SKILLEMALL.ai

AB aws-fis-experiment-execute

Use when the user wants to run a prepared AWS FIS experiment where the CloudFormation stack has already been deployed. Triggers on "execute FIS experiment", "run FIS experiment", "start chaos experiment", "执行 FIS 实验", "运行混沌实验", "执行故障注入实验", "run the experiment in [directory]", or when the user provides an FIS experiment template ID (e.g. EXT1a2b3c4d5e6f7). Does NOT deploy infrastructure — only checks that it is already deployed.

ClawHub Agent Skills author: panlm v1.0.0 MIT-0 6 files body ≈ 4 393 tokens Open the sourceclawhub.ai analyzed 4 d ago

As a process B 79/100 · Nearly there — no weak spots found

TemplateAWSInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
A
98/100
safety, quality, tests
Safety 60%
100
Quality 40%
96
Run on models
none yet
Process rating
B
79/100
Nearly there
Tools and files w 18
60
Result and completion w 14
60
When it triggers w 12
70
the three weakest of ten parameters · all ten

How to improve

    For the model run — optional
    • 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: 6. 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 79/100

    • 60Tools and files. Uses tools (bash, web, node) that frontmatter does not declare
    • 60Result and completion. Output format stated, no completion criterion
    • 70When it triggers. States when to use, but not when not to
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 70Execution cost. Instruction body is 4393 tokens
    • 100Steps. 54 steps
    • 100Failures and branches. 6 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Running it twice. Mutating operations check current state
    • 100Progress reporting. Reports progress
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing

    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

    • +4Description does not say when NOT to use the skill (false activations)
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 7 example trigger phrases
    • +3Description length 431: enough signal without eating the budget
    • +4Structure: 21 headings
    • +3Step-by-step instructions: 54 items
    • +3Output format is stated explicitly
    • +4Has examples (8 code blocks)
    • +4Reference files are cited in the instructions (2 of 2)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 96.

    External checks

    ClawHub: suspicious
    This skill is transparent about running AWS fault-injection experiments, but it deserves review because it can affect live cloud resources and broadly collect Kubernetes/application logs by default.
    LLM: suspicious (high) · VirusTotal: · 29 May 2026