AC python-fastapi-development
Python FastAPI backend development with async patterns, SQLAlchemy, Pydantic, authentication, and production API patterns.
Python FastAPI backend development with async patterns, SQLAlchemy, Pydantic, authentication, and production API patterns.
As a process C 57/100 · Has gaps — weak spots: result and completion, when it triggers, failures and branches
ProcedureDockerPostgreSQLSoftware developmenttype and topics are labelled automatically from the skill text
This is a copy of a skill from another catalog; the rating counts the canonical one: python-fastapi-development (sickn33/agentic-awesome-skills)
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-keyunknown frontmatter key "risk" - note
frontmatter-keyunknown frontmatter key "source" - note
frontmatter-keyunknown frontmatter key "date_added"
Process rating: all ten parameters 57/100
- 0Failures and branches. Linear process with no failure handling
- 0Progress reporting. Says nothing while it works
- 20When it triggers. No condition that starts the skill
- 30Running it twice. 12 mutating operations with no state check
- 40Result and completion. Does not say what the result is
- 60Tools and files. Uses tools (python) that frontmatter does not declare
- 70Inputs and preconditions. Inputs and preconditions are listed
- 100Steps. 79 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1226 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 122: enough signal without eating the budget
- +4Structure: 40 headings
- +3Step-by-step instructions: 79 items
- +4Has examples (11 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 81.