BC rdf_triple_store_integration
Connect to RDF triple stores and execute SPARQL queries for storing, retrieving, and managing semantic knowledge graph data
Connect to RDF triple stores and execute SPARQL queries for storing, retrieving, and managing semantic knowledge graph data
As a process C 52/100 · Has gaps — weak spots: when it triggers, inputs and preconditions, consistency
How to improve
- For Hermes the description must be one sentence under 60 characters; move the conditions to a "When to Use" section.
For the model run — optional
- Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
- A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.
Guard findings · 0
✓ No critical or high findings
Files scanned: 6. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
name-formatname should be kebab-case (lowercase letters, digits, hyphens) - warning
description-long-hermesdescription is 123 chars; the Hermes authoring standard requires ≤ 60 (one sentence, ending with a period)
Process rating: all ten parameters 52/100
- 0Inputs and preconditions. Does not say what the process needs to start
- 20When it triggers. No condition that starts the skill
- 30Running it twice. 7 mutating operations with no state check
- 40Consistency. Frontmatter name (rdf_triple_store_integration) differs from the folder (rdf-triple-store-integration)
- 50Failures and branches. 0 branches, has a failure section
- 60Tools and files. Uses tools (python) that frontmatter does not declare
- 60Result and completion. Output format stated, no completion criterion
- 85Steps. 97 steps, 1 vague phrases
- 100Execution cost. Instruction body is 3470 tokens
- 100Progress reporting. Reports progress
- low 15 top-level sections: this looks like several domains in one skill
Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.
Quality signals
- +5Description has no quoted example phrases that should trigger the skill
- +4Description does not say when NOT to use the skill (false activations)
- -2localhost URLs: will not work for another user
- -247 emoji in the instructions: noise for the model
- -41 reference files, but SKILL.md never points to them: the model will not open them
- -31 of 1 scripts are never mentioned in SKILL.md
- +1No license
- +2Single-language instructions
- +3Description length 123: enough signal without eating the budget
- +4Structure: 105 headings
- +3Step-by-step instructions: 97 items
- +3Output format is stated explicitly
- +4Has examples (39 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 65.
External checks
ClawHub: clean
This skill is a disclosed RDF/SPARQL integration, but users should treat its write and federated-query examples as capable of changing or exposing graph data.
LLM: benign (high) · VirusTotal: · 7 Jun 2026