source-media-signals
Skill2 starsChanged 7 months agoMITView on GitHub
Collect first-pass source media facts, audio silence/energy, shot estimates, representative frame status, and persistent text/caption risk evidence.
In this repository17 other files it gives its agents.
- AGENTS.mdAGENTS.md
- CLAUDE.mdCLAUDE.md
- compare-video-runsSkill
- install-skill-packSkill
- layout-safety-reviewSkill
- package-review-promptSkill
- review-bundleSkill
- segment-evidenceSkill
- segment-storyboardSkill
- skill-catalogSkill
- storyboard-extractSkill
- storyboard-ocrSkill
- storyboard-transitionsSkill
- storyboard-understandSkill
- video-artifact-intakeSkill
- video-shotsSkill
- video-technical-reviewSkill
The file
We split this skill into the rules it gives an agent
1 rule. About 0 are common, 1 is distinctive, and 0 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈1
Distinctive · estimate
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 8
Use this when an agent needs conservative source-video signals before deeper review.
≈ “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
Source Media Signals
Use this when an agent needs conservative source-video signals before deeper review. This skill is intended as an intake layer, not a semantic video understanding pass.
This skill owns:
- ffprobe facts through the existing media probe
- audio energy and silence signals through
ffmpegwhen available - coarse shot and scene estimates through
video-shotswhen possible - representative frame evidence status
- persistent text/caption risk evidence from nearby OCR/layout artifacts when present
- deterministic frame/video fallback evidence when OCR/layout artifacts are absent
Inputs
Required:
{
"videoPath": "./output/example-run/output.mp4"
}Optional fields:
outputDir: directory forsource-media.signals.json; defaults next to the video.outputPath: exact JSON output path.sceneThreshold: ffmpeg scene-change threshold, default0.08.minShotDurationSeconds: minimum shot duration, default0.5.extractRepresentativeFrames: whether to writevideo-shots/shot-*.jpg, defaulttrue.runAudioSignals: whether to run ffmpeg audio analysis, defaulttrue.runVideoShots: whether to run shot analysis, defaulttrue.silenceNoiseDb: silence threshold, default-35.silenceMinDurationSeconds: minimum silence duration, default0.25.
Outputs
The result contains manifestPath and manifest. The manifest is
source-media-signals.v1 and writes:
ffprobe.statusand normalized media factsaudio.status, volume fields, and silence segmentsvideo.status, shot counts, boundaries, and scene-estimate metadatarepresentativeFrames.statusand frame paths when availabletextRisk.status,riskLevel, metrics, artifact paths, and bounded evidence items for visible source text, caption/bottom text, layout text issues, or deterministic frame/video fallback signals.
Interpretation Notes
Evidence status is part of the output contract. Treat unavailable, failed,
and skipped as abstention signals, not as proof that a risk is absent. When
OCR/layout artifacts are unavailable, textRisk.evidence still records fallback
frame/video signals so downstream guards can persist why the text risk decision
was conservative.
Repo-side runner:
node --import tsx scripts/harness/source-media-signals.ts
Installed-pack runner:
node ./.video-evaluator/agent/run-tool.mjs source-media-signals
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
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.

