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Build Domain Ontology

Organization
yogsoth-ai
build-domain-ontology

Construct a tool-independent domain ontology: scope the domain, atomize concepts, type relations, build hierarchy, audit consistency, detect gaps, and refine confidence.

Overview

Publisheryogsoth-ai
Repositoryde-anthropocentric-research-engine
Skill namebuild-domain-ontology
Stars
505
Forks
43
Bundled files
Instructions only
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by yogsoth-ai on GitHub. Read the source before you install it.

Installation

Install the Build Domain Ontology AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/yogsoth-ai/de-anthropocentric-research-engine/tree/main/skills/build-domain-ontology
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/yogsoth-ai/de-anthropocentric-research-engine.git /tmp/de-anthropocentric-research-engine
mkdir -p .claude/skills
cp -r /tmp/de-anthropocentric-research-engine/skills/build-domain-ontology .claude/skills/build-domain-ontology
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Build Domain Ontology in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Build Domain Ontology on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Build Domain Ontology is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

build-domain-ontology

Purpose

Construct a tool-independent domain ontology: scope the domain, atomize concepts, type relations, build hierarchy, audit consistency, detect gaps, and refine confidence.

Input contract

yaml
required: [domain_scope, concept_records, relation_evidence]
optional: [assumptions, prior_findings, evidence_updates]
constraints: [consume named scientific objects; preserve provenance; keep unresolved uncertainty visible]

Execution protocol

Do not perform called SOP operations inline; each loaded SOP owns its contract and thresholds.

  1. You MUST load skill scope-domain to freeze the domain question, inclusion boundary, granularity, and reopening trigger before extracting entities.
  2. You MUST load skill extract-concepts to capture source-defined concepts and relation candidates while retaining aliases, qualifiers, and unresolved meanings.
  3. You MUST load skill atomize-concept to split broad concepts into mechanism-, function-, level-, or context-specific facets.
  4. You MUST load skill type-relation to type each in-scope relation while preserving direction, rejected alternatives, and evidence.
  5. You MUST load skill construct-hierarchy to insert supported edges into an acyclic hierarchy and quarantine cycles, orphans, and missing levels.
  6. You MUST load skill audit-structure-consistency to check node and relation schemas with hierarchy, dependency, cardinality, and cycle constraints.
  7. You MUST load skill detect-coverage-gap to prioritize absent, thin, disconnected, or weak regions.
  8. You MUST load skill canonicalize-entity to merge only semantically identical aliases and near-duplicates while retaining rejected merges.
  9. You MUST load skill update-confidence-from-evidence to update confidence after identities and structure are stable. If the ontology is ready to express directed mechanisms, consider construct-causal-model as the next tactic.

Deviation: reorder only when a dependency is already satisfied or unavailable; record the reason and confidence effect.

Output contract

yaml
produces: [scoped_ontology, typed_relations, consistency_findings, coverage_gaps]
delta_fields: [findings, decisions]

Thresholds and quality gates

  • Each output is traceable to an input object, operation, and evidence reference.
  • Scope, assumptions, and unresolved alternatives remain explicit.
  • Retain $\alpha$ 0.05 and power 0.8 wherever the predeclared statistical design requires them.

Failure and counterexamples

Stop synthesis when a required object is absent, a precondition is violated, or a counterexample invalidates the proposed conclusion; return the partial delta with the failure recorded.

Provenance map

  • intermediate: knowledge-structuring/ontology-building [campaign]
  • resolved: domain-scoping
  • resolved: concept-extraction
  • resolved: relation-typing
  • resolved: taxonomy-validation
  • resolved: ontology-refinement
  • resolved: hierarchy-construction
  • intermediate: consistency-checking [tactic]

Preserved source criteria ledger

sourcecriteriontreatment
resolved v3 entries abovenode-specific criteriaretained and specialized to the v4 object contract
experiment-execution/statistical-testing$\alpha$ = 0.05fixed value retained where applicable
experiment-execution/sample-size-estimationpower = 0.8fixed value retained where applicable

Context checkpoint / Delta notes

Return the node-specific research-state delta and preserve findings, evidence updates, uncertainties, decisions, open questions, and recommended jumps as applicable.

Frequently asked questions

What does the Build Domain Ontology AI skill do?

Construct a tool-independent domain ontology: scope the domain, atomize concepts, type relations, build hierarchy, audit consistency, detect gaps, and refine confidence.

Why use Build Domain Ontology on TypingMind?

Because you install it once and use it with any model. Build Domain Ontology is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Build Domain Ontology in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/yogsoth-ai/de-anthropocentric-research-engine/tree/main/skills/build-domain-ontology. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Build Domain Ontology?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Build Domain Ontology?

As many as you like. As long as a model supports skills, you can use Build Domain Ontology with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Build Domain Ontology AI skill free?

Yes. It is published on GitHub by yogsoth-ai under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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