Skipper: An AI Video Consultant With Real Diagnostics
A question we see often, in some form, is “my viewers keep buffering, what’s wrong?” No docs page answers that. Answering it means looking at the stream (rebuffer ratio, packet loss, which edges the viewers landed on, whether the encoder is keeping up) as well as the documentation.
Skipper does both. It is an AI video consultant built into FrameWorks: it searches curated streaming documentation, runs diagnostics against your live streams, generates API operations from the current schema, and, in authenticated dashboard chat, can manage streams and billing for you.
One question, answered with your stream’s data
Section titled “One question, answered with your stream’s data”Take the buffering question. Ask Skipper “why is stream X buffering” and it pulls the stream’s real numbers: rebuffering patterns, packet loss, buffer health, and where viewers were routed. The diagnosis that takes 30 minutes of dashboard-hopping by hand comes back as one answer with the evidence attached, so you can see whether the problem is the encoder’s uplink, one region’s viewers, or a player falling behind.
When the answer is a configuration change, Skipper looks it up before it tells you. Knowledge questions go to a curated corpus that covers FrameWorks, MistServer, FFmpeg, OBS, SRT, HLS, DASH, WebRTC, nginx-rtmp, and Livepeer, and source-backed answers include citations. Topics the corpus does not cover fall back to web search where one is configured.
Ask “give me a Python script that creates a stream” and it reads the live GraphQL schema at query time, generates the operation, and wraps it in code. The output matches your current API rather than an API shape from the model’s training data.
In authenticated dashboard chat it can also act: create streams, refresh keys, clip the last 30 seconds of a broadcast, start a DVR recording, upload VOD assets, check your balance, or start a supported crypto top-up. Each action is a call to the same GraphQL API you would use directly, and each one shows up in the chat as a card with what ran and what came back.
It tells you when it is guessing
Section titled “It tells you when it is guessing”A consultant that sounds equally sure about everything is hard to use, because you cannot tell which part to check. Skipper tags each section of an answer with one of four levels:
- Verified: grounded in the curated knowledge base, with source links.
- Sourced: backed by live web search results.
- Best guess: from the model’s general knowledge, not checked against a source.
- Unknown: nothing supporting it was found.
Mixed answers are the common case. In that Python snippet, the GraphQL operation comes from the real schema, while the Python around it is model-written glue code and is tagged as a best guess. The tag tells you where to spend your review time. Skipper is also instructed never to guess CLI flags, codec parameters, or configuration values without a source, which is where a confident wrong answer does the most damage.
Your agents get the same access, and no more
Section titled “Your agents get the same access, and no more”Skipper connects through the same MCP gateway that external agents use: the same GraphQL API, the same tool catalog, the same authorization checks. It has no private path into the platform. Anything it can do in your account, your own agents can do too, and anything your account cannot do, Skipper cannot do either.
That boundary shapes where it runs and what it may change:
- Dashboard: authenticated chat with account-scoped diagnostics and management actions where enabled.
- Docs site: a chat panel on every page (Cmd/Ctrl+J) that can explain and inspect but never modifies anything.
- Your own agents: any MCP client can call
ask_consultantfor the full answer with confidence tags. Changes are blocked on that path; an agent that intends to create or delete something calls the dedicated platform tools, which carry their own authorization.
Keeping changes out of the consultant path means an agent asking Skipper a question cannot end up altering your account as a side effect of the answer.
Watching streams when nobody is asking
Section titled “Watching streams when nobody is asking”Where it is enabled, Skipper also checks the health of your active streams and infrastructure on a schedule. It learns what normal looks like for your account and runs a full investigation only when something departs from it; the report comes back with a root cause and recommendations over email or the channels you have configured. Accounts whose streams are healthy use no model calls at all during these checks, so monitoring does not turn into a background token bill.
What it does not do
Section titled “What it does not do”Skipper is a consultant, not an operator with its own authority. It can only act where your account can, on the dashboard, and with the tier and rate limits of your plan. Standard usage billing applies to stream operations it performs on your behalf. Its answers are only as current as the knowledge base and the schema it reads, which is why the confidence tags exist: verify a best-guess section before acting on it.
The Skipper guide covers where to find it, what it can do, and how agents call it. Operators running their own deployment can pick models, add knowledge sources, and tune retrieval and monitoring in the operator guide.