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 calesthio/generative-media-skills --skill livestream-event-productiongit clone --depth 1 https://github.com/calesthio/generative-media-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/calesthio/generative-media-skills/livestream-event-production)<a href="https://agentmods.dev/skills/calesthio/generative-media-skills/livestream-event-production"><img src="https://agentmods.dev/badge/skills/calesthio/generative-media-skills/livestream-event-production/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/calesthio/generative-media-skills/livestream-event-production"><img src="https://agentmods.dev/badge/skills/calesthio/generative-media-skills/livestream-event-production.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 | $0.00068 | $0.06652 |
| Opus 5 | $0.00034 | $0.03326 |
| Sonnet 5 | $0.00014 | $0.01330 |
| Haiku 4.5 | $0.00007 | $0.00665 |
Grade A, and why
livestream-event-production 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 — 444 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Livestream Event Production
Use this skill when the user needs an AI agent to plan, direct, rehearse, run, troubleshoot, or hand off a live or hybrid livestream event. The event may be a webinar, conference session, town hall, launch, panel, class, fundraiser, worship service, performance, creator show, press briefing, or multi-room hybrid event.
This is a provider-independent production skill. It should produce a practical production plan, not a single-platform integration recipe.
Do not use this skill for:
- recutting, packaging, or editing an already recorded livestream after the fact;
- single-provider API integration, account setup, or dashboard-specific click paths;
- purely cinematic prerecorded video production with no live switching, live guests, ingest, monitoring, or incident plan;
- generic social short editing, podcast editing, or talking-head recut tasks unless the output is a live event plan.
Evidence Labels
When making claims, keep these categories distinct:
- Documented fact: a standard, first-party documentation page, regulator page, or formal technical report supports the claim.
- Volatile platform fact: a platform-specific setting, codec table, bitrate recommendation, ingest limit, feature availability, or dashboard behavior. State the verification date.
- Production heuristic: field-tested operating guidance that is not a standard. Present it as a decision aid, not as a universal requirement.
Operating Model
A livestream is a live show plus a live network service. Treat it as both.
The agent should produce or request these artifacts:
- event brief: audience, objective, platform destinations, privacy level, rights, language, accessibility, duration, success metrics;
- run of show: exact segments, timings, cues, sources, speakers, slides, graphics, lower thirds, videos, polls, Q&A, sponsor reads, breaks, backup content;
- crew plan: producer, technical director/switcher, audio engineer, graphics operator, stream engineer, stage manager, moderator, caption lead, guest wrangler, recording/media manager, incident lead;
- signal plan: cameras, screen shares, remote guests, playback, audio buses, graphics, program, clean feed, confidence monitors, comms, records, platform ingest;
- network and transport plan: contribution, ingest, delivery, latency profile, primary/backup paths, bandwidth headroom, monitoring points;
- rehearsal and go/no-go plan;
- incident plan and handoff package.
What ships with it
1 file 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 · 444 lines · 68 tokens per session scan A ba56a6333fbf
livestream-event-production is a skill published in the GitHub repository calesthio/generative-media-skills (170 stars, last pushed 2mo ago), licensed MIT. It adds 68 tokens to every session and 6,652 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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