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.
git clone --depth 1 https://github.com/yempik-ai/cowork-osWrote 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/commands/yempik-ai/cowork-os/linkedin-editor)<a href="https://agentmods.dev/commands/yempik-ai/cowork-os/linkedin-editor"><img src="https://agentmods.dev/badge/commands/yempik-ai/cowork-os/linkedin-editor/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/commands/yempik-ai/cowork-os/linkedin-editor"><img src="https://agentmods.dev/badge/commands/yempik-ai/cowork-os/linkedin-editor.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 | $0.00374 |
| Opus 5 | $0.00023 | $0.00187 |
| Sonnet 5 | $0.00009 | $0.00075 |
| Haiku 4.5 | $0.00005 | $0.00037 |
Grade A, and why
linkedin-editor 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 10d 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.
What it actually says
/cowork-os:linkedin-editor
Turn a source (article, case study, deck, draft, published post, or rough notes) into ready-to-publish LinkedIn posts. Optimize for impressions, comments, profile visits, and leads, never for likes. Use the workspace's context/positioning.md and context/tone_of_voice.md if present.
Run these 8 steps:
- Read the source fully. If it is a link, read it. Identify the core argument and the proof.
- Extract 20 distinct post ideas from it.
- Classify each idea by format: story, contrarian take, how-to, case or proof, list, or question.
- Pick the 5 strongest for this audience and goal.
- For each of the 5, write 3 hook variants (the first 150 characters that stop the scroll).
- Score each of the 5 on reach potential and lead potential (low, medium, high) with a one-line reason.
- Pick today's single best post and write it in full: founder voice, concrete, no hype, no long dash. Put any link in the first comment, not in the body.
- Deliver: the finished post, the 2 runner-up outlines, and the hook bank.
Source: $ARGUMENTS. If empty, ask for the source before starting.
End with a Memory Update if a content decision emerged (route reusable ideas to marketing/content.md).
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.
- 10d ago First seen · 23 lines · 47 tokens per session scan A 12764c029b60
linkedin-editor is a command published in the GitHub repository yempik-ai/cowork-os (70 stars, last pushed 1mo ago), licensed MIT. It adds 47 tokens to every session and 374 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 2026-08-30.
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