norton77930

Corpus Ingest Core — Data skill for Claude Code

Data community

Deterministic, provenance-tracked pipeline that turns spoken content into verified, searchable knowledge.

How to install Corpus Ingest Core

This entry records only its repository, not the path inside it, so there is no exact command to give. Open norton77930/corpus-ingest-core and copy the folder into ~/.claude/skills/, or the file into ~/.claude/agents/.

What Corpus Ingest Core does

Deterministic, provenance-tracked pipeline that turns spoken content into verified, searchable knowledge. MCP server + Claude Skills.

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README

Corpus Ingestion Core

Turns podcast audio into verifiable, searchable knowledge. It takes an RSS feed (or an X / YouTube video), transcribes it locally with faster-whisper, and produces timestamped transcripts, summaries, entity mentions, and research artifacts on disk — exposed to AI agents through an MCP server and Skills.

[![tests](https://github.com/norton77930/corpus-ingest-core/actions/workflows/tests.yml/badge.svg)](https://github.com/norton77930/corpus-ingest-core/actions/workflows/tests.yml) [![license: MIT](https://img.shields.io/badge/license-MIT-blue.svg)](LICENSE) ![python: 3.11+](https://img.shields.io/badge/python-3.11%2B-blue.svg)

[繁體中文說明](README.zh-TW.md)

**In:** a podcast RSS feed, or a video URL. **Out:** transcripts, subtitles, summaries, mention indexes, and research report bundles under `data/`, plus a SQLite index for cross-episode search.

Nothing this project produces is investment advice.

Why it exists

Podcast audio is hard to cite. A claim you half-remember from an episode three months ago is effectively unrecoverable. This project makes that content addressable: every extracted claim keeps a timestamp back to the audio, so an answer can be traced to the second it was said.

Two properties follow from that goal and shape the whole design:

Local-first. Transcription runs on your machine. Audio and transcripts do not leave it unless you explicitly opt into an LLM step.
Evidence over inference. The deterministic path is the default. LLM interpretation is a separate, opt-in layer that never overwrites it.

Quickstart

Requires Python 3.11+. Examples use PowerShell; any shell works.

git clone https://github.com/norton77930/corpus-ingest-core.git
cd corpus-ingest-core
python -m venv .venv
.\.venv\Scripts\Activate.ps1
python -m pip install -e .[dev]