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 ZhixiangLuo/10xProductivity --skill linkedin-engagementgit clone --depth 1 https://github.com/ZhixiangLuo/10xProductivityWrote 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/zhixiangluo/10xproductivity/linkedin-engagement)<a href="https://agentmods.dev/skills/zhixiangluo/10xproductivity/linkedin-engagement"><img src="https://agentmods.dev/badge/skills/zhixiangluo/10xproductivity/linkedin-engagement/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/zhixiangluo/10xproductivity/linkedin-engagement"><img src="https://agentmods.dev/badge/skills/zhixiangluo/10xproductivity/linkedin-engagement.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 | $0.00107 | $0.00184 |
| Opus 5 | $0.00053 | $0.00092 |
| Sonnet 5 | $0.00021 | $0.00037 |
| Haiku 4.5 | $0.00011 | $0.00018 |
Grade A, and why
linkedin-engagement 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.
What it actually says
Canonical:
workflows/linkedin_automation/linkedin_engagement.md(in this repo). This file is a thin pointer for Claude Code skill discovery — load the canonical workflow doc for the full agent loop, setup, privacy notes, and risk warning.
Read workflows/linkedin_automation/linkedin_engagement.md and follow 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.
- 12d ago First seen · 9 lines · 107 tokens per session scan A 0e992a95262d
linkedin-engagement is a skill published in the GitHub repository ZhixiangLuo/10xProductivity (474 stars, last pushed 2mo ago), licensed MIT. It adds 107 tokens to every session and 184 once invoked, about $0.0005 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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