</> CAIS 2026 · DEMO PAPER

# Skillful
Alhazen

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

</> ontology </> agentic-memory </> knowledge-graph </> biomedicine
Author Gully Burns
Conference 1st ACM Conf. on Agents & Agentic Systems
DOI 10.1145/3786335.3813226

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

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 breaks down when you need structured questions or 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 — the same issues apply now.

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 extends this basic model with its own typed namespace.

COLLECTIONS · ENTITIES KG representation — corpora, papers, diseases, genes, jobs, people.
ARTIFACTS · FRAGMENTS Information entities providing evidence for KG elements.
NOTES LLM analysis output saved to the knowledge graph.

Curation Pipeline

01 Goal Definition Interview to define success criteria
›
02 Discovery Identify candidates for investigation
›
03 Ingestion Collect artifacts: repos, docs, PDFs
›
04 Sensemaking Structured note-taking, typed knowledge
›
05 Analysis Visualizations, queries, comparisons
›
06 Reporting Synthesis against criteria

Three Hard Problems

Hard Problem · One

### Use ontological distinctions to create clarity.

Most schemas conflate person with role. We apply BFO/UFO patterns: a person is rigid and intrinsic; a role is anti-rigid and externally grounded. Strip the institution — the role evaporates, the person remains.

Hard Problem · Two

### Evolve domain models with experience.

Most agent stacks treat "I can't represent this" as a silent skip. We treat it as the most important signal.

- Execution Failure Crash, timeout, empty result.
- Schema Gap Claude detects missing concepts → filed as GitHub issue.
- User Request User requests schema or code changes.

Gaps are signal — how the graph grows from use.

Hard Problem · Three

### Map memory data between schemas.

Claude authors GLAV mapping rules (Fagin, Kolaitis, Miller, Popa 2005) for two cases:

A · External Integration

Map complex data from external schemas into the notebook — e.g. Monarch's DisMech via TypeDB staging.

B · Schema Migration

Migrate notebook data between schema versions as the model evolves.

Deployed Skills

Skill | Type | Domain | What it does
--- | --- | --- | ---
tech-recon | core | technology reconnaissance | Goal-driven investigation over code repos, papers, docs, web-pages
curation-skill-builder | core | domain modeling | Create schema, scripts, and dashboard for a new curation skill
jobhunt | external | career tracking | Track positions, fit analysis, skill-gap identification
coach | external | health tracking | Unify Apple Watch, lab reports, doctor notes into one schema
dismech | external | disease mechanisms | Ingest Monarch Initiative's DisMech (1000+ curated mechanisms)
scilit | external | scientific literature | Functionality for scientific publications (used by other skills)

In Practice

## Agentic 'memory' rendered from the knowledge graph.

Disease mechanisms, scientific literature, competitive intelligence, career tracking — one schema-validated store.

DM Monarch dismech · 46,942 entities
SCILIT Scientific literature · 11,875 entities
TREC Tech reconnaissance · 809 entities
JHUNT Job hunting · 822 entities

Outcomes

CAIS 2026 Accepted demo paper at 1st ACM Conference on Agents & Agentic Systems
Demos Live examples across biomedical, career, and journalism domains
Open Source git clone && make build
Email gullyburns@gmail.com
GitHub github.com/sciknow-io/skillful-alhazen
Paper doi.org/10.1145/3786335.3813226