AC python-auto-dev
Automated Python code generation, testing, debugging, and optimization with integrated conda environment management. Uses default conda path "C:\anaconda3\condabin\conda.bat" and environment "py311". Project files are stored at H:\code\Daily. Use when Codex needs to: (1) Generate Python code from specifications, (2) Create and run automated tests, (3) Debug code with interactive tools, (4) Optimize performance and code quality, (5) Manage conda environments for Python projects. This skill bundles executable scripts that handle the entire Python development workflow end-to-end.
As a process C 53/100 · Has gaps — weak spots: result and completion, inputs and preconditions, running it twice
How to improve
- 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: 8. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
frontmatter-yamlSKILL.md: the frontmatter is not valid YAML (YAML parse error: Nested mappings are not allowed in compact mappings at line 2, column 14: description: Automated Python code generation, testing, debugging, and optimiza… ^ ); fields were read line by line. The usual cause is a colon inside an unquoted value
Process rating: all ten parameters 53/100
- 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. 4 mutating operations with no state check
- 50When it triggers. No condition that starts the skill
- 55Failures and branches. 1 branches
- 60Tools and files. Uses tools (python) that frontmatter does not declare
- 100Steps. 40 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 743 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
- +3Output format is not stated: the model decides each time
- +4No input/output examples
- +1No license
- +2Single-language instructions
- +4Description says when NOT to use the skill
- +3Description length 583: enough signal without eating the budget
- +4Structure: 10 headings
- +3Step-by-step instructions: 40 items
- +4Reference files are cited in the instructions (1 of 1)
- +3All 5 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 81.