seed

Create provenance-tracked inbox items from URLs, files, or inline text.

226|55|Updated Mar 19, 2026
One-click install
npx skills add https://github.com/Miosa-osa/canopy --skill seed-miosa-osa
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: seed
Source: https://github.com/Miosa-osa/canopy/tree/main/library/skills/processing/seed
Command: npx skills add https://github.com/Miosa-osa/canopy --skill seed-miosa-osa

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Seed sources into your knowledge pipeline by creating provenance-tracked inbox items from URLs, files, or inline text, ensuring no untracked data enters the system.

Core Features & Use Cases

  • Ingest inputs from multiple sources (URL, file, text, clipboard) and convert them into structured inbox items with provenance metadata.
  • Deduplicate content by hashing and linking to existing seeds to prevent duplicates.
  • Generate and store comprehensive provenance records (source URL, acquisition time, method, and hash) for each item.

Quick Start

Seed a new source by passing a URL, file path, or inline text to create a provenance-tracked inbox item.

Frequently Asked Questions about seed

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

FAQPage Schema
How do I ingest external data into a provenance-backed knowledge pipeline?

To ingest external data into a provenance-backed knowledge pipeline, you can pass URLs, file paths, or inline text to create structured inbox items. The system automatically handles acquisition, format detection, and metadata extraction for durable downstream storage.

What is the best way to track provenance metadata for ingested URLs and files?

The best way to track provenance metadata for ingested URLs and files is to generate structured seed items during acquisition. This process automatically creates comprehensive provenance records containing the source URL, acquisition time, method, and content hash.

How can I deduplicate content during the knowledge ingestion workflow?

You can deduplicate content during the knowledge ingestion workflow by hashing incoming items and linking them to existing seeds. This prevents duplicate data from entering the inbox and ensures untracked data does not pollute the pipeline.

Does the seed ingestion process support inline text and clipboard inputs?

Yes, the seed ingestion process supports inline text and clipboard inputs alongside URLs and files. It normalizes these varied input formats, generates frontmatter metadata, and writes structured items into a durable inbox for downstream processing.

What limitations exist when normalizing external content for a provenance inbox?

A key limitation when normalizing external content for a provenance inbox is that the process relies on automated format detection and content hashing. Complex or non-standard input formats may require manual normalization before successful seed creation and deduplication.