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 rampstackco/claude-skills --skill content-repurposinggit clone --depth 1 https://github.com/rampstackco/claude-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/rampstackco/claude-skills/content-repurposing)<a href="https://agentmods.dev/skills/rampstackco/claude-skills/content-repurposing"><img src="https://agentmods.dev/badge/skills/rampstackco/claude-skills/content-repurposing/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/rampstackco/claude-skills/content-repurposing"><img src="https://agentmods.dev/badge/skills/rampstackco/claude-skills/content-repurposing.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 | $0.00165 | $0.04928 |
| Opus 5.5 | $0.00066 | $0.01971 |
| Sonnet 5.5 | $0.00033 | $0.00986 |
| Haiku 4.5 | $0.00016 | $0.00493 |
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 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 — 305 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Content Repurposing
A senior editorial leader's playbook for cross-format content adaptation. The discipline of turning one substantial piece into many derivative formats without losing the original's value or producing slop variants.
Most content programs underspend on repurposing. A flagship piece costs 40-80 hours to produce; the program publishes it once, shares it on three channels, and moves on. The same piece could have produced a blog series, an email sequence, a webinar, a podcast episode, a dozen social posts, video shorts, and FAQ extractions for AI search visibility. The work to extend the source piece across formats is small relative to the value extracted; programs that skip repurposing leave most of the value unrealized.
The failure mode in the other direction is mass-blast: the same content reposted across channels without adaptation. A blog post pasted into LinkedIn as a long-text post; the email newsletter is the blog's first three paragraphs with "read more" tacked on; the YouTube video is a slideshow of the article text read aloud. Mass-blast respects neither the medium nor the audience. AI-assisted repurposing has made mass-blast cheap; the result is a wave of derivative content that performs poorly across every channel because it was adapted to none of them.
This skill is the discipline of adaptation per medium. Each format has constraints, conventions, and reader expectations the source piece does not have. Repurposing that respects those constraints produces work that earns engagement on the new format; repurposing that ignores them produces filler.
When to use this skill: planning the extension of a flagship piece across formats, auditing a repurposing pipeline that is producing low-engagement derivatives, calibrating an AI-assisted repurposing workflow that is producing slop, or designing the cross-format adaptation conventions for a content program.
What this skill is for
This skill spans cross-format adaptation work after a source piece has been produced. The content suite distinction:
What ships with it
10 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- README.md 360 B
- references/aeo-extraction-patterns.md 10 KB
- references/common-repurposing-failures.md 10 KB
- references/cross-promotion-patterns.md 9.5 KB
- references/format-adaptation-patterns.md 13 KB
- references/per-format-constraints.md 11 KB
- references/repurposing-pipeline-templates.md 11 KB
- references/sequencing-and-cadence-patterns.md 11 KB
- references/source-piece-selection-criteria.md 11 KB
- references/voice-consistency-across-formats.md 11 KB
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 · 305 lines · 165 tokens per session scan A 627533fe62fa
content-repurposing is a skill published in the GitHub repository rampstackco/claude-skills (993 stars, last pushed 5d ago), licensed MIT. It adds 165 tokens to every session and 4,928 once invoked, about $0.0007 per session on Opus 5.5. 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 2026-08-30.
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