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Agent Skill · 运维

tamar-resume-tailor(添马简历裁缝)

tamar-resume-tailor 用于补充运维相关能力,适合在 OpenClaw 中需要让 Agent 承接运维相关任务时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

tamar-resume-tailor 是运维类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为接触敏感数据;许可证为 MIT-0,第三方扫描已通过,按安装量在运维类的 954 个 Skill 里排第 565。

总安装 5,284周安装 218GitHub Stars 1下载 1,727核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
MIT-0
版本
1.4.1

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:tamar-resume-tailor(添马简历裁缝)
来源仓库:https://github.com/evgenyshneyderman/tamar-resume-tailor
安装命令:
openclaw skills install tamar-resume-tailor
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install tamar-resume-tailor

这个 Skill 是做什么的

运维类 Skill 处理部署、监控、日志和云资源,多数需要较高的执行权限。

使用 Tamar AI API 为特定工作申请定制简历。适合在 OpenClaw 中需要让 Agent 承接运维相关任务时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

注意:涉及外部 API 调用时需确保网络连通性和密钥配置正确。

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 OpenClaw;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 openclaw skills install tamar-resume-tailor;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要接触敏感数据,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

tamar-resume-tailor 怎么安装?

在终端执行 openclaw skills install tamar-resume-tailor。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

tamar-resume-tailor 支持哪些 AI 工具?

公开资料标注支持 OpenClaw。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

tamar-resume-tailor 需要什么权限?

权限级别是接触敏感数据。该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

tamar-resume-tailor 是免费的吗?

来源标注的许可证是 MIT-0。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

tamar-resume-tailor 用的人多吗?

公开数据是累计安装 5,284 次,近一周安装 218 次,下载 1,727 次,GitHub 1 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

tamar-resume-tailor 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 专注 OpenClaw 一种宿主,触发方式和使用习惯不用来回切换。
  • 许可证明确为 MIT-0,商用和分发边界好判断。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 需要接触敏感数据,装到常用环境前要先评估影响面。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 OpenClaw,想直接扩展它能力的人。
  • 需要处理运维类任务、又不想每次都手写提示词的人。
  • 需要许可证清楚、并且有第三方扫描结果的团队项目。

不太适合

  • 环境里有大量密钥、凭证,不方便给第三方 Skill 读取的人。
  • 需要厂商级技术支持和 SLA 的生产系统。

运维类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
docker-essentials

Docker 基础 · OpenClaw

1,005,15130接触敏感数据MIT-0通过
playwright-scraper-skill

Playwright scraper 技能 · OpenClaw

923,55754接触敏感数据MIT-0有提醒
moltguard

moltguard 搜索 · OpenClaw

825,096110接触敏感数据MIT-0通过
finance-news

财经新闻 · OpenClaw

275,51210需要联网MIT-0有提醒
security-monitor

安全监视器 · OpenClaw

224,1695需要联网MIT-0有提醒
tamar-resume-tailor

添马简历裁缝 · 本页

5,2841接触敏感数据MIT-0通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

name
tamar-resume-tailor
description
Tailor resumes for specific job applications using the Tamar AI API
metadata
{"openclaw":{"requires":{"bins":["tamar"],"env":["TAMAR_API_KEY"]},"primaryEnv":"TAMAR_API_KEY","install":[{"id":"npm","kind":"node","package":"tamar-cli","bins":["tamar"],"label":"Install Tamar CLI (npm)"}]}}

Tamar Resume Tailoring

Use the tamar CLI to tailor resumes for specific job applications via the Tamar API.

Triggers

Activate when the user says anything like:

  • "tailor my resume"
  • "customize my resume for"
  • "help me apply for this job"
  • "make a resume for this role"
  • "adapt my resume"
  • "target my resume at"

Prerequisites

  • The tamar CLI must be installed by the user before using this skill: npm install -g tamar-cli
  • An API key must be configured. Verify by running tamar status. If it fails with "No API key configured", ask the user to run tamar auth --key <their-key> (keys are obtained from https://ask-tamar.com → Profile → API Keys)
  • Do NOT read or inspect ~/.tamarrc directly — use tamar status to check auth

Pipeline

1. Check if the user has an experience profile

tamar profile

Shows existing profiles with name, role, seniority, skills, and enrichment depth. If a profile exists, use its ID with tamar tailor --profile <id> for higher-quality output. If none exists, proceed to step 2.

2. Ensure user has a resume uploaded

If the user has a resume file and hasn't uploaded one yet:

tamar upload <resume-file>

3. Get the job description

Ask the user for the job description. It can be:

  • A URL (LinkedIn, company careers page, etc.)
  • A file path — read the file content first
  • Pasted text

4. Tailor the resume

IMPORTANT — Input safety: Never interpolate user-provided strings directly into shell commands. Always write job descriptions or multi-word arguments to a temporary file and pass the file path, or use the -- separator and single-quote the argument. This prevents shell injection from malicious input.

# Safe: write JD to a temp file, pass the file
echo '<job description text>' > /tmp/jd.txt
tamar tailor --job /tmp/jd.txt

# Safe: single-quote the argument to prevent shell expansion
tamar tailor --job 'https://example.com/jobs/12345'

If the user also provides a resume file and hasn't uploaded before:

tamar tailor --job /tmp/jd.txt --resume '<resume-file>'

5. Review the output

The command returns JSON with:

  • id — the generated resume ID (stored for later commands)
  • quality — "enriched" (has profile) or "basic" (resume-only)
  • analysis — job match analysis
  • changes — list of changes made

Present the analysis summary conversationally. Highlight key matches and gaps.

6. Handle feedback

If the user wants changes:

Write the feedback to a temp file to avoid shell injection:

echo '<feedback text>' > /tmp/feedback.txt
tamar feedback "$(cat /tmp/feedback.txt)"

Or for a specific resume:

tamar feedback "$(cat /tmp/feedback.txt)" --id '<resume-id>'

7. Download the result

tamar download                    # PDF (default)
tamar download --format json      # structured JSON

The PDF is saved to the current directory. Tell the user the file path.

Error Handling

ErrorAction
No API key configuredGuide user to run tamar auth --key <key>
401 Invalid or expiredPrompt to re-run tamar auth with a new key
422 Could not parse URLSite blocks scraping (common with LinkedIn). Paste the JD text instead
429 Rate limitedTell user to wait and retry
402 Plan limit reachedDirect to https://ask-tamar.com for upgrade
Network error / timeoutCheck connection. AI calls can take 15–60s — ensure client timeout is ≥120s

Quality Notes

  • Enriched quality (user has an experience profile — check via tamar profile) = higher quality tailoring
  • Basic quality (resume-only, no profile) = still useful but less nuanced
  • If user has no profile, suggest building one at https://ask-tamar.com via the interactive Q&A, or use the enrichment API flow

Example Interaction

User: Can you tailor my resume for this job? https://linkedin.com/jobs/12345

Agent: Let me tailor your resume for that role.

[runs: tamar tailor --job 'https://linkedin.com/jobs/12345']

Agent: Done! Here's what I found:
- Quality: enriched (used your experience profile)
- Key alignments: Python, data pipelines, team leadership
- Adjusted: Reframed your experience to emphasize data platform work
- Gaps: Kubernetes — no production experience listed

Want me to tweak anything? I can also download the PDF for you.

平台分布

OpenClaw

70.38%
按下载量换算1,215

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

通过

权限和风险

接触敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。目前只有一个公开来源,建议同时核对源仓库。

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