Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/sun2443/designer-skillsnpx agentmods add skills/sun2443/designer-skills/draft-contentWrote 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/sun2443/designer-skills/draft-content)<a href="https://agentmods.dev/skills/sun2443/designer-skills/draft-content"><img src="https://agentmods.dev/badge/skills/sun2443/designer-skills/draft-content/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/sun2443/designer-skills/draft-content"><img src="https://agentmods.dev/badge/skills/sun2443/designer-skills/draft-content.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 | $0.00062 | $0.00985 |
| Opus 5.5 | $0.00025 | $0.00394 |
| Sonnet 5.5 | $0.00012 | $0.00197 |
| Haiku 4.5 | $0.00006 | $0.00098 |
Grade A, and why
draft-content 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 13d 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.
This is a copy
100% identical to draft-content — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 118 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Draft Content
If you see unfamiliar placeholders or need to check which tools are connected, see CONNECTORS.md.
Generate marketing content drafts tailored to a specific content type, audience, and brand voice.
Trigger
User runs /draft-content or asks to draft, write, or create marketing content.
Inputs
Gather the following from the user. If not provided, ask before proceeding:
-
Content type — one of:
- Blog post
- Social media post (specify platform: LinkedIn, Twitter/X, Instagram, Facebook)
- Email newsletter
- Landing page copy
- Press release
- Case study
-
Topic — the subject or theme of the content
-
Target audience — who this content is for (role, industry, seniority, pain points)
-
Key messages — 2-4 main points or takeaways to communicate
-
Tone — e.g., authoritative, conversational, inspirational, technical, witty (optional if brand voice is configured)
-
Length — target word count or format constraint (e.g., "1000 words", "280 characters", "3 paragraphs")
Brand Voice
- If the user has a brand voice configured in their local settings file, apply it automatically. Inform the user that brand voice settings are being applied.
- If no brand voice is configured, ask: "Do you have brand voice guidelines you'd like me to follow? If not, I'll use a neutral professional tone."
- Apply the specified or default tone consistently throughout the draft.
Content Generation by Type
Blog Post
- Engaging headline (provide 2-3 options)
- Introduction with a hook (question, statistic, bold statement, or story)
- 3-5 organized sections with descriptive subheadings
- Supporting points, examples, or data references in each section
- Conclusion with a clear call to action
- SEO considerations: suggest a primary keyword, include it in the headline and first paragraph, use related keywords in subheadings
Social Media Post
- Platform-appropriate format and length
- Hook in the first line
- Hashtag suggestions (3-5 relevant hashtags)
- Call to action or engagement prompt
- Emoji usage appropriate to brand and platform
- If LinkedIn: professional framing, paragraph breaks for readability
- If Twitter/X: concise, punchy, within character limit
- If Instagram: visual-first language, story-driven, hashtag block
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.
- 13d ago First seen · 118 lines · 62 tokens per session scan A 0bfe029dcc03
draft-content is a skill published in the GitHub repository sun2443/designer-skills (0 stars, last pushed 6mo ago), licensed Apache-2.0. It adds 62 tokens to every session and 985 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to draft-content, differing in 0 lines, and is treated as a copy.
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