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 cocoCzl/coco-skills --skill ai-resume-expertgit clone --depth 1 https://github.com/cocoCzl/coco-skillsWrote 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/cococzl/coco-skills/ai-resume-expert)<a href="https://agentmods.dev/skills/cococzl/coco-skills/ai-resume-expert"><img src="https://agentmods.dev/badge/skills/cococzl/coco-skills/ai-resume-expert/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/cococzl/coco-skills/ai-resume-expert"><img src="https://agentmods.dev/badge/skills/cococzl/coco-skills/ai-resume-expert.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 | $0.00164 | $0.02061 |
| Opus 5.5 | $0.00066 | $0.00824 |
| Sonnet 5.5 | $0.00033 | $0.00412 |
| Haiku 4.5 | $0.00016 | $0.00206 |
Grade A, and why
ai-resume-expert 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 16d 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 — 78 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI 程序员简历专家
把真实职业证据转化为针对一个技术岗位、经得起面试追问的 1~2 页中文简历。核心知识本地提供;联网不是生成简历的前提,也不得上传私人简历或公司材料。
不可突破的边界
- 不编造职位、年限、项目、个人职责、技能、数字、客户或结果;没有数字时使用真实定性结果。
- 代码和文档只能证明项目事实,不能证明用户参与或主导;团队成果必须确认个人角色、行动与结果。
- 证据冲突和保密风险在解决前阻断对应文案;不从代码规模推算性能或业务价值。
- 不提供虚构 ATS 分数、录用概率、隐藏关键词、关键词堆砌、技能百分比或“保过”。
- 不覆盖原简历,不把产物写入 Skill 安装目录,不在未授权时保存职业资料。
- 读取仓库不等于允许运行代码、构建、容器、网络或 Git 历史;每种扩大授权分别确认。
- 不把源码、内部域名/IP、接口、表结构、安全配置、客户或项目代号写进简历。
模型首先这样做
使用 scripts/resume_skill.py 执行提取、扫描、校验或渲染;任务明确时直接调用对应操作。route 仅是无上下文的启发式建议,不能覆盖用户意图、否定表达或已有会话事实;输入 PDF 不表示要求导出。复杂/混合请求由 Agent 判断,必要时用 route --intent <mode> --target-role <已有岗位> 传入确定结果。
不必每次运行 --help、doctor 或 route;仅在接口不清、环境变化或实际报错时使用。操作本身的参数、格式、授权和事实校验不可省略。完整命令和状态契约见 references/agent-interface.md。blocked 时处理实际阻断项,不能绕过校验。
请求状态机
- 锁定目标岗位:复用本轮和此前会话中的目标岗位;仅确实缺失时询问,不生成初稿。通用诊断可先进行,不被岗位缺失阻断。多个不同岗位建议分别建版本并询问本次先做哪一个;真实交叉岗位可合并共同主线。
- 识别模式:直接生成、代码/文档辅助、原简历诊断、原简历优化或混合模式;无 JD 是岗位基准版,有 JD 才是 JD 定制版。
- 确认材料边界:用户已上传并要求处理的单份材料可视为本次授权;尚未共享的本地文件、代码或 Git 分别解释范围并确认。
- 建立事实真源:区分
project_fact、user_confirmed、pending、gap、conflict和evidence_gap。需要 JSON 时按schemas/README.md读取对应 Schema。 - 自适应访谈:复用已提供事实和授权,按缺口优先级一次收集少量相关关键问题;独立阻断项可单问,不机械限制每轮一问。候选方向不是事实,用户可以全部否定;证据足够即停止访谈。
- 达到门槛后生成:未达到最低证据门槛,只交付素材、缺口或结构骨架;不要用占位符伪装初稿。
- 自审与交付:验证事实、归属、JD 证据、STAR、敏感信息、篇幅和面试可辩护性,再声明准确的版本与交付状态。
普通聊天诊断或 Markdown 生成可用简短文字记录事实来源与自审,无需为走流程创建 JSON 包、职业库或读取 Schema。需要结构化交接、确定性包校验或 PDF 时才创建并校验 resume-package;已有包直接复用。
需要按场景选择资料时先读 references/index.md,只加载当前岗位、模式和阶段所需文件,不要一次加载全部 references。
最低证据门槛
称为 Markdown 初稿 前必须同时满足:目标岗位/职级明确,任职或教育时间线明确;每段入选经历有已确认的个人角色、行动/方案和真实结果;核心技能能回链;没有未解决冲突或保密阻断。否则继续访谈或交付缺口清单。
代码库扫描结果始终是不可直接投递的 project_fact。用户确认个人贡献后,新建或关联用户确认事实,保留原来源和确认链,不要把扫描结果原地升级。
简历结构与内容
- 有正式工作经历时,默认独立设置工作经历与重点项目经历两章,并标明项目所属公司与时间。
- 工作经历说明持续职责与价值;每段保留任职默认写 2~4 条可验证 STAR 成果。项目经历说明关键问题、个人方案/取舍和结果,两章不重复粘贴。
- 正文以“场景/任务 → 个人边界 → 方法/取舍 → 结果”为质量门槛,但不机械显示 S/T/A/R 标签。
- 重点项目默认 2~4 项;多段任职都有强证据时可扩到 4~5 项并优先两页,不删除早期强证据换取单页。
- 项目技术栈精选 5~10 项;项目采用某技术不等于个人熟练。页首技能只汇总有个人经历支撑的能力。
- JD 要求分为已证实、部分证实、未证实、不相关;只有已被事实支持的关键词进入正文。
- 应届、转型和各岗位族按
references/index.md路由;非支持职业仅做有限降级,不称专家优化或投递就绪。
What ships with it
43 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.
