AB huawei-cloud-cloudrobo-r2c
Run the R2C (Robot-to-Cloud) data-plane client — start the robot-side edge client (Zenoh pub/sub with mTLS credential bundle, hardware adapter, translator, control loop). The credential bundle is produced by the robot skill's export-certificate command. This skill does NOT cover robot registration or certificate export (use the robot skill). Triggers include: r2c client, robot edge client, Zenoh, mTLS, credential bundle, hardware adapter, robot config, dry_run, observation recording, custom adapter, robot-to-cloud, R2C客户端, 硬件适配器, 机器人配置, 凭证包.
Run the R2C (Robot-to-Cloud) data-plane client — start the robot-side edge client (Zenoh pub/sub with mTLS credential bundle, hardware adapter, translator…
As a process B 68/100 · Nearly there — weak spots: when it triggers, running it twice
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: 12. 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 68/100
- 20When it triggers. No condition that starts the skill
- 30Running it twice. 21 mutating operations with no state check
- 50Failures and branches. 0 branches, has a failure section
- 60Tools and files. Uses tools (bash, python) that frontmatter does not declare
- 70Inputs and preconditions. Inputs and preconditions are listed
- 70Execution cost. Instruction body is 4950 tokens
- 85Steps. 58 steps, 1 vague phrases
- 100Result and completion. Output format and completion criterion are stated
- 100Consistency. Name and required fields are in place
- 100Progress reporting. Reports progress
- low 10 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)
- +1No license
- +2Single-language instructions
- +3Description length 547: enough signal without eating the budget
- +4Structure: 20 headings
- +3Step-by-step instructions: 58 items
- +3Output format is stated explicitly
- +4Has examples (8 code blocks)
- +4Reference files are cited in the instructions (8 of 8)
- +3All 1 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 94.