurry/TabNexus --skill resume-experience-templategit 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/skills/kaichencurry/tabnexus/resume-experience-template)<a href="https://agentmods.dev/skills/kaichencurry/tabnexus/resume-experience-template"><img src="https://agentmods.dev/badge/skills/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/skills/kaichencurry/tabnexus/resume-experience-template"><img src="https://agentmods.dev/badge/skills/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 | $0.00147 | $0.01892 |
| Opus 5 | $0.00073 | $0.00946 |
| Sonnet 5 | $0.00029 | $0.00378 |
| Haiku 4.5 | $0.00015 | $0.00189 |
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 9d 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 — 123 lines — stays where its author put it; the contents beside it link to each section on GitHub.
简历经历模板
目标
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。优先保证真实可解释,再追求竞争力;让面试官能快速识别候选人的专业能力、业务判断和商业价值。
选择任务模式
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。
需要完整规则时读取 evidence-and-writing-rules.md。需要收集信息或输出成稿时读取 templates.md。
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。
将信息区分为“用户明确确认”“合理但未确认”“缺失”。只把已确认事实写进可直接使用版。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。
若材料只剩岗位名称而无法写出任何真实动作,先提出最多 3 个高价值问题。其余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,而不是按每日事项罗列。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,避免互相覆盖。
当一段经历存在多个合理方向时,明确给出:
- 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,但仅在证据足够时写成简历内容;证据不足时说明需要补充什么,不得先写成事实。
3. 搭建结果证据链
按以下顺序追溯价值,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
优先使用业务结果,其次是采用/决策影响、效率或质量提升、覆盖规模,最后才是报告页数等交付物。没有业务结果时,用真实的交付、覆盖范围和使用去向收束,不要暗示未发生的影响。
仅使用有来源的数据。需要近似值时,只有用户确认可用且能解释口径后才写“约”“近”等限定词。对标必须说明基线,例如目标值、改版前、同期、团队平均或明确的同行口径。
4. 撰写经历
先写一句职责概括,再按能力模块写 Bullet Point:
- 最新或最重要的经历写 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;
- 每条遵循“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,写清用到什么程度、处理什么对象、支持什么结果;
- 使用准确的所有权动词,如“主导、负责、搭建、推动、产出、优化、识别、拆解、验证”,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。
不要为了统一句式牺牲自然表达。不要强行让每条都出现夸张数字;没有数字时,优先写范围、交付和真实应用。
5. JD 定向与排序
若提供 JD,提取最重要的 3–5 项能力,建立“JD 要求—事实证据—对应 Bullet”映射。只复用有事实支撑的关键词,不做关键词堆砌。按“岗位相关性 → 商业/决策价值 → 工作量与所有权”排序。
若未提供 JD,根据经历内容说明最可信的岗位方向;不要虚构具体招聘要求。用户仅要求通用版本时,优先保留可迁移的专业能力与可验证结果。
6. 输出结果
除非用户指定其他格式,按以下顺序交付:
- 推荐定位:一句话说明最稳方向;存在显著差异时补充最有竞争力方向及证据条件。
- 可直接使用版:只包含已确认事实,不插入解释、批注或虚构占位符。
- 待补充增强点:列出最多 3–5 个能显著提高竞争力的具体证据问题;没有则省略。
- 修改说明:仅在用户要求审阅、对比或解释时提供,保持简短。
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 9d ago First seen · 123 lines · 147 tokens per session scan A 20f043bf9bc3
resume-experience-template is a skill published in the GitHub repository KaichenCurry/TabNexus (29 stars, last pushed 21d ago), licensed MIT. It adds 147 tokens to every session and 1,892 once invoked, about $0.0007 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 2026-08-30.
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