BA alibabacloud-ros-agent
Use Alibaba Cloud ROS Agent through its StartChat API for remote infrastructure conversations. Trigger when the user explicitly asks for the ROS Agent, its StartChat API, or a remote iac-code conversation through Alibaba Cloud. Supports normal and selling Pipeline conversations, questions, candidate selection, correlated permission approval or denial, and explicit StopChat cancellation. Do not trigger for ordinary Alibaba Cloud infrastructure work that can use the local iac-code Skill, or for unrelated ROS API operations.
Use Alibaba Cloud ROS Agent through its StartChat API for remote infrastructure conversations.
As a process A 80/100 · Runs to the end — weak spots: result and completion, running it twice
What is at stake
Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.
Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.
The skill contains commands that delete files, rewrite disks or execute code fetched from the network. The agent may run them without asking if it believes the instructions require it.
Replace destructive commands with safe equivalents that ask for confirmation, scope them to one folder, and stop piping curl into a shell: pin a version and a checksum.
How to improve
- 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 · 1
✓ No critical or high findings
Medium and low: 1
-
medium Dangerous commands
cmd-eval-dynamicscripts/ros_agent.py:176Dynamic code execution from decoded/untrusted inputexec(compile(source, str(path), "exec"), globals())
Files scanned: 9. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
body-longSKILL.md body ≈ 7939 tokens (recommended < 5000); move details to references/
Process rating: all ten parameters 80/100
- 30Running it twice. 23 mutating operations with no state check
- 40Result and completion. Does not say what the result is
- 60Tools and files. Uses tools (bash, web, python) that frontmatter does not declare
- 70Execution cost. Instruction body is 7939 tokens
- 100Steps. 50 steps
- 100When it triggers. States when to use and when not to
- 100Inputs and preconditions. Inputs and preconditions are listed
- 100Failures and branches. 8 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 13 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)
- +3Output format is not stated: the model decides each time
- -33 of 4 scripts are never mentioned in SKILL.md
- +1No license
- +2Single-language instructions
- +3Description length 527: enough signal without eating the budget
- +4Structure: 14 headings
- +3Step-by-step instructions: 50 items
- +4Has examples (12 code blocks)
- +4Reference files are cited in the instructions (1 of 1)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 75.