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 oyi77/1ai-skills --skill multi-platform-distributiongit clone --depth 1 https://github.com/oyi77/1ai-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/oyi77/1ai-skills/multi-platform-distribution)<a href="https://agentmods.dev/skills/oyi77/1ai-skills/multi-platform-distribution"><img src="https://agentmods.dev/badge/skills/oyi77/1ai-skills/multi-platform-distribution/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/oyi77/1ai-skills/multi-platform-distribution"><img src="https://agentmods.dev/badge/skills/oyi77/1ai-skills/multi-platform-distribution.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 | $0.00048 | $0.03361 |
| Opus 5 | $0.00024 | $0.01681 |
| Sonnet 5 | $0.00010 | $0.00672 |
| Haiku 4.5 | $0.00005 | $0.00336 |
Grade A, and why
multi-platform-distribution 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 8d 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 — 449 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Overview
Transform a single piece of content into platform-specific formats, schedule distribution across channels, and track performance. Saves 80% of content marketing time by automating the repurposing pipeline. One blog post becomes a Twitter thread, LinkedIn article, YouTube script, newsletter edition, TikTok script, podcast outline, Reddit post, and Hacker News submission — each optimized for its platform's audience and format.
Required Tools
pandocfor format conversion- Python with
markdown/jinja2for templating - Twitter API v2 (now X API) for thread posting
- LinkedIn API for article publishing
- YouTube Data API for video metadata
- Substack/Beehiiv API for newsletter
- TikTok Content Posting API
- Reddit API for post submission
- Buffer/Hootsuite API for scheduling
- Google Analytics / Plausible for tracking
Capabilities
- Parse source content into semantic blocks (intro, key points, examples, conclusion)
- Generate platform-specific versions respecting character limits, tone, and format
- Auto-generate Twitter threads with proper numbering and hooks
- Create LinkedIn posts with engagement-optimized structure
- Generate YouTube scripts with timestamps and B-roll suggestions
- Produce TikTok scripts with hook, value, CTA structure
- Schedule posts at optimal times per platform
- Track cross-platform performance in unified dashboard
When to Use
Trigger phrases:
-
"multi platform distribution"
-
"One piece of content becomes 10 — blog to Twitter thread, LinkedIn article, YouT"
-
"I wrote a blog post, distribute it everywhere"
-
"Turn this article into a Twitter thread and LinkedIn post"
-
"Repurpose our latest podcast episode into 10 pieces of content"
-
"Create a content calendar from our existing content library"
-
"This newsletter issue should also go on LinkedIn and as a blog"
When NOT to Use
- Task is about content strategy, not creation (use strategy skills)
- Task is about content distribution (use distribution skills)
- You need to analyze content performance (use analytics skills)
- Task is about content moderation (use moderation tools)
- You don't have content guidelines
- Task requires domain expertise (consult experts)
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.
- 8d ago First seen · 449 lines · 48 tokens per session scan A 6a2def509ec8
multi-platform-distribution is a skill published in the GitHub repository oyi77/1ai-skills (12 stars, last pushed today), licensed MIT. It adds 48 tokens to every session and 3,361 once invoked, about $0.0002 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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