cv-analyze

cv-analyze is a skill for Claude Code from xujingchen1996/research-app-toolkit. It costs 59 tokens per session (616 once invoked), scanned A, original, MIT.

A tool for reading a CV or resume and turning it into a structured profile for research or job applications. It can use a saved profile file so later application tasks can reuse the information.

In plain words
What is it for?
Use it to find a CV, read its contents, choose an output language, analyze the applicant profile, and save the result for other application-related skills.
Why use it?
It avoids repeatedly extracting the same education, experience, and skills from a resume, while keeping that information organized for related tasks.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the research-app-toolkit plugin — 9 skills, 9 commands, 1 agent, 1 hook shipped together

Good fit Use it to find a CV, read its contents, choose an output language, analyze the applicant profile, and save the result for other application-related skills.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/xujingchen1996/research-app-toolkit/cv-analyze
Install

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.

Any agent
npx skills add xujingchen1996/research-app-toolkit --skill cv-analyze
Clone the repo
git clone --depth 1 https://github.com/xujingchen1996/research-app-toolkit

Made for: Claude Code.

Or install research-app-toolkit, the plugin that ships this one along with the rest of its 9 skills, 9 commands, 1 agent, 1 hook.

Wrote 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.

agentmods badge for cv-analyze

README.md
[![agentmods](https://agentmods.dev/badge/skills/xujingchen1996/research-app-toolkit/cv-analyze/github.svg)](https://agentmods.dev/skills/xujingchen1996/research-app-toolkit/cv-analyze)
Your own site
<a href="https://agentmods.dev/skills/xujingchen1996/research-app-toolkit/cv-analyze"><img src="https://agentmods.dev/badge/skills/xujingchen1996/research-app-toolkit/cv-analyze/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.

agentmods 80×15 button for cv-analyze

Your own site · 80×15
<a href="https://agentmods.dev/skills/xujingchen1996/research-app-toolkit/cv-analyze"><img src="https://agentmods.dev/badge/skills/xujingchen1996/research-app-toolkit/cv-analyze.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 59 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 616 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00059 $0.00616
Opus 5 $0.00030 $0.00308
Sonnet 5 $0.00012 $0.00123
Haiku 4.5 $0.00006 $0.00062

Measured 9d ago against content hash 846fbc6a4481, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

Grade A, and why

cv-analyze 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.

codex/skills/cv-analyze/SKILL.md · 61 lines

How it starts

The opening of the file, as written. The whole thing — 61 lines — stays where its author put it; the contents beside it link to each section on GitHub.

CV Profile Analysis

Shared State

  • The shared memory file is located at ../../memory.md.
  • Read that file first at the beginning; if it does not exist, create a template with the same structure.
  • After profile analysis is completed, rewrite the entire memory.md and set cv_profile_analyzed in the frontmatter to true.

Language Rules

  • Support three output modes: zh, en, and bilingual.
  • If the user's current message explicitly specifies a language, prioritize the current message.
  • Otherwise read preferred_language from memory.md.
  • If it is still unclear, follow the user's current conversation language.
  • Section headings in memory.md should remain in a fixed English structure, while section content may be written in the selected language.

Workflow

  1. Confirm the CV file path:
    • First read cv_file_path from the memory.md frontmatter.
    • If it is empty, search the current working directory with rg --files for files related to cv, CV, resume, or Resume.
    • If it still cannot be found, directly ask the user for an explicit path in Chinese.
  2. Read the CV content:
    • For PDFs, first try command-line extraction such as pdftotext.
    • On macOS, fall back to textutil -convert txt -stdout.
    • If that still fails, use a Python library as a fallback extractor.
    • For DOCX files, prefer textutil, and use Python only when necessary.
  3. Extract and organize the following information:
    • Educational background
    • Technical skills
    • Research-related projects and experience
    • Work / internship experience
    • Awards and honors
    • Papers / outputs
  4. Based on that, provide:
    • Strengths for Applications
    • Areas for Improvement
    • Packaging Opportunities
  5. Write the result back to ../../memory.md:
    • update cv_file_path
    • update cv_profile_analyzed
    • reorganize all sections cleanly, rather than only appending to the end

Output Requirements

  • When reporting back to the user, prioritize:
    • the 3 strongest application strengths
    • the experience that best connects to research direction
    • 2 to 3 points that can be packaged more strongly

Read the full file on GitHub · 61 lines

Changes

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.

  1. 9d ago First seen · 61 lines · 59 tokens per session scan A 846fbc6a4481

Subscribe to this mod's changes

cv-analyze is a skill published in the GitHub repository xujingchen1996/research-app-toolkit (115 stars, last pushed 1mo ago), licensed MIT. It adds 59 tokens to every session and 616 once invoked, about $0.0003 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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