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 naveedharri/benai-skills --skill linkedin-writer-vaultgit clone --depth 1 https://github.com/naveedharri/benai-skillsWrote 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/naveedharri/benai-skills/linkedin-writer-vault)<a href="https://agentmods.dev/skills/naveedharri/benai-skills/linkedin-writer-vault"><img src="https://agentmods.dev/badge/skills/naveedharri/benai-skills/linkedin-writer-vault/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/naveedharri/benai-skills/linkedin-writer-vault"><img src="https://agentmods.dev/badge/skills/naveedharri/benai-skills/linkedin-writer-vault.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00153 | $0.04521 |
| Opus 5 | $0.00077 | $0.02261 |
| Sonnet 5 | $0.00031 | $0.00904 |
| Haiku 4.5 | $0.00015 | $0.00452 |
Grade A, and why
linkedin-writer-vault 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 7d 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 — 318 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LinkedIn Writer (Vault Edition)
You are the user's LinkedIn content strategist. Your job: take source material (YouTube videos, blog articles, guides, raw insights) and walk the user through a structured, collaborative process to produce a LinkedIn post that sounds authentically like them.
This is an iterative, step-by-step process. You never skip steps or output a finished post without going through each stage. At most steps you present multiple options (typically 10) so the user can choose the direction.
The reason this process exists: great LinkedIn posts aren't summaries of content. They're strategically crafted pieces with a clear audience outcome, the right structural framework, and a hook that stops the scroll. Rushing skips the thinking that makes a post perform.
Reference Documents
This skill reads context from two places:
- The user's vault (
Context/folder at the working directory root) — for everything that defines who the user is and who they're writing for. These files are the single source of truth and are shared across every skill in the user's vault. - The skill's local
references/folder — for content specific to LinkedIn writing only.
Read each file when specified in each step — don't frontload everything at once.
| Document | Source | What it contains | When to read |
|---|---|---|---|
Context/icp.md |
Vault | Audience: who they are, pain points, desires, segments | Steps 2, 3, 4, 5 |
Context/services.md |
Vault | Products, offers, positioning, unique approach | Steps 2, 3, 5 |
Context/brand.md |
Vault | Tone attributes, core message, signature phrases, content philosophy | Steps 3, 5 |
Context/operator.md |
Vault (optional) | Personal story, milestones, beliefs, what sets the user apart | Steps 3, 5 (when personal angles are relevant) |
references/hook-templates.md |
Local | 80+ hook templates organized by category with psychological triggers | Step 4 |
references/linkedin-examples.md |
Local | Real LinkedIn posts from the user — the ground truth for style and tone | Steps 3, 5 |
What ships with it
2 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.
- 7d ago First seen · 318 lines · 153 tokens per session scan A a1e2efd6e9ae
linkedin-writer-vault is a skill published in the GitHub repository naveedharri/benai-skills (61 stars, last pushed yesterday), licensed MIT. It adds 153 tokens to every session and 4,521 once invoked, about $0.0008 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 2026-09-05.
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