From skillful-alhazen
Interacts with TypeDB databases to list entities, ingest and analyze domain data, recognize schema gaps, and file issues for schema evolution using Python scripts.
How this skill is triggered — by the user, by Claude, or both
Slash command
/skillful-alhazen:_templateThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Use this skill to [describe primary use case]. Claude acts as [describe Claude's role in this domain].
Use this skill to [describe primary use case]. Claude acts as [describe Claude's role in this domain].
When to use: [Triggers: "ingest [item]", "analyze [item]", "show [entity]", ...]
make db-startuv sync --all-extras (from project root)make build-db after adding your schema.tqlTYPEDB_HOST: TypeDB server (default: localhost)TYPEDB_PORT: TypeDB port (default: 1729)TYPEDB_DATABASE: Database name (default: alhazen_notebook)uv run python .claude/skills/<your-domain>/<your-domain>.py list-entities
During sensemaking, if you encounter a concept, relationship, or entity type that has no place in the current TypeDB schema, that is a schema gap — a signal for schema evolution, not a failure.
When you notice a schema gap:
uv run python local_resources/skilllog/skill_logger.py file-schema-gap \
--skill <this-skill-name> \
--concept "<the concept you tried to represent>" \
--missing "<which TypeDB entity/relation/attribute is absent>" \
--suggested "<proposed TypeQL addition, or 'unknown' if unsure>"
Examples of schema gaps:
methodology attribute on scilit-paperUse --dry-run first to review the issue before filing it.
uv run emits a VIRTUAL_ENV warning to stderr. Always use 2>/dev/null when piping output to a JSON parser — never 2>&1, which merges the warning into stdout and breaks JSON parsing.
Before executing any commands, read USAGE.md in this directory for the complete command reference, workflows, and data model.
npx claudepluginhub sciknow-io/skillful-alhazen --plugin agent-osDesigns and implements TypeDB-backed curation skills following the Skillful Alhazen methodology with a 6-phase pattern (task definition, foraging, ingestion, sensemaking, analysis, reporting).
Interviews users about their datasets and databases to generate reusable data context skills that document schema, entities, metrics, and domain knowledge.
Generates or improves company-specific data analysis skills by extracting warehouse schemas, entity details, metrics definitions, and query patterns from analysts.