SKILLEMALL.ai

AC AWS Knowledge

AWS expert powered by the AWS Knowledge MCP Server (via mcporter). Provides real-time access to AWS documentation, best practices, SOPs, and regional availability. Use when the user asks about AWS services (S3, EC2, Lambda, CDK, CloudFormation, ECS, EKS, RDS, DynamoDB, Bedrock, SageMaker, IAM, VPC, etc.), AWS architecture patterns, deployment guidance, troubleshooting, regional availability, cost optimization, security hardening, or any "how do I do X on AWS" question. Also activates for CDK/CloudFormation infrastructure-as-code questions and AWS Well-Architected guidance.

ClawHub Agent Skills author: w0yne v1.0.2 MIT-0 3 files body ≈ 705 tokens Open the sourceclawhub.ai analyzed 3 d ago

As a process C 55/100 · Has gaps — weak spots: result and completion, inputs and preconditions, consistency

ReferenceAWSInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
A
93/100
safety, quality, tests
Safety 60%
100
Quality 40%
83
Run on models
none yet
Process rating
C
55/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Progress reporting w 2
0
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: 3. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • warning name-format name should be kebab-case (lowercase letters, digits, hyphens)

    Process rating: all ten parameters 55/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 2 mutating operations with no state check
    • 40Consistency. Frontmatter name (AWS Knowledge) differs from the folder (aws-knowledge)
    • 55Failures and branches. 1 branches
    • 70When it triggers. States when to use, but not when not to
    • 85Steps. 11 steps, 1 vague phrases
    • 100Tools and files. No external tools needed
    • 100Execution cost. Instruction body is 705 tokens

    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 579: enough signal without eating the budget
    • +4Structure: 6 headings
    • +3Step-by-step instructions: 11 items
    • +4Has examples (1 code blocks)
    • +4Reference files are cited in the instructions (1 of 1)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 83.

    External checks

    ClawHub: clean
    This skill is a straightforward AWS documentation helper that calls a configured MCP server through mcporter, with no hidden scripts or unrelated access requests found.
    LLM: benign (high) · VirusTotal: · 29 May 2026