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 nicepkg/ai-workflow --skill linkedin-announcement-generatorgit clone --depth 1 https://github.com/nicepkg/ai-workflowWrote 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/nicepkg/ai-workflow/linkedin-announcement-generator)<a href="https://agentmods.dev/skills/nicepkg/ai-workflow/linkedin-announcement-generator"><img src="https://agentmods.dev/badge/skills/nicepkg/ai-workflow/linkedin-announcement-generator/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/nicepkg/ai-workflow/linkedin-announcement-generator"><img src="https://agentmods.dev/badge/skills/nicepkg/ai-workflow/linkedin-announcement-generator.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 | $0.00062 | $0.03933 |
| Opus 5.5 | $0.00025 | $0.01573 |
| Sonnet 5.5 | $0.00012 | $0.00787 |
| Haiku 4.5 | $0.00006 | $0.00393 |
Grade A, and why
linkedin-announcement-generator 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 — 553 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LinkedIn Announcement Generator
Overview
This skill automates the creation of professional LinkedIn announcements for intelligent textbooks. It analyzes book metrics from the docs/learning-graph/ directory, gathers statistics about chapters, concepts, and educational resources, and generates engaging announcement text with relevant hashtags and links to the published site.
The announcements are designed to highlight the scope and completeness of the textbook, showcase its educational features, and attract educators, students, and learning professionals to the content.
When to Use This Skill
Use this skill when:
- Publishing a completed intelligent textbook to GitHub Pages
- Announcing major milestones (e.g., "First 10 chapters complete!")
- Promoting updated or newly added content
- Sharing the textbook with the educational technology community
- Preparing social media posts for course launches
- Creating announcements for conference presentations or workshops
- Building awareness for open educational resources
Prerequisites
The intelligent textbook project should have:
- A
docs/learning-graph/book-metrics.mdfile containing textbook statistics - A
mkdocs.ymlfile with site_name, site_url, and site_description - Deployed site on GitHub Pages (or another hosting platform)
- Optional:
docs/learning-graph/chapter-metrics.mdfor chapter-level details - Optional:
docs/course-description.mdfor audience and topic information
Workflow
Step 1: Gather Book Metadata
Extract key information from the project configuration:
-
Read
mkdocs.ymlto get:site_name- Title of the textbooksite_url- Live site URL (typically GitHub Pages)site_description- Brief description of the textbookrepo_url- GitHub repository URL
-
Read
docs/course-description.md(if it exists) to get:- Target audience (grade level, prerequisites)
- Subject matter/topic
- Learning objectives
- Course context
Example extraction:
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 · 553 lines · 62 tokens per session scan A d2fd2b8f2c21
linkedin-announcement-generator is a skill published in the GitHub repository nicepkg/ai-workflow (288 stars, last pushed 9mo ago), licensed MIT. It adds 62 tokens to every session and 3,933 once invoked, about $0.0002 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 2026-08-30.
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