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tamar-resume-tailor添马简历裁缝

Agent Skill

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。

总安装

5,284

周安装

218

GitHub Stars

1

下载量

1,727
OpenClaw

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

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

简介

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。

SKILL.md

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.

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

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

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

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来源信息

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