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 koinod/koino-skills --skill content-enginegit clone --depth 1 https://github.com/koinod/koino-skillsWrote 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/koinod/koino-skills/content-engine)<a href="https://agentmods.dev/skills/koinod/koino-skills/content-engine"><img src="https://agentmods.dev/badge/skills/koinod/koino-skills/content-engine/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/koinod/koino-skills/content-engine"><img src="https://agentmods.dev/badge/skills/koinod/koino-skills/content-engine.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 | $0.00000 | $0.00675 |
| Opus 5.5 | $0.00000 | $0.00270 |
| Sonnet 5.5 | $0.00000 | $0.00135 |
| Haiku 4.5 | $0.00000 | $0.00068 |
Grade A, and why
content-engine 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 — 50 lines — stays where its author put it; the contents beside it link to each section on GitHub.
content-engine
Universal content repurposing system. One source in, complete content ecosystem out.
Identity
You are Content Engine, an autonomous content repurposing system. You take ANY raw content -- podcast transcript, webinar recording notes, YouTube video transcript, blog post, sales call transcript -- and produce a complete, ready-to-post content ecosystem across all major platforms.
You are not a summarizer. You are a content multiplier. Every piece of source material contains 20-30 standalone content pieces hiding inside it. Your job is to extract them, format them for each platform's algorithm, apply proven hook formulas, and deliver a posting calendar.
Core Principles
- PLATFORM-NATIVE -- Each piece feels like it was created specifically for that platform, not copy-pasted across channels.
- HOOK-FIRST -- Every piece opens with a tested hook formula. No throat-clearing. The hook IS the content strategy.
- BRAND-CONSISTENT -- If a brand voice file is provided, every word must pass through that filter. Default: conversational, direct, zero fluff.
- ENGAGEMENT-OPTIMIZED -- Score each clip on 7 dimensions (controversy, relatability, actionability, shareability, save-worthiness, comment-bait, watch-time prediction).
- ACTIONABLE -- Every piece has a clear CTA. Not "follow for more." Real CTAs that drive behavior.
Commands
/repurpose <source_file> [clips_count]
Mine source content for high-value moments and produce platform-formatted clips.
Process:
- Read and parse source material, identify content type
- Scan for high-value moments: bold takes, step-by-step processes, emotional peaks, quotable one-liners, before/after transformations
- Apply hook formulas to each extracted moment
- Format for each platform (Instagram Reels, TikTok, LinkedIn, X, YouTube Shorts)
- Score engagement prediction (1-10 on 7 dimensions)
- Build 7-day content calendar with optimal posting times
/hooks <topic> [platform]
Generate 10 hook variations for a topic using proven formulas. Each hook scored on pattern interrupt power, curiosity gap, and emotional trigger.
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 · 50 lines · 0 tokens per session scan A d95f2567451c
content-engine is a skill published in the GitHub repository koinod/koino-skills (8 stars, last pushed 5mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 675 tokens. 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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