</> A WALKTHROUGH

# Skillful
Alhazen

A TypeDB‑powered, ontology‑backed notebook for agentic curation — one framework, many domains.

</> framework </> ontology </> agentic-memory </> biomedicine

Brief

"The duty of the man who investigates the writings of scientists, if learning the truth is his goal, is to make himself an enemy of all that he reads, and, applying his mind to the core and margins of its content, attack it from every side."

— Ibn al‑Haytham · 965–1039 AD

Speaker Gully Burns
Role Architect & Principal Engineer
Date May 19, 2026

Origin

## Curation - the process of organizing disparate, complex information into a well-formed knowledge representation.

— 2023

Literature curation

Hand‑curated metadata + Pre LLM NLP pipelines for biomedical papers. Brittle, limited, specializes.

2023-2024 (CZI)

Alhazen v1

First pass: langchain agent, nbdev implementation, Metadata curation for CryoET + RNASeq @ Biohub.

Feb 2026 —

Skillful Alhazen

Reimagined around Claude Code, TypeDB 3.x, and a skills marketplace. Domain becomes a plug‑in.

Today

CAIS 2026 Demo Paper

Demos:
· Jobhunt — demo
· Tech‑recon — curate
· Skill‑builder — model

The thesis

## Scientists need structured notebooks, not just memory of conversations or plain-text notes.

— WHAT MOST AGENT HARNESSES DO

Save context to MD files or a RAG index. Adequate for chat, but less powerful when you need to ask structured questions (How many? How much?) or require tracking complex context at scale.

— WHAT SKILLFUL ALHAZEN DOES

Use Claude Skills to store agentic context to a next-generation ontologically-powered knowledge graph (TypeDB).

— the lesson, recalled

Bioinformatics taught us data hygiene 20 years ago. This was the justification for biomedical ontologies previously — the same issues apply now.

The Realization

## When building agents, we must think of platforms

Solomon Hynes -- AI Engineering World's Fair, 2025

The Base Design

## System Architecture.

The notebook model

## A simple, general high-level schema for curation.

The Alhazen notebook schema is a type hierarchy. Everything inherits from alh‑identifiable‑entity. Each skill (e.g. job-hunting) extends this basic model.

COLLECTIONS · ENTITIES · RELATIONS Standard KG representation of things in the domain — corpora, papers, diseases, genes, jobs, people.
ARTIFACTS · FRAGMENTS Information entities that provide evidence for the definition of KG elements.
NOTES LLM analysis output saved to the knowledge graph.

The curation process

## Curation as a general pipeline.

01

Goal Definition

Interview to define success criteria

02

Discovery

Identify candidates, confirm for investigation

03

Ingestion

Collect artifacts: repos, docs, pages, PDFs

04

Sensemaking

Structured note-taking, typed knowledge

05

Analysis

Visualizations, queries, comparisons

06

Reporting

Synthesis against success criteria

example tech-recon — scoping interview defines what "better" means, then the skill discovers systems, ingests their codebases, makes typed notes, runs comparative analyses, and writes a synthesis report evaluated against the original criteria.

Deployment

## Use Claude's /plugin mechanism for deployment.

Skills are git repos. Register a URL, run make build-skills — schema loads into TypeDB, CLI mounts into Claude, dashboard wires into the UI.

CLAUDE CODE Natural language → skill CLI → TypeDB writes.
TYPEDB Dockerized notebook — runs locally, zero cloud dependency.
DASHBOARD Next.js UI — typed entities rendered live from the graph.

Hard problem · one

## Use ontological distinctions to create clarity in the data.

What's the best way to model a person in the system?

- · An author
- · A job-hunter
- · A contact at a company

Claude applies formal ontological best practices (Unified Foundational Ontology / OntoUML) to model Person + Roles.

Hard problem · two

## Evolve domain models with experience.

- EXECUTION FAILURE Crash · timeout · empty result.
- SCHEMA GAP Claude detects missing Concepts — filed as a GitHub issue.
- USER REQUEST User requests changes to the schema or code.

Gaps are signal — how the graph grows from use.

Hard problem · three

## Mapping memory data between schemas.

- A · EXTERNAL INTEGRATION
  Map complex data from external schemas into an Alhazen Notebook — e.g. Monarch's DisMech via a local TypeDB staging copy.
- B · SCHEMA MIGRATION
  Migrate notebook data between schema versions as the model evolves.

Claude authors GLAV mapping rules (Fagin, Kolaitis, Miller, Popa 2005) — TypeDB's expressive type system makes both cases tractable.

What's deployed

## Skills

Skill | Type | Domain | What it does | UI
--- | --- | --- | --- | ---
tech-recon | core | technology reconnaissance | Goal-driven investigation over code repos, papers, docs, web-pages, etc. of a specific technology | ✓
curation-skill-builder | core | domain modeling | Create schema, scripts, and dashboard for a new skill in a new repo | ✓
jobhunt | external | demo for job-hunting | Track positions · fit analysis · skill-gap identification | ✓
coach | external | health tracking demo | Unify Apple Watch, lab reports, doctor notes into one schema | ✓
dismech | external | disease mechanisms | Ingest Monarch Initiative's DisMech project (1000+ curated mechanisms), map to Alhazen schema | ✓
scilit | external | scientific literature | Subcomponent (used by other skills) to provide functionality around scientific publications. | ✓

In practice

## Agentic 'memory' consists of rendered views over the knowledge graph.

Disease mechanisms, scientific literature, competitive intelligence, career tracking — one schema-validated store. Each skill contributes its own typed namespace. You can load and combine any of them.

DM Monarch's dismech · 46,942 entities
SCILIT Scientific literature · 11,875 entities
TREC tech reconnaissance · 809 entities
JHUNT jhunt · 822 entities

Outcomes

## A paper, working demos, and a pragmatic agentic platform for curation.

Recognition

CAIS 2026

Accepted Demo paper 1st ACM Conference on Agents and Agentic Systems.

Paper · Conference Page

Examples

Working Demos Live examples across domains

Potential use cases for philanthropy, competitive analysis, biocuration, journalism - a viable open source approach for rapid development and prototyping.

Platform

Pragmatic Agentic Toolkit An open source toolkit based on a novel idea

Get started with git clone && make build.

Get in touch

## Any Questions?

gullyburns@gmail.com