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 walterwritesai/walter-skills --skill repurposergit clone --depth 1 https://github.com/walterwritesai/walter-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/walterwritesai/walter-skills/repurposer)<a href="https://agentmods.dev/skills/walterwritesai/walter-skills/repurposer"><img src="https://agentmods.dev/badge/skills/walterwritesai/walter-skills/repurposer/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/walterwritesai/walter-skills/repurposer"><img src="https://agentmods.dev/badge/skills/walterwritesai/walter-skills/repurposer.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 | $0.00049 | $0.00334 |
| Opus 5 | $0.00024 | $0.00167 |
| Sonnet 5 | $0.00010 | $0.00067 |
| Haiku 4.5 | $0.00005 | $0.00033 |
Grade A, and why
walter-content-repurposer 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.
What it actually says
Walter Content Repurposer
You have access to Walter Writes AI tools. Your job is to turn one piece of content into multiple formats.
When content is provided
When the user shares a blog post, article, or long-form content:
- Ask what formats they need (or default to all of: LinkedIn post, X/Twitter thread, email newsletter, Instagram caption)
- Generate each format from the original content
- Humanize each format with Walter in light mode (repurposed content should feel casual and native to each platform)
- Run detection on each
Format guidelines
LinkedIn: 150-200 words. Lead with an insight or hot take. End with a question. Professional but not stiff.
X/Twitter thread: 5-8 tweets, each under 280 characters. First tweet hooks. Last tweet has a CTA. Each tweet should standalone but flow as a narrative. No hashtags.
Email newsletter: 300-400 words. Lead with the most interesting insight. Conversational tone. End with a link to the full piece.
Instagram caption: Under 150 words. More personal and visual-description friendly. Include 3-5 relevant hashtags at the end.
Show a summary
After generating all formats, show a summary table with format, word/character count, and detection score for each.
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 · 32 lines · 49 tokens per session scan A 31909d1a895f
walter-content-repurposer is a skill published in the GitHub repository walterwritesai/walter-skills (10 stars, last pushed 2mo ago), licensed MIT. It adds 49 tokens to every session and 334 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 2026-08-31.
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