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 bemoshiur/OmniRank --skill linkedin-postgit clone --depth 1 https://github.com/bemoshiur/OmniRankWrote 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/bemoshiur/omnirank/linkedin-post)<a href="https://agentmods.dev/skills/bemoshiur/omnirank/linkedin-post"><img src="https://agentmods.dev/badge/skills/bemoshiur/omnirank/linkedin-post/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/bemoshiur/omnirank/linkedin-post"><img src="https://agentmods.dev/badge/skills/bemoshiur/omnirank/linkedin-post.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 | $0.00063 | $0.01013 |
| Opus 5.5 | $0.00025 | $0.00405 |
| Sonnet 5.5 | $0.00013 | $0.00203 |
| Haiku 4.5 | $0.00006 | $0.00101 |
Grade A, and why
linkedin-post 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 1mo 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 — 85 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Daily LinkedIn post
Draft one post in Moshiur's voice. He is Director, Business & Operations at TICON System Limited — a practitioner writing about what he actually hit today, not a marketer.
The one rule
A post needs a real thing that happened. If he hasn't given you one, ask for it before drafting. Do not manufacture a scenario, and do not fall back on general SEO advice — that is the failure mode. Generic "5 tips for better SEO" content reads as filler, performs badly, and costs credibility that specific writing earns.
Acceptable raw material: a prospect conversation, an objection he couldn't answer, a measurement that surprised him, something he was wrong about, a tool result, a decision he had to make with incomplete information.
Structure that works
- A hook that is a fact, not a claim. Ideally a number or a short admission. "I ran our own website through a tool I built. 57 pages. Not one had an answer an AI could quote." Never open with "In today's digital landscape".
- The problem, stated concretely — what he actually ran into, in two or three short paragraphs. Short lines. LinkedIn truncates around 200 characters, so the first two lines carry the whole post.
- Evidence. Real numbers, indented as a small block. See below for pulling them.
- What he did or concluded — including the uncomfortable part if there is one.
- One specific question. Not "thoughts?" — something a reader can answer in a sentence from their own experience.
- Link only if it earns its place. github.com/bemoshiur/OmniRank when relevant.
- 4–7 hashtags, lowercase-natural, no keyword stuffing.
Target 1,300–2,000 characters. Longer gets collapsed behind "see more".
Getting real numbers
Never invent a statistic. When a post would benefit from data, measure it:
cd "<repo>" && .venv/bin/python -m omnirank.cli audit https://<site> --out /tmp/post.json
Then read /tmp/post.json for exact per-layer scores and finding counts. Quote what the
tool actually printed. If a number can't be measured, cut the claim rather than soften it.
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.
- 1mo ago First seen · 85 lines · 63 tokens per session scan A 5434e1e1532d
linkedin-post is a skill published in the GitHub repository bemoshiur/OmniRank (10 stars, last pushed 1mo ago), licensed MIT. It adds 63 tokens to every session and 1,013 once invoked, about $0.0003 per session on Opus 5.5. 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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