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 SkillMedev/social-media-studio --skill cross-platform-reformattergit clone --depth 1 https://github.com/SkillMedev/social-media-studioWrote 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/skillmedev/social-media-studio/cross-platform-reformatter)<a href="https://agentmods.dev/skills/skillmedev/social-media-studio/cross-platform-reformatter"><img src="https://agentmods.dev/badge/skills/skillmedev/social-media-studio/cross-platform-reformatter/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/skillmedev/social-media-studio/cross-platform-reformatter"><img src="https://agentmods.dev/badge/skills/skillmedev/social-media-studio/cross-platform-reformatter.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 | $0.00137 | $0.01255 |
| Opus 5 | $0.00068 | $0.00628 |
| Sonnet 5 | $0.00027 | $0.00251 |
| Haiku 4.5 | $0.00014 | $0.00126 |
Grade A, and why
Cross-Platform Reformatter 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 10d 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.
This is a copy
100% identical to Cross-Platform Reformatter — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 74 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Cross-Platform Reformatter
Take one existing piece and ship the same idea as a native post on each requested channel - not the same text with hashtags swapped.
Workflow
- Extract the core: pull the single transferable idea plus its proof (the stat, story, or takeaway). This stays constant across every version; everything else is rebuilt around it.
- Confirm the target channels. Reformat only the channels the user named; do not invent a full distribution stack. If they named none, ask which channels.
- Rebuild - do not truncate - for each target, in its native grammar:
- LinkedIn: text-first, ~1300-2000 chars, short lines with white space, professional-but-human, one clear insight.
- X: a tight single post under 280, or a thread where each post stands alone; punchy, no corporate tone.
- Instagram: caption ~125-150 chars or a short narrative; the image/Reel carries the weight.
- TikTok: a spoken script with a verbal hook in the first two seconds; conversational, written to be said aloud.
- YouTube Shorts / Reels: the same script logic with on-screen text beats.
- Newsletter: longest form; keep the nuance the social cuts drop.
- Apply native conventions per channel: hashtags 3-5 on Instagram, 1-2 on LinkedIn/X, a couple of discovery tags at most on TikTok; use features as intended (carousels/Reels on IG, document posts and polls on LinkedIn, threads and quote-posts on X); tune emoji density to the channel.
- Hold voice, shift register: keep signature phrasing so the brand stays recognizable; go more formal on LinkedIn, looser on TikTok and X; drop jargon that does not travel to a casual feed.
- If output feeds a calendar, note sequencing: long-form first (the anchor), then the atomized versions pointing back to it, staggered over days rather than dumped at once.
- Label each version with its target channel and format on delivery.
Worked example
Source: a newsletter section arguing that most A/B tests are called too early, with the proof point "tests stopped at 50 conversions flip their winner a third of the time."
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.
- 10d ago First seen · 74 lines · 137 tokens per session scan A ef0b28d44766
Cross-Platform Reformatter is a skill published in the GitHub repository SkillMedev/social-media-studio (1 stars, last pushed 2mo ago), licensed MIT. It adds 137 tokens to every session and 1,255 once invoked, about $0.0007 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to Cross-Platform Reformatter, differing in 0 lines, and is treated as a copy.
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