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 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/brand)<a href="https://agentmods.dev/skills/asaferdman23/career-brand-plugin/brand"><img src="https://agentmods.dev/badge/skills/asaferdman23/career-brand-plugin/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/brand"><img src="https://agentmods.dev/badge/skills/asaferdman23/career-brand-plugin/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 | $0.00034 | $0.05044 |
| Opus 5 | $0.00017 | $0.02522 |
| Sonnet 5 | $0.00007 | $0.01009 |
| Haiku 4.5 | $0.00003 | $0.00504 |
Grade A, and why
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 11d 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 — 470 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.
First: Load Context
Before doing anything, find the current project's memory directory:
ls ~/.claude/projects/*/memory/profiles/ 2>/dev/null | head -1
Look for profile files in that profiles/ subdirectory. Each person gets their own file: profile_[name].md (e.g., profile_asaf.md, profile_dana.md).
Also look for these files in the project memory directory (skip any that don't exist yet):
career_goals.mdbrand_performance.mdbrand_calendar.mdbrand_automation.mdbrand_style_[name].md(approved writing style — load and apply to all drafts if present)linkedin_algorithm.md(curated algorithm best practices — overrides embedded defaults if present)
Which profile to use?
- If the user says "for [name]" or "for him/her", load that person's profile from the
profiles/directory. - If only one profile exists, use it by default.
- If multiple profiles exist and the user didn't specify, ask: "Who are we working on today? I have profiles for: [list names]"
- If no profiles exist, run the First-Time Setup below.
First-Time Setup
If no profile exists for the person, set one up:
Step 1: Ask who this is for
Ask: "Who are we building a brand profile for? You or someone else?"
Use the answer to set the profile name (e.g., "asaf", "dana"). This becomes the filename: profile_[name].md.
Step 2: Gather profile information (PDF-first)
Ask the user to download their LinkedIn profile as a PDF and share it:
The fastest way: download your LinkedIn profile as a PDF. On LinkedIn → Me → View Profile → More → Save to PDF. Then drag it here or paste the path. I'll extract everything automatically.
This is the primary flow — gives the most complete data in one step.
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.
- 11d ago First seen · 470 lines · 34 tokens per session scan A 76c2109c227a
brand is a skill published in the GitHub repository asaferdman23/career-brand-plugin (5 stars, last pushed 5mo ago), licensed MIT. It adds 34 tokens to every session and 5,044 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.
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