How do I install the Ontology Shacl Validate skill?

Run npx skillmds@latest add daniel-dona/ontology-shacl-validate in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Ontology Shacl Validate safe to use?

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Which AI agents work with Ontology Shacl Validate?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Ontology Shacl Validate free to use?

Yes. Installing skills from SkillMD is free. This skill is licensed under MIT.

Who published Ontology Shacl Validate?

daniel-dona (@daniel-dona) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Ontology Shacl Validate by daniel-dona

Name: Ontology Shacl Validate
Rating: 4.5 (4 reviews)
Author: daniel-dona

File contents

Ontology SHACL Validation

Validates RDF instance data against SHACL shapes. If you don't have shapes yet, the script auto-generates minimal ones from owl:Class + rdfs:domain/range axioms.

Single script: scripts/shacl_validate.py

Auto-generated shapes set sh:nodeKind appropriately: sh:BlankNodeOrIRI for object properties, sh:Literal for datatype properties. This prevents false violations when instance data uses blank nodes.

What It Detects

Issue	Example
Missing required property	`:Person` has no `:name` but `owl:minCardinality 1` → violation
Wrong datatype	`:age "twenty"` but SHACL shape expects `xsd:integer`
Cardinality violation	Multiple values for a functional property
Missing classes	Resources of a class that has no instances (info-only)

Setup

cd <your-ontology-repo>
python3 -m venv .venv
source .venv/bin/activate

pip install rdflib pyshacl

After finishing: deactivate the venv and remove it:
deactivate && rm -rf .venv
Skip this if the user asks to keep the environment.

Usage

# Auto-generate shapes from schema axioms
python scripts/shacl_validate.py . -o SHACL_REPORT.md

# Use your own shapes file
python scripts/shacl_validate.py . --shapes shapes.ttl -o report.md

# JSON for CI
python scripts/shacl_validate.py . --format json

Standardized Report

All skills support --format report which outputs a common JSON schema:

{
  "skill": "skill-name",
  "summary": {"errors": N, "warnings": N, "info": N},
  "issues": [{"file": ".ttl", "element": ":Class", "message": "...",
              "severity": "error|warning|info", "check": "RULE",
              "suggestion": "fix"}]
}

This format is consumed by ontology-full-audit to produce unified reports.

Output Files

Never write files into the repository without permission. Before generating any report or output file, ask the user where to save it (e.g. -o ../report.md or an absolute path outside the repo). The default output path in script examples is only a suggestion — always confirm with the user first.

Important Rules

Auto-generated shapes are minimal. They only cover cardinality constraints from owl:minCardinality/owl:maxCardinality restrictions. For richer validation (pattern, datatype, class membership), write explicit SHACL shapes.
SHACL validates instances, not schemas. The script separates shape definitions from data triples automatically. If you have no instances yet, the validation will pass trivially.
Combine with OOPS. OOPS checks schema design pitfalls. SHACL checks data quality. Together they cover the full ontology lifecycle.

---name: ontology-shacl-validatedescription: Provides a script and instructions to validate instance data against SHACL shapes using pySHACL, auto-generating shapes from OWL axioms if needed. Use when checking data quality or enforcing schema constraints.license: MIT---# Ontology SHACL ValidationValidates RDF instance data against SHACL shapes. If you don't have shapes yet,the script auto-generates minimal ones from `owl:Class` + `rdfs:domain`/`range` axioms.**Single script:** `scripts/shacl_validate.py`Auto-generated shapes set `sh:nodeKind` appropriately: `sh:BlankNodeOrIRI` forobject properties, `sh:Literal` for datatype properties. This prevents falseviolations when instance data uses blank nodes.## What It Detects| Issue | Example ||-------|---------|| Missing required property | `:Person` has no `:name` but `owl:minCardinality 1` → violation || Wrong datatype | `:age "twenty"` but SHACL shape expects `xsd:integer` || Cardinality violation | Multiple values for a functional property || Missing classes | Resources of a class that has no instances (info-only) |## Setup```bashcd <your-ontology-repo>python3 -m venv .venvsource .venv/bin/activatepip install rdflib pyshacl```> **After finishing:** deactivate the venv and remove it:> ```bash> deactivate && rm -rf .venv> ```> Skip this if the user asks to keep the environment.## Usage```bash# Auto-generate shapes from schema axiomspython scripts/shacl_validate.py . -o SHACL_REPORT.md# Use your own shapes filepython scripts/shacl_validate.py . --shapes shapes.ttl -o report.md# JSON for CIpython scripts/shacl_validate.py . --format json```## Standardized ReportAll skills support `--format report` which outputs a common JSON schema:```json{ "skill": "skill-name", "summary": {"errors": N, "warnings": N, "info": N}, "issues": [{"file": ".ttl", "element": ":Class", "message": "...", "severity": "error|warning|info", "check": "RULE", "suggestion": "fix"}]}```This format is consumed by `ontology-full-audit` to produce unified reports.## Output Files**Never write files into the repository without permission.** Before generatingany report or output file, ask the user where to save it (e.g. `-o ../report.md`or an absolute path outside the repo). The default output path in scriptexamples is only a suggestion — always confirm with the user first.## Important Rules1. **Auto-generated shapes are minimal.** They only cover cardinality constraints from `owl:minCardinality`/`owl:maxCardinality` restrictions. For richer validation (pattern, datatype, class membership), write explicit SHACL shapes.2. **SHACL validates instances, not schemas.** The script separates shape definitions from data triples automatically. If you have no instances yet, the validation will pass trivially.3. **Combine with OOPS.** OOPS checks schema design pitfalls. SHACL checks data quality. Together they cover the full ontology lifecycle.

Frequently asked questions

Ontology Shacl Validate