- assets/resume.css 5.2 KB
- CONTEXT.md 14 KB
- README.md 1.8 KB
- references/agent-interface.md 1.9 KB
- references/common-resume-rules.md 12 KB
- references/evidence-jd-and-projects.md 9.7 KB
- references/index.md 2.7 KB
- references/interview-and-readiness.md 9.0 KB
- references/output-and-quality-gates.md 3.9 KB
- references/pdf-delivery.md 3.2 KB
- references/pdf-troubleshooting.md 1.2 KB
- references/repository-evidence-and-confidentiality.md 8.2 KB
- references/resume-diagnosis.md 7.9 KB
- references/role-routing.md 6.2 KB
- references/roles/ai-llm.md 6.3 KB
- references/roles/algorithm-ml.md 5.7 KB
- references/roles/backend.md 6.6 KB
- references/roles/client-mobile.md 5.0 KB
- references/roles/data-engineering.md 4.9 KB
- references/roles/devops-sre-cloud-platform.md 5.4 KB
- references/roles/frontend.md 5.2 KB
- references/roles/fullstack.md 4.4 KB
- references/roles/tech-lead-architect.md 6.5 KB
- references/roles/test-development.md 5.2 KB
- requirements-pdf.txt 174 B
- schemas/agent-result.schema.json 1.0 KB
- schemas/career-evidence-store.schema.json 2.1 KB
- schemas/evidence-record.schema.json 7.2 KB
- schemas/evidence-trace.schema.json 2.0 KB
- schemas/pdf-visual-signoff.schema.json 2.1 KB
- schemas/README.md 14 KB
- schemas/resume-package.schema.json 8.6 KB
- scripts/_json_cli.py 5.9 KB runs code
- scripts/archive_resume_artifacts.py 7.7 KB runs code
- scripts/career_store.py 26 KB runs code
- scripts/extract_resume.py 22 KB runs code
- scripts/pdf_pipeline.py 68 KB runs code
- scripts/record_pdf_visual_signoff.py 3.5 KB runs code
- scripts/render_resume.py 39 KB runs code
- scripts/resume_skill.py 12 KB runs code
- scripts/scan_repository.py 27 KB runs code
- scripts/validate_pdf.py 7.6 KB runs code
- scripts/validate_resume_package.py 33 KB runs code
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.
- 16d ago Changed · -2 lines ac8c7a1ea933
- 2mo ago Changed · +1 lines c91454109aa8
- 2mo ago First seen · 79 lines · 164 tokens per session scan A d5006cf56918
ai-resume-expert is a skill published in the GitHub repository cocoCzl/coco-skills (5 stars, last pushed 16d ago), licensed MIT. It adds 164 tokens to every session and 2,061 once invoked, about $0.0007 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 2026-08-31.
ai-resume-expert compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| ai-resume-expertcocoCzl/coco-skills | Skill | 5 | 16d ago | MIT | A | 164 | Claude Code, Codex |
| imagegenopenai/codex | Skill | 134k | today | Apache-2.0 | A | 113 | Codex |
| local-ai-agentsmicrosoft/ai-agents-for-beginners | Skill | 79k | yesterday | MIT | A | 200 | Claude Code, Codex |
| playwright-climicrosoft/playwright | Skill | 100k | yesterday | Apache-2.0 | A | 19 | Claude Code |
| browser-automationopenclaw/openclaw | Skill | 394k | today | MIT | A | 36 | Claude Code |
| systematic-debuggingobra/superpowers | Skill | 314k | 4d ago | MIT | A | 21 | Claude Code |
| next-cache-components-adoptionvercel/next.js | Skill | 143k | today | MIT | A | 95 | Claude Code |