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…
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
$ npx -y skills add oxbshw/watch-skill --skill configuring-vision --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/configuring-visionContext preview
The summary Claude sees to decide when to auto-load this skill.
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
name: configuring-vision version: "1.4.3" description: 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. license: MIT user-invocable: true allowed-tools: Bash, Read
Watch Skill's agent surface and model backend are separate choices. Claude Code, Codex, Cursor, OpenClaw, framework agents, and REST clients all call the same engine; the engine can send selected frames to any supported vision provider.
watch-skill setup-vision --provider anthropic --api-key <KEY> watch-skill setup-vision --provider openai --api-key <KEY> watch-skill setup-vision --provider gemini --api-key <KEY> watch-skill setup-vision --provider openrouter --api-key <KEY> watch-skill setup-vision --provider ollama
Prefer a key the user already has. Do not claim Ollama is required, and do not ask the user to reveal a secret in chat. They can set the matching environment variable or run the command privately in their terminal.
One model can serve both tiers:
watch-skill setup-vision --provider openai --api-key <KEY> --model <vision-model>
Or use a cheaper model for scene descriptions and a stronger model for uncertain answers and loop critiques:
watch-skill setup-vision --provider openrouter --api-key <KEY> \ --cheap-model <fast-vision-model> --strong-model <strong-vision-model>
Add `--verify` to make one live probe call. If it fails, report the structured error and its `fix`; never echo the key.
Without a vision API, Watch Skill still acquires video, reads captions, runs local transcription and OCR, indexes evidence, and searches it. Visual synthesis degrades to timestamped evidence instead of guessing.
Give AI agents eyes, ears, and verifiable results. Watch Skill turns video, audio and screen activity into searchable, timestamped evidence, and answers did that actually work? with a deterministic contract rather than a model's opinion.
The user asks a question about a video that was already watched or indexed — "what did they say about X", "what error code…
The user wants structure pulled out of a watched video — "make chapters for this video", "where does the bug appear in this…
The user corrected an answer about a video — "no, it actually says X", "that's the wrong timestamp", "you misread the error code"…
A Watch Skill command failed, video download broke, ffmpeg or yt-dlp is missing, a provider returned an error, local vision…
The user wants to share or hand off what was found in a video — "send this analysis to my team", "make a page I can share",…