bicameral-ingest

Ingest implementation-relevant decisions from source documents into the decision ledger.

Updated Aug 27, 2026
One-click install
npx skills add https://github.com/BicameralAI/bicameral-mcp --skill bicameral-ingest
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: bicameral-ingest
Source: https://github.com/BicameralAI/bicameral-mcp/tree/main/.claude/skills/bicameral-ingest
Command: npx skills add https://github.com/BicameralAI/bicameral-mcp --skill bicameral-ingest

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Ingest implementation-relevant decisions from source documents into the decision ledger to ensure traceability between product decisions and code.

Core Features & Use Cases

  • Boundary detection and pre-ingest segmentation for transcripts, PRDs, Slack threads, and design documents.
  • Grounding support for both code-region based ingests and natural-language ingests, with optional post-ingest context-sentry reconciliation.
  • End-to-end workflow orchestration (preflight, ingest, and gap-judgment) to surface business-tied decisions and drift.

Quick Start

Feed source documents to bicameral-ingest to seed the decision ledger.

Frequently Asked Questions about bicameral-ingest

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I ingest decisions from source documents into a traceable ledger?

To ingest decisions into a traceable ledger, feed source documents like PRDs or Slack threads to the Skill. It handles boundary detection, pre-ingest segmentation, and grounding to capture business-tied decisions for future pinning and review.

What is decision traceability and how does boundary detection work for transcripts?

Decision traceability links product decisions to code regions. Boundary detection works by performing pre-ingest segmentation on transcripts and design documents to isolate implementation-relevant decisions before capturing them in the ledger.

Can I use natural language formats for grounding decisions without strict code regions?

Yes, you can use natural language formats for grounding decisions without code regions. The ingestion process supports both code-region based ingests and natural-language ingests to ensure business-tied decisions are captured for review.

How do I surface drift and ensure only business-tied decisions are captured during ingestion?

To surface drift and ensure only business-tied decisions are captured, use the end-to-end workflow orchestration. It runs preflight, ingest, and gap-judgment phases with optional gates to filter out non-compliant entries.

What is the best way to manage compliance and traceability for product decisions across Slack threads?

The best way to manage compliance and traceability for product decisions across Slack threads is to ingest them into a decision ledger. Boundary detection and pre-ingest segmentation isolate the relevant decisions for compliance tracking.