oxbshw/watch-skill

video-memory

The user asks about videos watched in the past or across sessions — "have we watched anything about X", "which video showed that error", "what did that meeting decide", "search my videos", or a question that spans several videos.

Vedi sorgente

Come usare

  1. Copia il comando e controlla il pacchetto prima di installarlo nel tuo ambiente agente.
  2. Controlla strumenti, credenziali e ambienti richiesti nel documento originale.
  3. Descrivi input e risultato desiderato; confronta l’output con le fonti.
npx skills add https://github.com/oxbshw/watch-skill --skill video-memory

Prima di iniziare

  • L’installazione non include abbonamenti, quote API o accesso ai file.
  • Stelle e installazioni indicano attività, non un test indipendente di qualità o sicurezza.
Documento Skill originale

Contenuto dal repository con titoli, esempi, codice, tabelle, link e immagini preservati.

Video memory

Every video ever watched on this machine is still here — frames, OCR, transcripts, and past answers, in one persistent index under ~/.watch-skill/. You never have to say "I don't remember that video." Sessions don't expire it; watching more videos makes it more useful.

What's in the library?

bash
watch-skill list

Every indexed video: id, title, duration, when it was analyzed. Check here before watching anything — it may already be in memory from a previous session.

Find something across all of it

bash
watch-skill search "<phrase or keywords>"

Hybrid keyword + semantic search across every video, any language (Arabic folding, CJK and Thai segmentation are handled). Hits come back with video + timestamp; follow up on a hit with:

bash
watch-skill ask <video_id> "<the user's actual question>"
watch-skill ask <video_id> "what is on screen at <timestamp>?"

Questions that span videos

"What did we decide about the pricing page across those meetings?" — questions no single video answers go to the library synthesizer:

bash
watch-skill library ask "<the question>"

It answers from distilled notes across every indexed video, drills into real evidence, and cites video + timestamp for each finding. When it says the library does not clearly answer, trust that — do not stitch a guess together from weak search hits. watch-skill library overview shows what the library knows (videos, note counts, entities recurring across videos).

For "which video showed X" (locate, not synthesize), plain search is the right tool; then ask the hit video.

Batches build memory fast

A playlist or a folder of recordings goes in as one call — watch-skill batch "<playlist-or-folder>" — and the whole set becomes searchable memory (see watching-videos).

Correlati per attività

dallo stesso repository

Altri Skills

Tutti gli Skills
oxbshw
Community

asking-with-evidence

The user asks a question about a video that was already watched or indexed — "what did they say about X", "what error code appears", "what happens at 2:30", "does the video show Y". Use this to answer from the persistent index with timestamped evidence and a confidence score instead of re-watching or guessing.

installazioni
2
GitHub Stars
503
Aggiornato
14 ott
oxbshw
Community

configuring-vision

The user wants to connect an LLM or vision provider, already has an API key, asks "can I use OpenAI/Anthropic/Gemini/OpenRouter", wants local Ollama, or needs different cheap and strong models. Use this to configure provider-neutral visual understanding without tying Watch Skill to one agent or model vendor.

installazioni
2
GitHub Stars
503
Aggiornato
14 ott
oxbshw
Community

extracting-structure

The user wants structure pulled out of a watched video — "make chapters for this video", "where does the bug appear in this recording", "turn this screen recording into a bug report", "how strong is my intro/hook". Use this for deterministic extraction from the index — chapters with timestamps, a fileable bug report with the exact frame, or a scored hook analysis.

installazioni
2
GitHub Stars
503
Aggiornato
14 ott
oxbshw
Community

learning-from-mistakes

The user corrected an answer about a video — "no, it actually says X", "that's the wrong timestamp", "you misread the error code" — or asks why a video answer was wrong. Use this to record the correction as a lesson so future answers on similar questions improve, and to show what the system has learned and saved.

installazioni
2
GitHub Stars
503
Aggiornato
14 ott