Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/LuizEduPP/Remembnpx agentmods add skills/luizedupp/rememb/resume-managerWrote 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/luizedupp/rememb/resume-manager)<a href="https://agentmods.dev/skills/luizedupp/rememb/resume-manager"><img src="https://agentmods.dev/badge/skills/luizedupp/rememb/resume-manager/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/luizedupp/rememb/resume-manager"><img src="https://agentmods.dev/badge/skills/luizedupp/rememb/resume-manager.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 | $0.00043 | $0.04692 |
| Opus 5.5 | $0.00017 | $0.01877 |
| Sonnet 5.5 | $0.00009 | $0.00938 |
| Haiku 4.5 | $0.00004 | $0.00469 |
Grade A, and why
resume-manager 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 1mo 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 — 730 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Resume Manager
Overview
This skill transforms the AI assistant into a comprehensive resume management system that maintains a structured database of your professional profile and generates tailored, professionally styled PDF resumes for specific job applications. The skill intelligently selects and highlights the most relevant experiences, projects, and skills based on the target role.
When to Use
Invoke this skill for resume-related tasks:
- Creating tailored resumes for job applications
- Updating professional experiences and projects
- Managing skills and certifications
- Tracking career progression
- Generating role-specific resumes
- Maintaining a comprehensive career portfolio
- Optimizing resume content for ATS systems
Core Workflow
Step 1: Check for Existing Data
Before any resume operations, check if the database is initialized:
python3 scripts/resume_db.py is_initialized
If output is "false", proceed to Step 2 (Initial Setup). If "true", proceed to Step 3 (Resume Operations).
Step 2: Initial Setup - Extract from Existing Resume
When no data exists, ask the user to provide their existing resume.
Prompt the User:
To help you create tailored resumes, I need to build a database of your professional
profile. Please provide your existing resume in one of these ways:
1. Upload your resume file (PDF, DOCX, or TXT)
2. Paste the content of your resume
3. Provide a link to your online resume/LinkedIn profile
I'll extract all the information and organize it in a structured database that I can
use to generate customized resumes for different job applications.
Extracting Data from Resume:
Once the user provides their resume, extract the following information:
1. Personal Information:
- Full name
- Email address
- Phone number
- Location (city, state/country)
- LinkedIn profile URL
- GitHub profile URL
- Personal website
- Professional summary/objective
2. Work Experience: For each role, extract:
- Position/Job title
- Company name
- Location
- Start date (format: "Mon YYYY" like "Jan 2022")
- End date (or "Present")
- Brief description
- Key highlights/achievements (bullet points)
- Technologies/tools used
What ships with it
5 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.
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.
- 1mo ago First seen · 730 lines · 43 tokens per session scan A 375b30dd8079
resume-manager is a skill published in the GitHub repository LuizEduPP/Rememb (5 stars, last pushed 2d ago), licensed MIT. It adds 43 tokens to every session and 4,692 once invoked, about $0.0002 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.
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