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 vstorm-co/content-skills --skill content-repurposegit clone --depth 1 https://github.com/vstorm-co/content-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/vstorm-co/content-skills/content-repurpose)<a href="https://agentmods.dev/skills/vstorm-co/content-skills/content-repurpose"><img src="https://agentmods.dev/badge/skills/vstorm-co/content-skills/content-repurpose/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/vstorm-co/content-skills/content-repurpose"><img src="https://agentmods.dev/badge/skills/vstorm-co/content-skills/content-repurpose.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 | $0.00052 | $0.01674 |
| Opus 5 | $0.00026 | $0.00837 |
| Sonnet 5 | $0.00010 | $0.00335 |
| Haiku 4.5 | $0.00005 | $0.00167 |
Grade A, and why
content-repurpose 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 9d 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 — 186 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Content Repurposing
Core Principle: 1-to-Many
One strong piece of content should become 5+ pieces across different platforms and formats. This is not about copying and pasting -- each transformation adapts the content for the target platform's norms, audience expectations, and native format.
A single blog post can become:
- An X thread extracting the key insights
- A LinkedIn post with a professional angle
- A newsletter section with personal commentary
- A short-form video script
- A slide deck for presentations
- A Reddit post framed for community value
- An infographic summarizing the data
The source material provides the substance. Each transformation reshapes it.
Before You Start
-
Check for brand voice: Read
brand/VOICE.mdif it exists. All transformations must maintain the same brand voice, even though tone and format vary by platform. If no brand voice is defined, maintain consistency across all outputs by inferring voice from the source material. -
Identify the source material: What is the input? It can be any content piece:
- Blog post or article
- X thread
- Video script or transcript
- Podcast transcript
- Slide deck or presentation
- Newsletter issue
- LinkedIn post
- Any other written or spoken content
-
Determine target formats: Ask the user which formats they want, or recommend based on the format matrix in
format-matrix.md. Not every source transforms well into every target -- the matrix shows which transformations are natural and which require significant rework. -
Understand platform norms: Each platform has its own culture, format constraints, and audience expectations. Transformations that ignore these norms feel off.
Repurposing Flow
Stage 1: Source Analysis
Read the source material and extract:
- Core message: The single most important idea (one sentence)
- Key insights: 3-7 individual points or arguments
- Best quotes: Punchy, shareable lines from the source
- Data points: Numbers, statistics, or concrete examples
- Narrative arc: The story structure (if any)
- Audience: Who was the original piece written for?
- Strongest section: Which part of the source has the most standalone value?
- Weakest section: Which part depends heavily on context from the rest of the piece?
What ships with it
6 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.
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.
- 9d ago First seen · 186 lines · 52 tokens per session scan A 2c7ca393e16c
content-repurpose is a skill published in the GitHub repository vstorm-co/content-skills (22 stars, last pushed 4mo ago), licensed MIT. It adds 52 tokens to every session and 1,674 once invoked, about $0.0003 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 2026-08-30.
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