BB huawei-cloud-cloudrobo-dispatch
Manage CloudRobo embodied-agent task dispatch (robo-dispatcher) — create embodied tasks that drive a robot via an exec/model and constraints, list/show tasks in a session, cancel tasks, and retrieve task results. Dispatch orchestrates robots (robot_id from cloudrobo-robot) with inference models (exec_model_id from cloudrobo-infer / cloudrobo-asset) inside a session. Triggers include: dispatch, dispatcher, task dispatch, embodied task, agent task, run task on robot, task scheduling, cancel task, task result, robo-dispatcher, 调度, 智能体调度, 任务下发, 机器人任务, 取消任务, 任务结果, 会话任务.
Manage CloudRobo embodied-agent task dispatch (robo-dispatcher) — create embodied tasks that drive a robot via an exec/model and constraints, list/show tasks…
As a process B 71/100 · Nearly there — weak spots: result and completion, running it twice
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
- 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: 10. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
description-no-whendescription does not say WHEN to use the skill (no "use when") - warning
body-longSKILL.md body ≈ 6356 tokens (recommended < 5000); move details to references/
Process rating: all ten parameters 71/100
- 30Running it twice. 68 mutating operations with no state check
- 40Result and completion. Does not say what the result is
- 50When it triggers. No condition that starts the skill
- 60Tools and files. Uses tools (bash, web, python) that frontmatter does not declare
- 70Inputs and preconditions. Inputs and preconditions are listed
- 70Execution cost. Instruction body is 6356 tokens
- 100Steps. 61 steps
- 100Failures and branches. 4 branches, has a failure section
- 100Consistency. Name and required fields are in place
- 100Progress reporting. Reports progress
- medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
- low 10 top-level sections: this looks like several domains in one skill
- low The response is described with custom markup (27 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
- +1No license
- +2Single-language instructions
- +3Description length 571: enough signal without eating the budget
- +4Structure: 26 headings
- +3Step-by-step instructions: 61 items
- +4Has examples (16 code blocks)
- +4Reference files are cited in the instructions (5 of 6)
- +3All 1 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 69.