youtube-intel-evidence-kit 0.1.0

Cross-video YouTube opinion-terrain packaging with caption-first evidence gates and MCP-ready handoff helpers.

YouTube Intel Evidence Kit

youtube-intel-evidence-kit is an alpha, dependency-free Python toolkit for packaging operator-admitted YouTube transcript evidence into reusable single-video records and cross-video opinion-terrain artifacts.

It does not download media, scrape YouTube, certify truth, or provide medical, financial, legal, investment, or political advice. The bundled demos use synthetic fixtures only.

Install

python -m pip install youtube-intel-evidence-kit==0.1.0

Python 3.10 or newer is required.

Quick start

youtube-intel doctor
youtube-intel topic-demo --out outputs/topic_demo
youtube-intel single-video-demo --out outputs/single_video

The topic demo produces reusable VideoKnowledgeRecord and TopicCollection artifacts with claim groups, disagreement candidates, outliers, speakers, timestamps, evidence coordinates, and modality gaps.

Main commands

• youtube-intel doctor — verify installed-package health and fixture availability.
• youtube-intel topic-demo — run the synthetic cross-video opinion-terrain flow.
• youtube-intel single-video-demo — run the synthetic residual package and handoff flow.
• youtube-intel package — build a residual package from operator-admitted segment JSON.
• youtube-intel worth — create an analysis-worth cost gate.
• youtube-intel single-video-handoff — create a validated AI handoff bundle.
• youtube-intel topic-mcp-stdio — expose a read-only TopicCollection JSON-RPC stdio facade.

Reliability boundaries

• Invalid or incoherent package, timestamp, schema, grouping, and handoff inputs fail closed.
• Source claims, evidence records, and trace rows preserve exact identity and provenance contracts.
• Installed wheels include byte-for-byte checked synthetic fixtures.
• Korean high-risk marker matching avoids generic capacity and substring collisions.
• Hidden-information aside detection requires explicit private evidence, direct observation, or cross-category signal combinations.

Development

git clone https://github.com/wva2ccyk-prog/youtube-intel-evidence-kit.git
cd youtube-intel-evidence-kit
python -m pip install -e ".[dev]"
python -m pytest -W error -q -p no:cacheprovider
python -m youtube_mcp_handoff.smoke

Project links

• Source and documentation: https://github.com/wva2ccyk-prog/youtube-intel-evidence-kit
• Issues: https://github.com/wva2ccyk-prog/youtube-intel-evidence-kit/issues
• Changelog: https://github.com/wva2ccyk-prog/youtube-intel-evidence-kit/blob/main/CHANGELOG.md

License

MIT