AC etl_pipeline_generator
Generate automated ETL pipelines for transforming and loading data into graph databases or knowledge graphs.
Generate automated ETL pipelines for transforming and loading data into graph databases or knowledge graphs.
As a process C 60/100 · Has gaps — weak spots: when it triggers, consistency, running it twice
ProcedureData 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 108 chars; the Hermes authoring standard requires ≤ 60 (one sentence, ending with a period) - note
frontmatter-keyunknown frontmatter key "title"
Process rating: all ten parameters 60/100
- 0Progress reporting. Says nothing while it works
- 20When it triggers. No condition that starts the skill
- 30Running it twice. 2 mutating operations with no state check
- 40Consistency. Frontmatter name (etl_pipeline_generator) differs from the folder (etl-pipeline-generator)
- 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
- 70Inputs and preconditions. Inputs and preconditions are listed
- 100Steps. 68 steps
- 100Execution cost. Instruction body is 2372 tokens
- low 13 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)
- +3Description length 108: 120–800 characters recommended
- -31 of 1 scripts are never mentioned in SKILL.md
- +2Single-language instructions
- +4Structure: 35 headings
- +3Step-by-step instructions: 68 items
- +3Output format is stated explicitly
- +4Has examples (19 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: 74.
External checks
ClawHub: clean
This is a coherent ETL pipeline-generation skill, but users should treat generated pipelines as capable of reading sensitive sources and writing or deleting target data.
LLM: benign (medium) · VirusTotal: · 7 Jun 2026