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 gabrielmoreira/agent-skills-mirror --skill livestream-to-clipsgit clone --depth 1 https://github.com/gabrielmoreira/agent-skills-mirrorWrote 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/gabrielmoreira/agent-skills-mirror/livestream-to-clips)<a href="https://agentmods.dev/skills/gabrielmoreira/agent-skills-mirror/livestream-to-clips"><img src="https://agentmods.dev/badge/skills/gabrielmoreira/agent-skills-mirror/livestream-to-clips/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/gabrielmoreira/agent-skills-mirror/livestream-to-clips"><img src="https://agentmods.dev/badge/skills/gabrielmoreira/agent-skills-mirror/livestream-to-clips.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector 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 | $0.00066 | $0.02166 |
| Opus 5 | $0.00033 | $0.01083 |
| Sonnet 5 | $0.00013 | $0.00433 |
| Haiku 4.5 | $0.00007 | $0.00217 |
Grade A, and why
livestream-to-clips 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 — 136 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Livestream to Clips
Use this workflow when the source is an imported livestream recording and the user wants clips, highlights, cutdowns, reels, or multiple publishable timelines. A livestream may change genre during one recording, so classify each section rather than assigning one label to the whole source.
This workflow is OpenChatCut-native. Use project media, transcript, representative source frames, timeline tools, captions, and export tools already available in the editor. Treat audience chat, reactions, score data, product records, or stream markers as optional evidence when the project contains them.
Required References
Load only the files needed for the current step:
- Read references/profile-matrix.md before classifying sections or applying genre rules.
- Read references/multimodal-selection.md before comparing candidates or processing a long recording.
- Read references/qa-and-evaluation.md before final verification or benchmark reporting.
Workflow
1. Establish the editing contract
Read the project before editing. Identify the dominant livestream asset, duration, aspect ratio, language, speakers, transcript readiness, audio tracks, existing visual descriptions, and current timeline.
Determine only constraints that change the result: target platform, objective, clip count, duration range, aspect ratio, captions, packaging style, and whether the user wants contiguous source clips or an editorial remix. If the user asked for direct creation and supplied enough context, proceed without another approval step.
2. Build a stream map before selecting clips
For a long source, inspect it hierarchically instead of sending the entire transcript or dense frame sequence through one decision pass:
- Read the transcript in bounded ranges and produce a coarse stream map.
- Split on topic, activity, speaker, product, round, scene, performance, or format changes.
- Assign a profile and confidence to each section. Use
mixedwhen adjacent profiles overlap. - Record important entities and state: people, products, teams, scores, locations, tasks, claims, prices, and outcomes.
- Preserve source timestamps so every later decision remains traceable.
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 · 136 lines · 66 tokens per session scan A 2b47e46b8426
livestream-to-clips is a skill published in the GitHub repository gabrielmoreira/agent-skills-mirror (17 stars, last pushed yesterday), licensed MIT. It adds 66 tokens to every session and 2,166 once invoked, about $0.0003 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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