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 vyralcontent/content-skills --skill viral-captions-and-ctasgit clone --depth 1 https://github.com/vyralcontent/content-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/vyralcontent/content-skills/viral-captions-and-ctas)<a href="https://agentmods.dev/skills/vyralcontent/content-skills/viral-captions-and-ctas"><img src="https://agentmods.dev/badge/skills/vyralcontent/content-skills/viral-captions-and-ctas/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/vyralcontent/content-skills/viral-captions-and-ctas"><img src="https://agentmods.dev/badge/skills/vyralcontent/content-skills/viral-captions-and-ctas.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk 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 | $0.00187 | $0.02688 |
| Opus 5 | $0.00093 | $0.01344 |
| Sonnet 5 | $0.00037 | $0.00538 |
| Haiku 4.5 | $0.00019 | $0.00269 |
Grade A, and why
viral-captions-and-ctas 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 12d 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 — 182 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Viral captions and CTAs
Help the user write the text around the video: captions, on-screen text, hashtags, CTAs, and the pinned comment. This is the copy layer that decides whether a good video gets distributed, saved, and sent. It does not predict virality. It rules out patterns that suppress reach and pushes toward ones that tend to lift it.
For hooks and opening lines, see viral-hooks. For platform algorithm depth,
see viral-tiktok-content, viral-youtube-shorts, viral-instagram-reels.
For ideation, see viral-short-form-ideas. For full scripting, see the
viral-short-form umbrella.
Operating principles (read these first, apply throughout)
- The caption is the SEO now. Instagram indexes captions for in-app search and for Google. TikTok reads captions as a main categorisation input. YouTube Shorts treats the title and first 125 chars of description as direct ranking signals. Hashtags aren't the discovery lever anymore. Words are.
- Most viewers watch on mute. Roughly 85% of social video is consumed silent. If the on-screen text doesn't carry the story, the video doesn't exist for most of the feed. Burn captions in. Don't trust auto-captions.
- Sends and saves beat likes and follows. The CTAs worth writing are tied to the algorithmically heavy actions (send, save, watch-to-end), not the lightweight ones ("like", "follow", "comment below") that pattern-match as engagement bait.
- Engagement bait is a distribution tax. "Comment YES for the link", "tag a friend", "like for part 2", "follow for more" all read as bait under Meta's and TikTok's policies. Each buys a small action and pays a reach penalty.
- One ask per video. Stacking like + save + share + follow + DM dilutes all five. Pick the action that matches the content type and earn it.
- Native, not cross-posted. A caption written for TikTok loses to one rewritten for Reels and another rewritten for Shorts. Different cutoffs, different keyword vocabulary, different hashtag conventions.
- Pattern-matching, not prediction. Captions and CTAs tend to perform a certain way. Nothing here guarantees a result. Honesty is the brand.
What ships with it
11 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.
- assets/bait-check.md 2.3 KB
- assets/caption-template.md 2.4 KB
- assets/cta-picker.md 2.6 KB
- assets/on-screen-text-spec.md 3.8 KB
- assets/pinned-comment-template.md 2.6 KB
- references/anti-patterns.md 5.8 KB
- references/caption-craft.md 5.1 KB
- references/ctas-that-work.md 5.9 KB
- references/hashtag-reality.md 3.6 KB
- references/on-screen-text.md 4.6 KB
- references/pinned-comments.md 5.8 KB
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.
- 12d ago First seen · 182 lines · 187 tokens per session scan A f8c1b642c35e
viral-captions-and-ctas is a skill published in the GitHub repository vyralcontent/content-skills (106 stars, last pushed 2mo ago), licensed MIT. It adds 187 tokens to every session and 2,688 once invoked, about $0.0009 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 2026-08-30.
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