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 agentmods add skills/skillmedev/creator-studio/content-repurposingnpx skills add SkillMedev/creator-studio --skill content-repurposinggit clone --depth 1 https://github.com/SkillMedev/creator-studioWhat 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 | $0.00182 | $0.01890 |
| Opus 5 | $0.00091 | $0.00945 |
| Sonnet 5 | $0.00036 | $0.00378 |
| Haiku 4.5 | $0.00018 | $0.00189 |
Grade A, and why
Content Repurposing 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 2d 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 — 95 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Content Repurposing
One strong pillar piece contains a week of distribution, and most creators throw that away by publishing once and moving on. Repurposing is not copying and pasting: it is extracting the strongest signal from a long-form asset and recasting it in the native language of each platform. The costly mistake this skill prevents is the opposite failure - spraying weak, resized fragments across every channel, which burns audience trust faster than posting nothing.
Operating procedure
Run the steps in order. The source audit must come before any derivative is written, because a derivative built on a weak moment fails no matter how well it is formatted.
Step 1: gather inputs
Collect these before writing anything. If a value is inferred rather than stated, label it a guess.
- The source asset: a link, transcript, or draft. Required - never repurpose from a summary of the source.
- The platforms the creator actively maintains. Default set: their primary short-form video platform, X or LinkedIn (whichever matches the audience), and email if a list exists. Do not generate pieces for channels the creator has abandoned.
- The primary goal: reach, email capture, or sales. Default to reach.
- Any recurring formats the audience already recognizes (a named series, a carousel style), so derivatives slot into them.
Step 2: audit the source for the three nucleus moments
Identify the three strongest moments in the source: the sharpest insight, the most relatable story beat, and the most actionable tip. Timestamp or quote each one exactly. Apply the standalone test: if a moment would not work as its own asset with zero surrounding context, it is not strong enough to repurpose. These three moments become the nucleus of every derivative; nothing in the map should derive from a moment outside them.
Step 3: build the derivation map
The working ratio: one long-form pillar yields at least 10 atomic pieces; 12-15 is normal for a 20+ minute video or an hour-long podcast episode. The standard stack, mapped to nucleus moments:
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.
- 2d ago First seen · 95 lines · 182 tokens per session scan A a81b7b1bb581
Content Repurposing is a skill published in the GitHub repository SkillMedev/creator-studio (5 stars, last pushed 2mo ago), licensed MIT. It adds 182 tokens to every session and 1,890 once invoked, about $0.0009 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 2026-08-31.
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