agent-watch
Skill1 starsChanged 3 months agoMITView on GitHub
Watch a video or screen recording, ground answers in what was actually shown and said, and optionally distill the watch session into a new installed hyper-specific skill. Use when Codex needs to analyze a YouTube video, local `.mp4` or `.mov`, product demo, course lesson, bug repro recording, presentation, tutorial, cooking video, workflow walkthrough, or any visual process where transcript-only reasoning is insufficient and the resulting learning may need to become a reusable skill.
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The file
Show its rules and text
We split this skill into the rules it gives an agent
18 rules. About 0 are common, 14 are distinctive, and 4 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈14
Distinctive · estimate
This author's own choices. Worth a look.
4
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 8
Use this skill when the model needs real video eyes and ears, not guesses from a title or transcript alone.
- Distinctive: line 10
Keep the upstream watch runtime intact.
- Distinctive: line 15
Run the bundled setup preflight before the first watch run.
- Very specific: line 16
Run the bundled watch.py pipeline against the URL or local file.
- Distinctive: line 19
If requested, distill the watch session into a new hyper-specific skill and install it locally.
- Very specific: line 23
Treat the bundled scripts in scripts/ as the canonical runtime.
- Distinctive: line 24
Prefer what was actually seen and heard over inferred summaries.
- Distinctive: line 25
Use focused re-runs for specific moments instead of sparse whole-video rescans.
- Distinctive: line 26
Do not create a learned skill from vague inspiration alone.
- Distinctive: line 27
Only create a learned skill when the video yields a repeatable, operational pattern that would help future work.
- Distinctive: line 28
When creating a learned skill, make it specific to the observed method, style, sequence, tools, constraints, cues, and failure modes from …
- Distinctive: line 29
Do not genericize the learned skill into broad internet advice unless the user explicitly asks for abstraction.
- Distinctive: line 33
Read references/watch-operations.md before substantial use.
- Distinctive: line 45
When the user wants the watch session converted into a reusable skill, read references/skill-distillation.md and follow it strictly.
- Very specific: line 49
create a new skill scaffold with scripts/init_learned_skill.py
- Distinctive: line 50
write the new skill in the repo or install root the user wants
- Very specific: line 57
Use scripts/init_learned_skill.py when you need a fast local scaffold for a new learned skill.
- Distinctive: line 67
Read these only as needed:
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Agent Watch
Use this skill when the model needs real video eyes and ears, not guesses from a title or transcript alone.
Keep the upstream watch runtime intact. Extend it by turning durable lessons from a watch session into a new local skill only when the user wants that outcome.
Core Workflow
- Resolve the absolute path of this skill directory.
- Run the bundled setup preflight before the first watch run.
- Run the bundled
watch.pypipeline against the URL or local file. - Inspect the returned frames and transcript together.
- Answer the user from observed evidence, with timestamps when helpful.
- If requested, distill the watch session into a new hyper-specific skill and install it locally.
Working Rules
- Treat the bundled scripts in
scripts/as the canonical runtime. - Prefer what was actually seen and heard over inferred summaries.
- Use focused re-runs for specific moments instead of sparse whole-video rescans.
- Do not create a learned skill from vague inspiration alone.
- Only create a learned skill when the video yields a repeatable, operational pattern that would help future work.
- When creating a learned skill, make it specific to the observed method, style, sequence, tools, constraints, cues, and failure modes from that source.
- Do not genericize the learned skill into broad internet advice unless the user explicitly asks for abstraction.
Setup and Execution
Read references/watch-operations.md before substantial use.
Use it for:
- setup and preflight flow
- detail modes and token tradeoffs
- focused-window re-runs
- transcript-cue frames
- failure handling
Distilling a Learned Skill
When the user wants the watch session converted into a reusable skill, read references/skill-distillation.md and follow it strictly.
Default behavior:
- create a new skill scaffold with
scripts/init_learned_skill.py - write the new skill in the repo or install root the user wants
- include only knowledge actually learned from the watched source
- make the skill narrow, operational, and non-generic
- install it locally after writing it
Bundled Helper
Use scripts/init_learned_skill.py when you need a fast local scaffold for a new learned skill. It creates:
SKILL.mdagents/openai.yamlreferences/source-notes.md
Then replace the scaffold text with the actual learned procedure from the watch session.
Read Next
Read these only as needed:
- references/watch-operations.md
- references/skill-distillation.md
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Your trail
Files you open will collect here, so you can find them again.
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Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

