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AC aws-anomaly-explainer

Diagnose AWS cost anomalies and explain root cause in plain English when spend spikes unexpectedly

modbender/skill-library-mcp Agent Skills author: modbender MIT 1 file body ≈ 380 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Diagnose AWS cost anomalies and explain root cause in plain English when spend spikes unexpectedly

As a process C 59/100 · Has gaps — weak spots: when it triggers, inputs and preconditions, consistency

AnalyzerAWSInfrastructuretype and topics are labelled automatically from the skill text
Runs in: OpenClaw Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) Goose OpenCode
JSON
Technical rating
A
91/100
safety, quality, tests
Safety 60%
100
Quality 40%
77
Run on models
none yet
Process rating
C
59/100
Has gaps
Inputs and preconditions w 11
0
Progress reporting w 2
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 · 0

    ✓ No critical or high findings

    Files scanned: 1. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "tools"
    • note frontmatter-key unknown frontmatter key "pack"
    • note frontmatter-key unknown frontmatter key "tier"
    • note frontmatter-key unknown frontmatter key "price"

    Process rating: all ten parameters 59/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 1 mutating operations with no state check
    • 40Consistency. Frontmatter name (aws-anomaly-explainer) differs from the folder (anomaly-explainer)
    • 50Failures and branches. 0 branches, has a failure section
    • 60Result and completion. Output format stated, no completion criterion
    • 100Tools and files. Tools declared in frontmatter
    • 100Steps. 20 steps
    • 100Execution cost. Instruction body is 380 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)
    • +3Description length 98: 120–800 characters recommended
    • +4No input/output examples
    • +1No license
    • +2Single-language instructions
    • +4Structure: 5 headings
    • +3Step-by-step instructions: 20 items
    • +3Output format is stated explicitly

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 77.