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 sutchan/Agent-Skills-Hub --skill captions-and-clippinggit clone --depth 1 https://github.com/sutchan/Agent-Skills-HubWrote 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/sutchan/agent-skills-hub/captions-and-clipping)<a href="https://agentmods.dev/skills/sutchan/agent-skills-hub/captions-and-clipping"><img src="https://agentmods.dev/badge/skills/sutchan/agent-skills-hub/captions-and-clipping/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/sutchan/agent-skills-hub/captions-and-clipping"><img src="https://agentmods.dev/badge/skills/sutchan/agent-skills-hub/captions-and-clipping.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 | $0.00103 | $0.01350 |
| Opus 5 | $0.00051 | $0.00675 |
| Sonnet 5 | $0.00021 | $0.00270 |
| Haiku 4.5 | $0.00010 | $0.00135 |
Grade A, and why
captions-and-clipping 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.
This is a copy
92% identical to captions-and-clipping — 16 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 — 76 lines — stays where its author put it; the contents beside it link to each section on GitHub.
captions-and-clipping
The transform producer of the video cluster — it turns existing long-form into native short clips with sound-off captions, the engine behind the Shorts funnel and cross-platform reach. Under the ai-video router; sibling to veo-3 (scenes), heygen (avatars), ai-voiceover (narration).
The POV: a clip is a standalone Short, not a random 30 seconds
AI tools find candidate moments and auto-caption fast — but the virality score is a hint, not a verdict (clips rated 40 beat 85; ~70% need cleanup), so a human still picks the moment that stands alone with its own hook, reframes so the subject stays in frame, captions for mute viewing, and ships clean (no other-platform watermark — it trips the Originality Score on Reels/ Shorts). One long video → ~10–30 native clips, each a real Short.
Read these first
- brand-profile — pillars, look, non-negotiables (for selection + caption style).
- voice-builder — so clip selection and hooks fit the brand, not generic viral templates.
The framework: CLIP
(Depth: references/the-clip-framework.md.)
- C — Cut to the moment: AI moment-detection (Opus Clip ClipAnything) surfaces candidates; a human picks complete, hook-first, on-strategy clips.
- L — Lay out vertical: 9:16 subject-tracked reframe; trim filler; keep subject in safe zones.
- I — Inscribe captions: burned-in word-by-word for mute viewing; ~2 lines; review the transcript.
- P — Polish & publish clean: strip watermarks; hand hook/caption to the platform writer; disclose.
Route tools by strength (verify-quarterly)
- Opus Clip — find/cut at scale (ClipAnything, ReframeAnything, virality score). API gated to Business. Deep pipeline (credits, triage, tiers): the opus-clip skill.
- Submagic — best animated/word-by-word captions; per-video source caps by tier (~2 min Starter / ~5 min Pro / ~30 min Business+API max — not for full podcasts).
- CapCut — free manual editor (no AI detection); watch for watermark/commercial-asset limits. Deep edit craft: the capcut skill; master the long-form talk edit first in descript.
- Common pattern: Opus Clip to cut → Submagic to caption → clean export. Full landscape:
references/clipping-tools-2026.md; selection + recipes:references/clip-and-caption-recipes.md.
What ships with it
5 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.
- 12d ago First seen · 76 lines · 103 tokens per session scan A 9d3d8f6b1895
captions-and-clipping is a skill published in the GitHub repository sutchan/Agent-Skills-Hub (2 stars, last pushed yesterday), licensed MIT. It adds 103 tokens to every session and 1,350 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. It is 92% identical to captions-and-clipping, differing in 16 lines, and is treated as a copy.
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