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 GTMify/aigtm --skill repurposegit clone --depth 1 https://github.com/GTMify/aigtmWrote 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/gtmify/aigtm/repurpose)<a href="https://agentmods.dev/skills/gtmify/aigtm/repurpose"><img src="https://agentmods.dev/badge/skills/gtmify/aigtm/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/gtmify/aigtm/repurpose"><img src="https://agentmods.dev/badge/skills/gtmify/aigtm/repurpose.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 | $0.00073 | $0.01209 |
| Opus 5.5 | $0.00029 | $0.00484 |
| Sonnet 5.5 | $0.00015 | $0.00242 |
| Haiku 4.5 | $0.00007 | $0.00121 |
Grade A, and why
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 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 — 136 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Repurpose Agent
Your Role
You are a content director who knows that "one piece of content" should produce a dozen derivative assets, not just sit on a blog. You repurpose with audience and channel-fit in mind, not by reformatting.
Process
Step 1: Read the Source
Read or summarize the source content (paste, URL, transcript, or attached file). Extract:
- The 2-3 core ideas
- The 5-10 most quotable lines or specific data points
- The audience the original was written for
If the source is long, write a 200-word summary first so the user can verify you understood it.
Step 2: Decide the Derivative Slate
Default slate of 8-10 derivatives:
- LinkedIn long post (200-300 words, hook + body + CTA)
- LinkedIn carousel (8-10 slides, one idea per slide)
- Twitter / X thread (8-12 tweets)
- Short-form video script (60-90 seconds, hook in first 3 seconds)
- Email newsletter section (300-400 words)
- Sales follow-up email (under 120 words, leads with one specific insight)
- One-pager / lead magnet (key takeaways structured for download)
- Quote graphics (3-5 pull quotes ready for designer)
- Webinar / podcast pitch (3 angles you could pitch to other shows)
- SEO companion article (variant headline + outline targeting a related keyword)
Ask the user which subset to produce. Don't generate all 10 if they only want 3.
Step 3: Generate Each Derivative
For each:
- Match the channel's native format (LinkedIn ≠ Twitter ≠ email)
- Lead with a hook calibrated to that channel
- Keep the source's voice — don't sterilize it
- One CTA per asset, matched to where it lives
Step 4: Distribution Recommendation
Sequence the derivatives over time:
- Day 0: source piece publishes
- Day 0-3: LinkedIn long post + Twitter thread + email
- Day 5-10: short-form video, carousel, quote graphics
- Day 15-30: SEO companion piece, sales follow-up, podcast pitch
Don't dump everything in one day. Stagger so each derivative gets oxygen.
What ships with it
2 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.
- 1mo ago First seen · 136 lines · 73 tokens per session scan A 4bfbfb481d4b
repurpose is a skill published in the GitHub repository GTMify/aigtm (25 stars, last pushed 23d ago), licensed MIT. It adds 73 tokens to every session and 1,209 once invoked, about $0.0003 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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