AC ble
Use BLEA to diagnose and automate local Bluetooth Low Energy devices. Trigger for BLE adapter or permission problems, nearby-device scans, deterministic device selection, GATT discovery and reads, bounded notification observation, read-only JSONL evidence capture, offline semantic comparison or replay of BLE captures, adapter-free CI tests, guarded request/notification exchanges, guarded writes, repeatable BLE YAML workflows, and raw-byte evidence collection through the `ble` CLI or BLEA MCP tools.
Use BLEA to diagnose and automate local Bluetooth Low Energy devices.
As a process C 52/100 · Has gaps — weak spots: result and completion, when it triggers, failures and branches
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: 5. 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 52/100
- 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. 2 mutating operations with no state check
- 40Result and completion. Does not say what the result is
- 40Consistency. Frontmatter name (ble) differs from the folder (blea)
- 60Tools and files. Uses tools (bash, python) that frontmatter does not declare
- 70Inputs and preconditions. Inputs and preconditions are listed
- 100Steps. 35 steps
- 100Execution cost. Instruction body is 2366 tokens
- medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
- low The response is described with custom markup (5 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)
- +3Output format is not stated: the model decides each time
- +4No input/output examples
- +1No license
- +2Single-language instructions
- +3Description length 503: enough signal without eating the budget
- +4Structure: 5 headings
- +3Step-by-step instructions: 35 items
- +4Reference files are cited in the instructions (2 of 2)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 84.