interknow

Manage durable knowledge entries with provenance tracking and semantic retrieval.

3|1|Updated Feb 15, 2026

Search Tags:#knowledge management #semantic search #information retrieval #provenance #decay #compounding

Ready to run

npx skills add https://github.com/mistakeknot/Demarch --skill interknow

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: interknow
Source: https://github.com/mistakeknot/Demarch/tree/main/.gemini/generated-skills/interknow
Command: npx skills add https://github.com/mistakeknot/Demarch --skill interknow

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill addresses the challenge of managing and retrieving knowledge effectively by providing a structured system for compounding information with provenance and temporal decay.

Core Features & Use Cases

Knowledge Compounding: Create durable knowledge entries with provenance tracking, domain tags, and evidence anchors.
Semantic Retrieval: Query a repository of knowledge with domain-aware filtering and temporal decay considerations.
Use Case: Maintain a team's collective intelligence by systematically recording insights from discussions and research, ensuring that valuable information is retrievable and relevant over time.

Quick Start

Use the interknow skill to write a new knowledge entry about the 'Demarch' project, tagging it as 'software-development' and citing the 'README.md' as evidence.

Frequently Asked Questions about interknow

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

FAQPage Schema

How do I build a knowledge management repository with provenance tracking?▼

Build a knowledge management repository with provenance by creating markdown files with YAML frontmatter that record domain tags and evidence anchors. This systematically structures information, ensuring insights remain traceable to their original sources over time.

What is temporal decay in semantic search and information retrieval?▼

Temporal decay in semantic search is a mechanism that reduces the retrieval relevance of older knowledge entries over time. This ensures information retrieval queries prioritize recent software development insights, preventing outdated data from dominating search results.

How do I record and recall software development insights using markdown?▼

Record software development insights by writing markdown entries tagged with specific domains and citing evidence anchors. Recall them later using semantic retrieval with domain-aware filtering to locate the exact compounding information needed for your project.

Does qmd semantic search work with markdown files for knowledge compounding?▼

Yes, qmd semantic search works with markdown files by querying a repository of knowledge entries containing YAML frontmatter. This integration enables domain-aware filtering and knowledge compounding for software development insights.

Can I filter information retrieval results by domain in a knowledge repository?▼

Yes, you can filter information retrieval results by domain using domain-aware filtering capabilities during semantic search. This narrows the query scope to specific knowledge tags, returning only relevant entries from your knowledge repository.

When should I use a knowledge management system with temporal decay?▼

Use a knowledge management system with temporal decay when maintaining a team's collective intelligence where information relevance changes over time. It is ideal for software development insights where older data becomes obsolete and recent findings matter most.