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/maxtechera/ship/repurposingnpx skills add maxtechera/ship --skill repurposinggit clone --depth 1 https://github.com/maxtechera/shipWhat 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 | $0.00024 | $0.00694 |
| Opus 5 | $0.00012 | $0.00347 |
| Sonnet 5 | $0.00005 | $0.00139 |
| Haiku 4.5 | $0.00002 | $0.00069 |
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 yesterday.
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 — 90 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Content Repurposing
Turn existing content into multi-platform assets. Adapt viral patterns to your brand.
Two Modes
Mode 1: Asset Repurposing
Take an existing piece (video, post, article) and derive new formats from it.
Mode 2: Viral Pattern Analysis
Identify what makes viral content perform, extract the pattern, and adapt it to your angle.
Mode 1: Asset Repurposing
Standard Repurposing Matrix
From 1 long-form video (10-15 min):
| Asset | Count | Platform |
|---|---|---|
| Short clips (hook cuts) | 3-5 | YouTube Shorts, Instagram Reels, TikTok |
| Carousel (key points) | 1-2 | |
| Thread (structured takeaways) | 1 | Twitter/X |
| LinkedIn post (professional angle) | 1 | |
| Newsletter block (personal angle) | 1 | |
| Blog post (expanded SEO version) | 1 | Website |
Total: 10-15 assets from 1 source
Process
- Transcript — get/create transcript from source content
- Extract best moments — hook, key insight, surprising claim, actionable tip
- Map to formats — which moments work for each platform
- Write platform copy — adapt language per platform (not copy-paste)
- Identify b-roll needs — what visuals are needed per clip
Mode 2: Viral Pattern Analysis
When adapting a viral piece from another creator:
-
Identify the pattern (not the content):
- Hook type (question / bold claim / pattern interrupt / story opener)
- Format structure (problem → solution / before → after / tutorial steps)
- Engagement mechanic (controversy / curiosity gap / transformation)
- Platform-specific elements (text overlay position, pacing, sound)
-
Strip the brand:
- Remove the specific topic, person, and examples
- Keep the structural skeleton
-
Rebuild with your angle:
- Insert your topic, your evidence, your VoC pain language
- Your CTA and offer — not theirs
-
Document the pattern:
Source: [URL / creator / date] Pattern: [hook type + structure] Why it worked: [engagement mechanic] Adaptation: [your angle + topic]
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.
- yesterday First seen · 90 lines · 24 tokens per session scan A f1ec79eb0385
content-repurposing is a skill published in the GitHub repository maxtechera/ship (2 stars, last pushed 4mo ago), licensed MIT. It adds 24 tokens to every session and 694 once invoked, about $0.0001 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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