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 ayushxx7/project-showcase-skill --skill linkedin-magicgit clone --depth 1 https://github.com/ayushxx7/project-showcase-skillWrote 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/ayushxx7/project-showcase-skill/linkedin-magic)<a href="https://agentmods.dev/skills/ayushxx7/project-showcase-skill/linkedin-magic"><img src="https://agentmods.dev/badge/skills/ayushxx7/project-showcase-skill/linkedin-magic/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/ayushxx7/project-showcase-skill/linkedin-magic"><img src="https://agentmods.dev/badge/skills/ayushxx7/project-showcase-skill/linkedin-magic.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 | $0.00022 | $0.00331 |
| Opus 5.5 | $0.00009 | $0.00132 |
| Sonnet 5.5 | $0.00004 | $0.00066 |
| Haiku 4.5 | $0.00002 | $0.00033 |
Grade A, and why
linkedin-magic 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 2mo 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
LinkedIn Magic
Transforms showcased projects into LinkedIn engagement.
Workflows
1. Prep
Run the main project-showcase skill first to ensure screenshots, health score, and README are ready.
2. Content Generation
4 post variants:
- 🚀 Viral Hype — Short, punchy, emoji-driven
- 🏗️ Deep Dive — Technical architecture focus
- 📈 Health Audit — Trust and quality metrics
- 💡 Story — Problem/solution narrative
Templates: references/post_templates.md
3. Asset Bundling
python3 linkedin-magic/scripts/bundle_assets.py
Copies best assets to linkedin_launch/ with descriptive names.
4. Hashtag Strategy
Core: #BuildingInPublic #AIAgents #OpenSource
Tech: #Python #React #AI (match your stack)
Checklist
- Showcase skill run (README updated, health score present)
- At least 2 high-res screenshots or 1 GIF
- 3 post variants generated
- Repo URL + Live App URL verified
-
linkedin_launch/folder ready
Pro Tips
- First Comment Rule — Post links in the first comment
- Lead with visuals — Images > text on LinkedIn
- Human-first tone — Avoid robotic AI language
What ships with it
3 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.
- 2mo ago First seen · 45 lines · 22 tokens per session scan A d6e3da62605f
linkedin-magic is a skill published in the GitHub repository ayushxx7/project-showcase-skill (5 stars, last pushed 5mo ago), licensed MIT. It adds 22 tokens to every session and 331 once invoked, about $0.0001 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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