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 muggl3mind/career-manager --skill onboardinggit clone --depth 1 https://github.com/muggl3mind/career-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/muggl3mind/career-manager/onboarding)<a href="https://agentmods.dev/skills/muggl3mind/career-manager/onboarding"><img src="https://agentmods.dev/badge/skills/muggl3mind/career-manager/onboarding/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/muggl3mind/career-manager/onboarding"><img src="https://agentmods.dev/badge/skills/muggl3mind/career-manager/onboarding.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 | $0.00047 | $0.03150 |
| Opus 5.5 | $0.00019 | $0.01260 |
| Sonnet 5.5 | $0.00009 | $0.00630 |
| Haiku 4.5 | $0.00005 | $0.00315 |
Grade C, and why
onboarding scanned grade C with 2 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 28d 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.
Downloads and executes remote codehighSupply chain
curl | sh runs whatever the server returns today, which is not necessarily what it returned when this was reviewed.
If `uv` is not installed, install it first (official installer: macOS/Linux `curl -LsSf https://astral.sh/uv/install.sh | sh`, Windows `powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"`) Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
If `uv` is not installed, install it first (official installer: macOS/Linux `curl -LsSf https://astral.sh/uv/install.sh | sh`, Windows `powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"`) How it starts
The opening of the file, as written. The whole thing — 267 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Onboarding
Personalize the career-manager pipeline for a new user through a focused setup.
When to Use
- First time setting up the pipeline after cloning
- Career goals, target roles, or compensation targets have changed
- Want to regenerate search queries or scoring criteria
Flow
Step 0: Ensure Local Python Environment
If uv is not installed, install it first (official installer: macOS/Linux curl -LsSf https://astral.sh/uv/install.sh | sh, Windows powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"), then make sure it is on PATH for subsequent commands. A system Python is not required — uv downloads a managed Python automatically when none is available.
Before reading the resume or generating files, create a repo-local virtual environment if one doesn't already exist:
uv venv
uv pip install -r requirements.txt
Run all subsequent Python commands through uv run (e.g., uv run python3 scripts/smoke_test.py) so dependencies resolve against the repo-local .venv and never install into a global Python, Conda, or system environment.
Step 1: Get the Resume
Ask: "Where's your resume? Give me the file path (e.g., ~/Desktop/resume.docx)"
When the user provides a path:
- Verify the file exists and is
.docxor.pdf - Copy it to
cv-tailor/data/CV/Master CV/(create directory if needed) - Read the resume and extract: work history, skills, industries, seniority, education, accomplishments
If the file doesn't exist or isn't a supported format, ask again with guidance.
Do NOT ask the user to copy files manually. You handle the copy.
Step 2: One Question
Ask this single combined question:
"Based on your resume, I can see your background. I need four things your resume doesn't tell me:
- Target roles - What roles are you going after? Be specific about seniority (Senior PM, Director of Engineering, Solutions Architect) and domain.
- Salary floor - What's the minimum base salary you'd accept?
- Location - Remote, hybrid, or onsite? Open to relocation?
- Job markets - Which countries should I search for jobs in? (Default: United States)"
What ships with it
1 file 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.
- 28d ago Changed · +7 lines scan A → C 6c3918a27587
- 1mo ago First seen · 260 lines · 47 tokens per session scan A 8dc5446c4306
onboarding is a skill published in the GitHub repository muggl3mind/career-manager (24 stars, last pushed 27d ago), licensed MIT. It adds 47 tokens to every session and 3,150 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it C with 2 findings (downloads and executes remote code, makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
job-application-agent
JobAppAgent, the open-source AI job application agent. Finds, evaluates, fills, submits (only with the candidate's approval), and tracks a candidate's own job applications using a verified resume, evidence-based targeting, secure local profile storage, and browser automation. Use only when the user names JobAppAgent…
apply
Apply to a single job (URL or pasted page) with fit review, or score and apply the job links pasted into an apply campaign when no argument is given.
scan-inbox
Classify unscanned mailbox messages, fuzzy-match each to an existing application, and write the proposal back. The user approves in /inbox.
resume-campaign
Resume a paused JobPilot campaign by id. Re-flips the campaign to inprogress and replays the apply loop on any remaining approved jobs without re-asking for fit confirmation.
autopilot-jobhunt
Run a job hunt in one agentic pass — scan configured company careers pages, score postings against the user's resume, draft a tailored resume + cover letter per chosen role (never applies), and export matches. Trigger when the user says /autopilot-jobhunt or asks to scan/find jobs or draft an application.
amlei-portfolio
A workflow for creating and checking a job portfolio: a collection of project case studies and evidence for employers. It selects real work, explains the person's contribution and decisions, and links claims to supporting material.