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 agentmods add skills/luizedupp/rememb/resume-managernpx skills add LuizEduPP/Rememb --skill resume-managergit clone --depth 1 https://github.com/LuizEduPP/RemembWrote 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.svg" alt="Measured on agentmods" 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 | $0.00043 | $0.04692 |
| Opus 5 | $0.00022 | $0.02346 |
| Sonnet 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 5d 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.
- 5d ago First seen · 730 lines · 43 tokens per session scan A 375b30dd8079
resume-manager is a skill published in the GitHub repository LuizEduPP/Rememb (4 stars, last pushed 1mo ago), licensed MIT. It adds 43 tokens to every session and 4,692 once invoked, about $0.0002 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 2026-08-31.
Other skills, from other repositories
Effective Memory
The essential habits for an AI agent with memory — session bookends, learning triggers, verification, safety, and the operational discipline that turns raw recall into compounding intelligence. Pinned, always-injected.
recall
Recall this repository's OwnMem local memory before changing code, and keep it healthy. Use when a repository contains .ownmem/, when past debugging lessons could apply ("have we hit this before", "why is it done this way"), or when the user mentions ownmem, project memory, or recalling across sessions.
init
Install or update OwnMem in the current repository. Use when the user asks to set up OwnMem, add local project memory for coding agents, or refresh an existing OwnMem installation after a version bump.
honcho-integration
Integrate Honcho memory into existing Python or TypeScript codebases. Use when adding Honcho SDK, setting up peers, configuring sessions, and accessing Honcho's representation.
honcho-memory
Concepts and strategy for using a connected Honcho as persistent memory of the user — the recall/record loop and session and peer design. Start here to understand how Honcho memory works, then connect — via a first-class integration for your environment if one exists (preferred), or raw MCP tools (covered here) or the…
verify
Build, launch, and drive a local Honcho stack to verify a change at its runtime surface (the /v3 HTTP API and the deriver queue). Use when verifying a diff or confirming a change works in the running app.