AC text_entity_relation_extractor
Extract entities and relationships from unstructured text and convert them into graph-ready structures such as triples, nodes, and edges.
Extract entities and relationships from unstructured text and convert them into graph-ready structures such as triples, nodes, and edges.
As a process C 62/100 · Has gaps — weak spots: when it triggers, failures and branches, consistency
GeneratorData and analyticsSoftware developmenttype and topics are labelled automatically from the skill text
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 137 chars; the Hermes authoring standard requires ≤ 60 (one sentence, ending with a period) - note
frontmatter-keyunknown frontmatter key "title"
Process rating: all ten parameters 62/100
- 0Failures and branches. Linear process with no failure handling
- 0Progress reporting. Says nothing while it works
- 20When it triggers. No condition that starts the skill
- 30Running it twice. 3 mutating operations with no state check
- 40Consistency. Frontmatter name (text_entity_relation_extractor) differs from the folder (text-entity-relation-extractor)
- 60Result and completion. Output format stated, no completion criterion
- 70Inputs and preconditions. Inputs and preconditions are listed
- 100Tools and files. No external tools needed
- 100Steps. 65 steps
- 100Execution cost. Instruction body is 2322 tokens
- low 11 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)
- -31 of 1 scripts are never mentioned in SKILL.md
- +2Single-language instructions
- +3Description length 137: enough signal without eating the budget
- +4Structure: 27 headings
- +3Step-by-step instructions: 65 items
- +3Output format is stated explicitly
- +4Has examples (17 code blocks)
- +4Reference files are cited in the instructions (1 of 1)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 77.
External checks
ClawHub: clean
This is a local text-extraction skill with a privacy caution around examples that include medical-style personal data.
LLM: benign (high) · VirusTotal: · 7 Jun 2026