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 brajendrak00068/agentic-ai-video-production --skill viral-clips-generatorgit clone --depth 1 https://github.com/brajendrak00068/agentic-ai-video-productionWrote 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/brajendrak00068/agentic-ai-video-production/viral-clips-generator)<a href="https://agentmods.dev/skills/brajendrak00068/agentic-ai-video-production/viral-clips-generator"><img src="https://agentmods.dev/badge/skills/brajendrak00068/agentic-ai-video-production/viral-clips-generator/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/brajendrak00068/agentic-ai-video-production/viral-clips-generator"><img src="https://agentmods.dev/badge/skills/brajendrak00068/agentic-ai-video-production/viral-clips-generator.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 | $0.00022 | $0.00733 |
| Opus 5.5 | $0.00009 | $0.00293 |
| Sonnet 5.5 | $0.00004 | $0.00147 |
| Haiku 4.5 | $0.00002 | $0.00073 |
Grade A, and why
viral-clips-generator 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 2d 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 — 67 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Highlight Clip Extractor
Identify high-engagement moments in long-form recordings and extract standalone short-form video clips.
Setup
This skill calls the Levea (livecore.ai) editing service, which requires a Levea account and API key.
- Sign up and generate an API key at https://livecore.ai/?openclaw=1&utm_source=clawhub&utm_medium=skill&utm_campaign=viral-clips-generator
- Provide the key to the tool you use:
- OpenClaw plugin
openclaw-ai-video-editor(ClawHub): setapiKeyin the plugin config. - MCP server
levea-mcp-server(npm): setLEVEA_API_KEYin the MCP server config.LEVEA_API_URLis optional and defaults tohttps://api.livecore.ai.
- OpenClaw plugin
- Describe the edit you want in plain language; the tool returns a project ID and, after export, an MP4 URL.
When to use
- Extracting multiple standalone highlight clips from a long lecture, interview, or stream.
- Selecting segments based on transcript hooks and narrative peaks.
- Batch-exporting short clips with vertical reframing and captions.
Tool interface
Use the registered ai-video-editor (OpenClaw plugin) or autonomous_edit (MCP server) tool to run edits against a project timeline.
Tool parameters
prompt(string, required): Description of the video edit to perform.project_id(string, optional): Project identifier returned by a prior call for multi-turn continuity.video_url(string, optional): Source video URL when starting a new project.captionTemplatePreset(string, optional): Built-in caption style slug (for examplehormozi,viral-pop,karaoke,minimal-pro).captionTemplateMode(string, optional): One ofadd,recreate,style,translate,clear,remove_fillers.requirePlanApproval(boolean, optional): Whentrue, pauses after planning and returnsworkingMemoryfor user confirmation.workingMemory(object, optional): State object from a paused plan-approval call.
Example tool input
{
"prompt": "Extract 3 standalone 30-second highlight clips from the most engaging moments in this recording.",
"project_id": "project_example_01"
}
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.
- 2d ago Changed · +5 lines 1b3e21cc074e
- 6d ago Changed · +5 lines f602ddd198d2
- 8d ago First seen · 57 lines · 22 tokens per session scan A d36c9cef1042
viral-clips-generator is a skill published in the GitHub repository brajendrak00068/agentic-ai-video-production (3 stars, last pushed yesterday), licensed MIT. It adds 22 tokens to every session and 733 once invoked, about $0.0001 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 2026-10-20.
Other skills, from other repositories
kinocut
Use when an agent must edit local video safely. Kinocut guards planning, FFmpeg operations, subtitles, audio, analysis, quality gates, receipts, repurposing, and Hyperframes via MCP, Python, or CLI.
kinocut-repurpose
Use when turning one local video into a platform-ready short. Kinocut builds clip packages with manifests, review artifacts, and human approval gates.
mcp-video
Use when a tool or doc references mcp-video, the former name. Kinocut is the same guarded local video toolkit: inspect, edit, render, verify.
ainb-fleet:daemons
Runtime-health view of the four fleet daemons — phone bridge, notifyd, ATC, and the fleet auto-continue daemon — in one table. Each row reports state (running / stopped / unknown), pid, uptime, last activity, error count, and a HEALTH reason that distinguishes a clean stop from a crash (stale heartbeat) or a…
ainb-fleet:daemon
Long-running watcher that scans every claude session every 5s and auto-sends continue to any session whose recent tmux pane buffer matches a known API-error regex (ratelimited, overloadederror, internalservererror, requesttimeout, sockethangup, fetchfailed, ECONNRESET). Use this when you want unattended recovery from…
ainb-fleet:cost
Show fleet spend — per-session, per-model, per-day, and per-group USD cost rollups for every claude/codex session, sourced live from ainb's burndown analytics (which already prices every provider call). Use when you need spend visibility across a multi-session fleet, want to find the most expensive session/model, or…