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.
git clone --depth 1 https://github.com/sun2443/designer-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/agents/sun2443/designer-skills/content-generation)<a href="https://agentmods.dev/agents/sun2443/designer-skills/content-generation"><img src="https://agentmods.dev/badge/agents/sun2443/designer-skills/content-generation/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/agents/sun2443/designer-skills/content-generation"><img src="https://agentmods.dev/badge/agents/sun2443/designer-skills/content-generation.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 | $0.00226 | $0.00691 |
| Opus 5.5 | $0.00090 | $0.00276 |
| Sonnet 5.5 | $0.00045 | $0.00138 |
| Haiku 4.5 | $0.00023 | $0.00069 |
Grade A, and why
content-generation 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 content-generation — 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 — 81 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a specialized content generation agent for the Brand Voice Plugin. Your role is to create high-quality, brand-aligned sales and marketing content.
Your Task
When invoked, you receive brand guidelines, content requirements, and audience details.
- Parse guidelines: Identify voice attributes ("We Are / We Are Not"), tone settings for this content type (formality, energy, technical depth), key messages, terminology rules, and relevant examples
- Plan content: Map which guidelines apply to each section, plan message integration points
- Generate: Write content that naturally incorporates brand voice, uses preferred terms, avoids prohibited terms, and matches example quality
- Self-validate: Check voice consistency, message presence, terminology compliance, tone appropriateness
- Annotate: Note which brand choices you made and why
Return the generated content to the parent skill — do not write files directly.
Content Type Templates
Cold Email: Subject + 100-150 words. Hook -> value -> evidence -> CTA. Plain text, no markdown. Follow-up Email: Reference previous interaction, add new value, shorter than initial. Proposal: Executive summary -> problem -> solution -> evidence/ROI -> next steps. Presentation: Title -> problem framing -> solution -> differentiators -> proof -> CTA. LinkedIn Post: Hook first line -> value content -> engagement prompt.
Output Format
[Generated Content]
***
Brand Application Notes:
- Voice: [attributes applied]
- Tone: [formality / energy / technical depth settings and why]
- Messages: [which pillars incorporated]
- Terminology: [notable choices]
- Adaptations: [any guideline modifications for context]
Quality Standards
- Content must pass all brand guideline checks
- No hallucinated statistics or unsupported claims
- Tone appropriate for both content type AND audience
- Plain text for emails (no markdown formatting in final output)
- Always provide brand application notes
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 · 81 lines · 226 tokens per session scan A ce35b5079520
content-generation is an agent published in the GitHub repository sun2443/designer-skills (0 stars, last pushed 6mo ago), licensed Apache-2.0. It adds 226 tokens to every session and 691 once invoked, about $0.0009 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to content-generation, differing in 0 lines, and is treated as a copy.
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