SKILLEMALL.ai

AC querying-aws-cloudwatch

Runs SQL queries on CloudWatch Logs data exported as Apache Iceberg tables in S3 Tables. Covers VPC Flow Logs, WAF logs, CloudFront access logs, Route 53 resolver logs, Network Firewall logs, EKS audit logs, Verified Access logs, SES logs, VPC Lattice logs, Step Functions logs, NLB access logs, and 20+ other AWS vended data sources. Applies when analyzing network traffic, investigating security incidents, querying exported logs with SQL, enabling S3 Tables integration, configuring log export, correlating logs with other data, or running Athena queries on the aws-cloudwatch table bucket. Trigger phrases: query logs with SQL, analyze logs in Athena, SQL on VPC flow logs, investigate network traffic, run SQL on exported logs, enable S3 Tables for CloudWatch, correlate logs, historical log analysis, set up log querying.

ClawHub Claude Code author: Amazon Web Services 1 file body ≈ 3 225 tokens Open the sourceclawhub.ai analyzed 2 d ago

Runs SQL queries on CloudWatch Logs data exported as Apache Iceberg tables in S3 Tables.

As a process C 51/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

IntegrationAWSInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
A
92/100
safety, quality, tests
Safety 60%
99
Quality 40%
81
Run on models
none yet
Process rating
C
51/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
When it triggers w 12
20
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 · 1

    ✓ No critical or high findings

    Medium and low: 1
    • low Secrets in code secret-high-entropy-token SKILL.md:277
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      "Sid": "Enab…age",
      quoted

    Files scanned: 1. 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 51/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 20When it triggers. No condition that starts the skill
    • 50Failures and branches. 0 branches, has a failure section
    • 60Tools and files. Uses tools (bash, web) that frontmatter does not declare
    • 85Steps. 21 steps, 1 vague phrases
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 3225 tokens
    • 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

    • +5Description has no quoted example phrases that should trigger the skill
    • +4Description does not say when NOT to use the skill (false activations)
    • +3Description length 827: 120–800 characters recommended
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +4Structure: 19 headings
    • +3Step-by-step instructions: 21 items
    • +4Has examples (14 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 81.