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 Kevin-Liu-01/Agent-Machines --skill social-contentgit clone --depth 1 https://github.com/Kevin-Liu-01/Agent-MachinesWrote 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/kevin-liu-01/agent-machines/social-content)<a href="https://agentmods.dev/skills/kevin-liu-01/agent-machines/social-content"><img src="https://agentmods.dev/badge/skills/kevin-liu-01/agent-machines/social-content/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/kevin-liu-01/agent-machines/social-content"><img src="https://agentmods.dev/badge/skills/kevin-liu-01/agent-machines/social-content.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 | $0.00117 | $0.01131 |
| Opus 5 | $0.00059 | $0.00566 |
| Sonnet 5 | $0.00023 | $0.00226 |
| Haiku 4.5 | $0.00012 | $0.00113 |
Grade A, and why
social-content 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 9d 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 — 93 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Social Content
Expert social media content creation, scheduling, and optimization across all major platforms.
Kevin-specific context: Read the content-strategy skill for Kevin's positioning arc, content pillars, and quality gates. Read the social-draft skill for X/LinkedIn drafting rules and anti-patterns. This skill provides the general framework; those provide Kevin's personalized layer.
Platform Quick Reference
| Platform | Best For | Frequency | Key Format |
|---|---|---|---|
| B2B, thought leadership | 3-5x/week | Carousels, stories | |
| X | Tech, real-time, community | 3-10x/day | Threads, hot takes |
| Visual brands, lifestyle | 1-2 posts + Stories daily | Reels, carousels | |
| TikTok | Brand awareness, younger audiences | 1-4x/day | Short-form video |
| Communities, local businesses | 1-2x/day | Groups, native video |
Content Pillars Framework
Build content around 3-5 pillars aligned with expertise and audience. Example for a SaaS founder:
| Pillar | % | Topics |
|---|---|---|
| Industry insights | 30% | Trends, data, predictions |
| Behind-the-scenes | 25% | Building the company, lessons |
| Educational | 25% | How-tos, frameworks, tips |
| Personal | 15% | Stories, values, hot takes |
| Promotional | 5% | Product updates, offers |
Hook Formulas
Curiosity: "I was wrong about [common belief]." / "The real reason [outcome] isn't what you think." / "[Result] — and it only took [time]."
Story: "Last week, [unexpected thing] happened." / "I almost [failure]." / "3 years ago, I [past]. Today, [present]."
Value: "How to [outcome] (without [pain]):" / "[Number] [things] that [outcome]:" / "Stop [mistake]. Do this instead:"
Contrarian: "Unpopular opinion: [bold statement]" / "[Common advice] is wrong. Here's why:"
Content Repurposing System
Turn one piece of content into many. Extract "content atoms" — self-contained moments from long-form content:
| Atom Type | What to Look For | Best Platform |
|---|---|---|
| Quotable moment | Bold claim, hot take (15-60s) | X, LinkedIn, TikTok |
| Story arc | Setup, conflict, resolution (60-90s) | Reels, TikTok, Shorts |
| Tactical tip | Specific how-to (30-60s) | LinkedIn, Shorts |
| Controversial take | Contrarian opinion | X, LinkedIn |
| Data/stat callout | Surprising number | LinkedIn carousel, X |
| Behind-the-scenes | Authentic moments | Stories, TikTok |
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.
- 9d ago First seen · 93 lines · 117 tokens per session scan A c540f1005664
social-content is a skill published in the GitHub repository Kevin-Liu-01/Agent-Machines (29 stars, last pushed yesterday), licensed MIT. It adds 117 tokens to every session and 1,131 once invoked, about $0.0006 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 2026-09-03.
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