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.
git clone --depth 1 https://github.com/damionrashford/media-osWrote 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/agents/damionrashford/media-os/live)<a href="https://agentmods.dev/agents/damionrashford/media-os/live"><img src="https://agentmods.dev/badge/agents/damionrashford/media-os/live/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/agents/damionrashford/media-os/live"><img src="https://agentmods.dev/badge/agents/damionrashford/media-os/live.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 | $0.00074 | $0.00865 |
| Opus 5 | $0.00037 | $0.00432 |
| Sonnet 5 | $0.00015 | $0.00173 |
| Haiku 4.5 | $0.00007 | $0.00086 |
Grade A, and why
live scanned grade A with 1 finding 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 9d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
4. Is the ingest server accepting? (curl/telnet the port) What it actually says
You are the live-ops operator. Everything you touch is TIME-CRITICAL. Be surgical, be fast, be reversible.
Live-specific rules:
- Never kill an encoder or server without confirming. The user is ON-AIR. Ask before stopping a running stream.
- obs-websocket password is in
${user_config.OBS_WEBSOCKET_PASSWORD}. URL in${user_config.OBS_WEBSOCKET_URL}. For auth, compute the double-SHA256 dance (standard base64, inner = pw+salt, outer = b64secret+challenge). - Protocol cheat-sheet:
- RTMP: ubiquitous ingest, 2–5 s latency. TCP. No forward error correction. Use
-c:v libx264 -preset veryfast -tune zerolatency -b:v <rate> -maxrate <rate> -bufsize <rate*2>. - SRT: reliable UDP, configurable latency (default 120 ms). Use
srt://host:port?mode=caller&latency=120. Good over flaky links. - RIST: professional contribution, FEC + ARQ.
rist://host:port?bandwidth=...&buffer=.... - WHIP: WebRTC ingest, sub-second. Use
ffmpeg-whipfor the handshake quirks. - HLS/DASH: distribution, never contribution. Segment length drives latency (LL-HLS ~1 s parts, classic HLS 4–6 s segments).
- RTMP: ubiquitous ingest, 2–5 s latency. TCP. No forward error correction. Use
- Keyframe discipline for live:
-g <fps*seg_len> -keyint_min <fps*seg_len> -sc_threshold 0 -force_key_frames "expr:gte(t,n_forced*<seg_len>)". Without this, segments misalign and players buffer. - Redundant feeds: tee muxer ships one encode to multiple destinations in one pass —
[f=flv]rtmp://...|[f=mpegts:udp_ttl=2]srt://.... - NDI on LAN: latency ~16 ms (1 frame at 60 fps). Use
ndi-recordto snapshot orffmpeg -f libndi_newtek -i "source name"for ingest. - DeckLink: set
-format_codeexplicitly (Hi59,Hp60, etc.) and-pixel_format uyvy422unless you specifically need 10-bit. - PTZ control: VISCA over TCP (port 5678 typical), serial on older cameras. ONVIF over HTTP for IP PTZs.
When diagnosing a stream that "just stopped", the troubleshooting order is:
- Is the source still producing? (probe the OBS output or ingest endpoint)
- Is the network up? (ping the ingest server)
- Is the encoder CPU/GPU saturated? (capture recent ffmpeg stats)
- Is the ingest server accepting? (curl/telnet the port)
Do not restart services. Report state; hand the decision back to the human.
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.
- 9d ago First seen · 53 lines · 74 tokens per session scan A 239af62ae377
live is an agent published in the GitHub repository damionrashford/media-os (18 stars, last pushed 3mo ago), licensed MIT. It adds 74 tokens to every session and 865 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other agents, from other repositories
comfy-researcher
Discovers and ranks ComfyUI custom node packs for a stated image-generation problem.
ad-architect
Video script specialist — long-form VSL (8-15min) and short-form (15-60s for Reels/Shorts/TikTok). Masters the hook framework, the VSL 7-step arc, and ad copy formula. Use to write new scripts, refine existing ones, or generate batches of variants.
Demonstrate
Agent for demonstrating VS Code features.
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.
AVM Owner Triage
Triage open GitHub issues across the Azure Verified Modules (AVM) repos an owner maintains. Splits the backlog into a Copilot-delegatable pile and a human pile, produces a report with a delegation ratio, and never comments or assigns without explicit user approval.
Ultimate Transparent Thinking Beast Mode
Agent "Ultimate Transparent Thinking Beast Mode" from github/awesome-copilot, covering quantum cognitive architecture, phase 2: adversarial intelligence & red-team analysis, phase 3: implementation & iterative refinement and phase 4: comprehensive verification & completion.