BC database-design
Database design principles and decision-making. Schema design, indexing strategy, ORM selection, serverless databases.
The skill teaches an AI agent to make decisions on database architecture: schema design, indexing, ORM selection, serverless options. Eight files, one script, around 538 body tokens. Checks show a B grade with 80% quality score and 64% process score.
No critical issues found, references intact, no lint errors. Model wasn't run, sandbox didn't test it. Platforms well-covered from Claude to DeepSeek. Worth installing if you need advice on schemas and indexes, but don't expect production-grade guarantees.
Database design principles and decision-making.
As a process C 64/100 · Has gaps — weak spots: result and completion, when it triggers, failures and branches
The same skill appears in 1 more place: agentic-awesome-skills
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
✓ No remarks against the Agent Skills spec
Process rating: all ten parameters 64/100
- 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. 1 mutating operations with no state check
- 40Result and completion. Does not say what the result is
- 70Inputs and preconditions. Inputs and preconditions are listed
- 100Tools and files. No external tools needed
- 100Steps. 11 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 538 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 118: 120–800 characters recommended
- +3Output format is not stated: the model decides each time
- +4No input/output examples
- +1No license
- +2Single-language instructions
- +4Structure: 8 headings
- +3Step-by-step instructions: 11 items
- +3All 1 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 80.