AC net-intel
UHCI Network Intelligence — AI-powered wireless network manager with SAC-LTC. Diagnoses, optimizes, and manages Wi-Fi and 3G/4G/5G hotspot switching. Provides deep RF environment awareness: signal quality, interference sources, channel congestion, presence detection, and navigation guidance. Use when: wifi slow, network issues, internet problems, signal weak, hotspot management, check connection, speed test, fix network, optimize wifi, why is my internet slow, network switch, net intel, sentinel mode, is someone nearby, interference, congestion, which direction, channel map, what is fighting for my network, which AP is best, walk towards signal.
As a process C 53/100 · Has gaps — weak spots: when it triggers, inputs and preconditions, consistency
How to improve
- 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: 17. 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 53/100
- 0Inputs and preconditions. Does not say what the process needs to start
- 20When it triggers. No condition that starts the skill
- 30Running it twice. 4 mutating operations with no state check
- 40Consistency. Frontmatter name (net-intel) differs from the folder (agentic-wireless-manager)
- 55Failures and branches. 1 branches
- 60Tools and files. Uses tools (bash, python) that frontmatter does not declare
- 60Result and completion. Output format stated, no completion criterion
- 70Execution cost. Instruction body is 4715 tokens
- 100Steps. 61 steps
- 100Progress reporting. Reports progress
- medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
- low 17 top-level sections: this looks like several domains in one skill
- low The response is described with custom markup (3 tags): a typed call is more reliable
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)
- -45 reference files, but SKILL.md never points to them: the model will not open them
- +1No license
- +2Single-language instructions
- +3Description length 653: enough signal without eating the budget
- +4Structure: 31 headings
- +3Step-by-step instructions: 61 items
- +3Output format is stated explicitly
- +4Has examples (23 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 83.