ontology-bootstrap
SolidBuild 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 implementation facts from proposed meanings, define and cite every domain/capability, answer competency questions, obtain independent source-hidden qualification and user approval, then write only the exact released plan with batch tools. Route mature vaults with 20+ curated nodes to ontology-sync instead; this is a workflow threshold, never a vault or project node limit.
Install
Quality Score: 85/100
Skill Content
Details
- Author
- wlsdks
- Repository
- wlsdks/ontology-atlas
- Created
- 4 months ago
- Last Updated
- today
- Language
- TypeScript
- License
- MIT
Integrates with
Similar Skills
Semantically similar based on skill content — not just same category
ontology-sync
After a code change, sync the project's ontology vault — read what's already there, identify new capabilities / elements / domains introduced by the change, and write them back via the MCP server (or fall back to the CLI). Use this at the end of any task that introduces a new feature, refactors a module, or renames a unit. Skip when the change is purely a typo, style nudge, or test fixture tweak.
ontology-field-trial
Measure Atlas ontology quality on an unfamiliar repository with a source-hidden handoff and four baseline measurements. Use for changes to meaning-construction rules or MCP read/write behavior that can change vault contents, or an explicit ontology-quality field trial. Skip UI work and wording-only clarifications that preserve the evidence, approval, and write contracts.
ontology-building
Builds, extends, reviews, or audits an ontology, domain model, conceptual/data model, taxonomy, or knowledge-graph/property-graph schema — deciding what entities, classes, relations, or aggregates should exist, how to shape a class or role hierarchy, how to scope with competency questions, and how to validate coherence (is-a correctness, anti-patterns, agent-actionability). Use when designing a data model, building a taxonomy, modeling a domain, defining a class hierarchy or knowledge-graph schema, or reviewing an existing ontology/domain model for coherence or AI-agent fitness. Skip for trained ML/statistical models, or routine schema tweaks with no structural decision.