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AB aws-cdk-python-setup

Setup and initialization guide for developing AWS CDK (Cloud Development Kit) applications in Python. This skill enables users to configure environment prerequisites, create new CDK projects, manage dependencies, and deploy to AWS.

github/awesome-copilot Agent Skills author: github MIT 1 file body ≈ 580 tokens Open the sourcegithub.com↗ analyzed 10 h ago

Setup and initialization guide for developing AWS CDK (Cloud Development Kit) applications in Python.

As a process B 65/100 · Nearly there — weak spots: when it triggers, running it twice, progress reporting

ProcedureAWSInfrastructuretype and topics are labelled automatically from the skill text
Runs in: GitHub Copilot / VS Code Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) OpenClaw Goose OpenCode
JSON
Technical rating
A
90/100
safety, quality, tests
Safety 60%
100
Quality 40%
75
Run on models
none yet
Process rating
B
65/100
Nearly there
Progress reporting w 2
0
When it triggers w 12
20
Running it twice w 4
30
the three weakest of ten parameters · all ten

The same skill appears in 1 more place: RA-Skills

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
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

  • warning description-no-when description does not say WHEN to use the skill (no "use when")

Process rating: all ten parameters 65/100

  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 5 mutating operations with no state check
  • 50Failures and branches. 0 branches, has a failure section
  • 60Tools and files. Uses tools (bash, python) that frontmatter does not declare
  • 60Result and completion. Output format stated, no completion criterion
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 100Steps. 19 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 580 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)
  • +1No license
  • +2Single-language instructions
  • +3Description length 231: enough signal without eating the budget
  • +4Structure: 14 headings
  • +3Step-by-step instructions: 19 items
  • +3Output format is stated explicitly
  • +4Has examples (8 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 75.