AI-native ontology engineering using 50+ MCP tools backed by an in-memory Oxigraph triple store. Build, validate, query, and govern RDF/OWL ontologies with a generate-validate-iterate loop. Use when building ontologies, knowledge graphs, RDF data, SPARQL queries, BORO/4D modeling, SHACL validation, clinical…
A recovery workflow for Exchange servers, which are systems used for organisational email and calendars. It covers planning a restoration from backups, creating a recovery job, and checking whether the service works afterward.
A knowledge network describing the main resource relationships in a Kubernetes cluster. Kubernetes is a system for running and managing containerised applications across machines.
Use when the user adds, changes, or refactors an LLM agent under src/agents/ — or anything that goes through Foundation Model API or an MLflow-traced LLM call. Mandatory under CNS §3.5 and .cursor/12-ai-feature-lifecycle.mdc. Walks the SPEC → dataset → eval-harness → impl → re-eval sequence.
Use when the user asks for a code review, asks to "review the code", or requests review of a feature/PR/branch. Runs the OntoBricks review checklist defined in .cursorrules.
Use when the user asks to deploy, ship, release, or push OntoBricks to Databricks. Wraps the Databricks Asset Bundle deploy for the FastAPI app and the MCP server, with the bootstrap-perms safety net described in README.md.
Query the Dev Knowledge Graph via SPARQL instead of grepping raw session files. Use this when asked about technologies, relationships between tools, session history, where a topic was discussed, or cross-platform knowledge. Prefer provenance-first SPARQL (message + session) over label-only CONTAINS or grep.
Build a trustworthy first ontology from an empty or near-empty ontology-atlas vault using only Atlas MCP evidence. Use when the user asks to analyze a codebase, bootstrap/fill its ontology, extract product meaning from a repository, or when a requested ontology task finds only starter nodes. Separate observed…
Measure whether Atlas actually works on a repository nobody here knows, by building a vault with a real MCP agent and then handing that vault — without the source — to a second agent who must answer questions from it alone. Use when changing the construction rules, the MCP read/write contract, the bootstrap skill, or…
Set up and maintain the GitHub Pages site for the mykg repo (SenolIsci/mykg) — a purpose-built pages/ folder (landing page adapted from README.md, blog posts, diagrams), built by a GitHub Actions workflow that runs Jekyll and deploys the result to a gh-pages branch. Use whenever the user wants to publish project…
Build, analyze, and visualize networks and graphs using NetworkX (Python). Use this skill whenever the user wants to: create graphs or networks, analyze graph properties, compute centrality measures, find shortest paths, detect communities, run graph algorithms, convert graphs to/from matrices or dataframes, visualize…
Run mykg knowledge-graph commands inside Claude Code from one slash command /mykg. The user describes intent in natural language (extract, append, resume, approve, walkthrough, parse-docs, fetch-web, query); the skill parses intent, builds the right mykg CLI command from the live --help output, confirms, runs it, and…
Build, validate, and convert SKOS thesauri and taxonomies. Use when creating or editing a controlled vocabulary (concepts, preferred/alternate labels, broader/narrower/related, mappings, concept-scheme metadata), when checking a vocabulary for integrity problems (cycles, orphans, duplicate labels, related/broader…
Design ObjectStack automation — Flows (visual logic), Triggers, Approvals, state machines, scheduled jobs, and webhooks. Use when the user is adding .flow.ts, wiring an event-driven rule, or modelling an approval chain. Do not use for data lifecycle hooks at the object layer (see objectstack-data) or for kernel /…
Design ObjectStack data schemas — objects, fields, field conditional rules, relationships, validations, indexes, lifecycle hooks, permissions, row-level security — and the seeds (defineSeed()) that load fixtures and reference data alongside them. Use when the user is creating or modifying .object.ts / .seed.ts files…
Author ObjectStack UI metadata — Views (list/form/kanban/calendar/gantt), Apps (navigation), Pages (structured plus the HTML and React source-authoring tiers, ADR-0080/0081), Dashboards, Reports, Charts, Actions, and package Docs (src/docs/.md). Use when the user is adding .view.ts / .app.ts / .dashboard.ts /…
Profile a research lab (a professor, or a school/department) over the last N years by collecting every paper from OpenAlex, structuring it into a temporal knowledge graph (ontology) — Papers, Researchers, Topics, Themes, Methods, Venues, Citations — and synthesizing a grounded report of the lab's year-by-year output…
Foundational layer beneath PDE. Governs eight-strata substrate (ontology, lineage, constellation, gap-map, agent-fleet, production-stack, internal-magnet, external-contribution) plus four operators (selfish-altruistic loop, magnetic membrane, portfolio operator, reflexive operator) for any domain a flag pierces …
Use this skill when the user wants to scaffold a new Obsidian-first LLM Wiki workspace, bootstrap Andrej Karpathy-style LLM Wiki structure, create raw/wiki/AGENTS.md layout in a fresh project, or standardize a markdown-first knowledge vault with scripts, templates, and meta pages. Trigger on requests to set up an LLM…
Grow and certify an existing wiki-only Obsidian-first LLM Wiki from raw sources through planning, semantic synthesis, wiki projection, procedure gates, batch certification, bounded repair, and proposal-only improvement. Use for new source ingest, corpus builds, or repeated wiki growth; do not use to scaffold a new…
Use when a repository needs one lightweight repository memory profile that evolves with the codebase to reduce drift, prevent structural mistakes, and keep current repository truth plus durable working context explicit. Trigger for requests to refresh current-state or architecture docs from live code, classify or…
A specification method for making a business action into a tool an AI agent can call safely. It defines the action’s inputs, outputs, permissions, preconditions, expected effects, error handling, and audit information.