AC predictive-scaler
Analyze resource usage patterns and predict future scaling needs using trend analysis and forecasting methods for capacity planning and auto-scaling decisions.
As a process C 57/100 · Has gaps — weak spots: when it triggers, inputs and preconditions, failures and branches
AnalyzerInfrastructureData and analyticstype 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
✓ No remarks against the Agent Skills spec
Process rating: all ten parameters 57/100
- 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
- 20When it triggers. No condition that starts the skill
- 40Consistency. Frontmatter name (predictive-scaler) differs from the folder (jpeng-predictive-scaler)
- 60Result and completion. Output format stated, no completion criterion
- 100Tools and files. No external tools needed
- 100Steps. 25 steps
- 100Execution cost. Instruction body is 1806 tokens
- 100Running it twice. No mutating operations
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 159: enough signal without eating the budget
- +4Structure: 26 headings
- +3Step-by-step instructions: 25 items
- +3Output format is stated explicitly
- +4Has examples (15 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 87.
External checks
ClawHub: clean
This skill is a local forecasting utility that analyzes supplied resource data and does not show hidden access, persistence, or automatic infrastructure changes.
LLM: benign (high) · VirusTotal: · 29 May 2026