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 xujingchen1996/research-app-toolkit --skill cv-analyzegit clone --depth 1 https://github.com/xujingchen1996/research-app-toolkitWrote 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/xujingchen1996/research-app-toolkit/cv-analyze)<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.
<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>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 | $0.00059 | $0.00616 |
| Opus 5 | $0.00030 | $0.00308 |
| Sonnet 5 | $0.00012 | $0.00123 |
| Haiku 4.5 | $0.00006 | $0.00062 |
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.
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.mdand setcv_profile_analyzedin the frontmatter totrue.
Language Rules
- Support three output modes:
zh,en, andbilingual. - If the user's current message explicitly specifies a language, prioritize the current message.
- Otherwise read
preferred_languagefrommemory.md. - If it is still unclear, follow the user's current conversation language.
- Section headings in
memory.mdshould remain in a fixed English structure, while section content may be written in the selected language.
Workflow
- Confirm the CV file path:
- First read
cv_file_pathfrom thememory.mdfrontmatter. - If it is empty, search the current working directory with
rg --filesfor files related tocv,CV,resume, orResume. - If it still cannot be found, directly ask the user for an explicit path in Chinese.
- First read
- 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.
- For PDFs, first try command-line extraction such as
- Extract and organize the following information:
- Educational background
- Technical skills
- Research-related projects and experience
- Work / internship experience
- Awards and honors
- Papers / outputs
- Based on that, provide:
Strengths for ApplicationsAreas for ImprovementPackaging Opportunities
- 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
- update
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
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 · 61 lines · 59 tokens per session scan A 846fbc6a4481
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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