client-knowledge

Name: client-knowledge
Availability: InStock
Author: takimoto-sketch

Collect client company information into a multi-layer knowledge base.

Updated Aug 23, 2026

Search Tags:#documentation #onboarding #claude-code #knowledge-base #gdrive #company-research #client-knowledge

Ready to run

npx skills add https://github.com/takimoto-sketch/medica-agent --skill client-knowledge

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: client-knowledge
Source: https://github.com/takimoto-sketch/medica-agent/tree/main/.claude/skills/client-knowledge
Command: npx skills add https://github.com/takimoto-sketch/medica-agent --skill client-knowledge

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Consolidates scattered public research and shared documents into a consistent, session-ready client knowledge base so agents and teams can quickly understand project context without manual aggregation.

Core Features & Use Cases

3-layer knowledge model: Integrates CLAUDE.md references, an operational {client}.md, and detailed research files under knowledge/{client}/research/.
Document ingestion and summarization: Reads shared Drive/docs, extracts facts, distinguishes hypotheses from facts, and records document IDs for traceability.
Onboarding and continuity: Ideal for client onboarding, project handovers, and ongoing account management where up-to-date context must be preserved across sessions.

Quick Start

Create a complete three-layer client knowledge base for Acme Corp by researching public sources and integrating any provided Drive documents into CLAUDE.md, Acme.md, and knowledge/acme/research/ files.

Frequently Asked Questions about client-knowledge

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

FAQPage Schema

How do I consolidate company research and shared documents into a client knowledge base?▼

To build a client knowledge base, you consolidate web research and shared Drive documents into a 3-layer structure: CLAUDE.md references, an operational {client}.md, and detailed research files under knowledge/{client}/research/.

How does the 3-layer client knowledge model work for onboarding?▼

The 3-layer model works by separating context into CLAUDE.md references for agents, an operational {client}.md for running workflows, and detailed research files for deep background, ensuring project continuity during onboarding and handovers.

Can I ingest shared Drive documents and track where the client information came from?▼

Yes, you can ingest shared Drive documents. The system extracts facts, distinguishes hypotheses from verified facts, and records document IDs under knowledge/{client}/research/ to preserve source attribution and traceability.

What is the best way to maintain client context for ongoing account management across sessions?▼

The best way to maintain client context is generating structured CLAUDE.md references and {client}.md templates that store up-to-date operational details, allowing agents to quickly understand project context without manual aggregation.

Does this approach separate verified facts from hypotheses when researching a client company?▼

Yes, the approach explicitly separates facts from hypotheses. When collecting and structuring client company information from public sources and shared documents, it preserves this distinction within the generated research files.