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 ABO3SKRALMASAOODI/valmera-mcp --skill turn-long-video-into-shortsgit clone --depth 1 https://github.com/ABO3SKRALMASAOODI/valmera-mcpWrote 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/abo3skralmasaoodi/valmera-mcp/turn-long-video-into-shorts)<a href="https://agentmods.dev/skills/abo3skralmasaoodi/valmera-mcp/turn-long-video-into-shorts"><img src="https://agentmods.dev/badge/skills/abo3skralmasaoodi/valmera-mcp/turn-long-video-into-shorts/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/abo3skralmasaoodi/valmera-mcp/turn-long-video-into-shorts"><img src="https://agentmods.dev/badge/skills/abo3skralmasaoodi/valmera-mcp/turn-long-video-into-shorts.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 | $0.00063 | $0.00598 |
| Opus 5.5 | $0.00025 | $0.00239 |
| Sonnet 5.5 | $0.00013 | $0.00120 |
| Haiku 4.5 | $0.00006 | $0.00060 |
Grade A, and why
turn-long-video-into-shorts 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 14d 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 — 32 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Turn a long video into shorts with Valmera
Valmera can split one long recording into several short projects. Each one is a complete, self-contained story with its own reframing and captions, and each can be refined and exported separately.
Workflow
- Set up the source. For a podcast or long interview, create a shorts project with
create_projectandkind="shorts", then upload into it. For an existing project, open it withopen_project. Wait forindex_statusto report done. - Read the whole conversation first. Use
get_transcript, which has speaker labels, plusget_editorial_mapandfind_visual_momentsfor visual beats. Pick stories that:- start with a hook that makes sense without context in the first two seconds,
- finish their thought; never cut mid-argument,
- fit the length the user asked for (often 30–60 seconds).
- Propose before you cut when the user hasn't chosen moments. List each candidate with its source start and end time, a working title, and one line on why it works.
- Create the shorts. Call
make_shortswith your chosen story arcs. Track progress withshorts_status, and open each short withopen_short. - Polish each short. Reframe to 9:16 so the speaker stays in frame (
auto_reframe, withcompose_panelswhen two people matter). Add captions with an active-word highlight, a title card or hook text, and punch-ins on key lines.apply_short_edit_batchesapplies the same kinds of edits to up to 30 shorts in one call. - Review. Render a preview of each short (
render_preview,wait_for_job) and check its opening frame, captions and framing withlook_atandrendered=true. - Export when asked. For each approved short, call
export_final, thenwait_for_job, thendownload_urlwithkind="final".
Be honest about virality
No tool can guarantee a clip goes viral. Say that you picked complete, hook-first stories from the transcript and visuals, and suggest the user test several.
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.
- 14d ago First seen · 32 lines · 63 tokens per session scan A 6da89185a5a6
turn-long-video-into-shorts is a skill published in the GitHub repository ABO3SKRALMASAOODI/valmera-mcp (0 stars, last pushed 13d ago), licensed MIT. It adds 63 tokens to every session and 598 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 2026-10-06.
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