Postgres performance optimization and best practices from Supabase. Use this skill when writing, reviewing, or optimizing Postgres queries, schema designs, or database configurations.
This is a copy of a skill from another catalog; the rating counts the canonical one: postgres-best-practices(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: 37. 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 67/100
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
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. 15 steps
100Consistency. Name and required fields are in place
100Execution cost. Instruction body is 516 tokens
100Running it twice. No mutating operations
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 183: enough signal without eating the budget
+4Structure: 7 headings
+3Step-by-step instructions: 15 items
+4Has examples (1 code blocks)
+4Reference files are cited in the instructions (3 of 33)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 88.
