AC evallayer-evaluator
AI-powered deliverable evaluation via EvalLayer API. Extracts factual claims, scores quality, returns structured JSON verdicts with pass/fail, confidence scores, and payout recommendations. Use as a quality gate in agent workflows.
As a process C 58/100 · Has gaps — weak spots: inputs and preconditions, failures and branches, progress reporting
IntegrationInfrastructureAI and agentstype and topics are labelled automatically from the skill text
How to improve
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: 4. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- note
frontmatter-keyunknown frontmatter key "metadata.clawdbot"
Process rating: all ten parameters 58/100
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Failures and branches. Linear process with no failure handling
- 0Progress reporting. Says nothing while it works
- 50When it triggers. No condition that starts the skill
- 60Tools and files. Uses tools (web) that frontmatter does not declare
- 60Result and completion. Output format stated, no completion criterion
- 100Steps. 26 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1133 tokens
- 100Running it twice. No mutating operations
- medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
- 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)
- +1No license
- +2Single-language instructions
- +3Description length 231: enough signal without eating the budget
- +4Structure: 14 headings
- +3Step-by-step instructions: 26 items
- +3Output format is stated explicitly
- +4Has examples (10 code blocks)
- +3All 2 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 89.
External checks
ClawHub: clean
This skill is a disclosed wrapper around EvalLayer's external API, so it is acceptable to install if users understand that submitted deliverables leave their environment and may be stored for aggregation.
LLM: benign (high) · VirusTotal: · 29 May 2026