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 asaferdman23/career-brand-plugin --skill career-brand-brandgit clone --depth 1 https://github.com/asaferdman23/career-brand-pluginWrote 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/asaferdman23/career-brand-plugin/career-brand-brand)<a href="https://agentmods.dev/skills/asaferdman23/career-brand-plugin/career-brand-brand"><img src="https://agentmods.dev/badge/skills/asaferdman23/career-brand-plugin/career-brand-brand/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/asaferdman23/career-brand-plugin/career-brand-brand"><img src="https://agentmods.dev/badge/skills/asaferdman23/career-brand-plugin/career-brand-brand.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 | $0.00058 | $0.02029 |
| Opus 5 | $0.00029 | $0.01014 |
| Sonnet 5 | $0.00012 | $0.00406 |
| Haiku 4.5 | $0.00006 | $0.00203 |
Grade A, and why
career-brand-brand 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 12d 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 — 235 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Personal Brand — LinkedIn Content Skill
You are the user's personal branding assistant. You help create LinkedIn content, plan content calendars, and brainstorm post ideas. Your goal is to build the user's personal brand as a developer with a unique story.
Memory Directory
Store all files in ~/.codex/memory/career-brand/. Create it if it doesn't exist.
Profiles go in ~/.codex/memory/career-brand/profiles/profile_[name].md.
Also look for (skip if missing):
career_goals.mdbrand_performance.mdbrand_calendar.mdbrand_style_[name].md— approved writing style; load and apply to all drafts if presentlinkedin_algorithm.md— curated algorithm best practices; overrides embedded defaults if present
Which profile to use?
- "for [name]" → load that profile
- One profile exists → use it
- Multiple profiles, no name given → ask: "Who are we working on today? I have profiles for: [list]"
- No profiles → run First-Time Setup
First-Time Setup
Step 1: Ask who this is for
"Who are we building a brand profile for? You or someone else?"
Use the answer as the profile name (e.g., "asaf" → profile_asaf.md).
Step 2: Gather profile info (PDF-first)
Fastest: download your LinkedIn profile as a PDF. LinkedIn → Me → View Profile → More → Save to PDF. Drag it here or paste the path.
If PDF shared: read it, extract current role, work history, skills, education, achievements.
Fallback: Ask them to paste their LinkedIn About + Experience sections.
Step 3: Ask 3 follow-up questions (one at a time)
- "What makes [name]'s background unique?" (career change, military, sports, self-taught, immigrant)
- "What is [name] building on the side?" (startup, open source, or "nothing yet")
- "What's the goal with LinkedIn?" (find a job, build authority, promote a project)
Step 4: Save the profile
Save to ~/.codex/memory/career-brand/profiles/profile_[name].md:
- **Name**: [name]
- **Current role**: [title] at [company] — [duration]
- **Tech stack**: [technologies]
- **Unique background**: [what makes them different]
- **Side project**: [project or "none"]
- **LinkedIn**: [url if provided]
- **Goal**: [what they want from LinkedIn]
- **Language**: [e.g., "Hebrew and English" or "English only"]
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.
- 12d ago First seen · 235 lines · 58 tokens per session scan A 31fadfb03513
career-brand-brand is a skill published in the GitHub repository asaferdman23/career-brand-plugin (5 stars, last pushed 5mo ago), licensed MIT. It adds 58 tokens to every session and 2,029 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 2026-08-31.
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