SKILLEMALL.ai

AC cm-postgres-query-optimizer

Analyze slow PostgreSQL queries, interpret EXPLAIN ANALYZE output, identify performance bottlenecks, and recommend indexes, query rewrites, and configuration tuning. Parses query plans to find sequential scans, nested loops on large tables, poor join ordering, and missing indexes. Use when asked to optimize a SQL query, analyze a slow query, read EXPLAIN output, suggest PostgreSQL indexes, tune a query plan, fix a slow database query, or improve query performance. Triggers on "slow query", "query optimization", "EXPLAIN ANALYZE", "PostgreSQL performance", "query plan", "sequential scan", "index suggestion", "postgres tuning", "query optimizer", "database performance", "slow SQL", "pg_stat".

ClawHub Agent Skills author: charlie-morrison v1.0.1 MIT-0 2 files body ≈ 3 306 tokens Open the sourceclawhub.ai analyzed 3 d ago

As a process C 60/100 · Has gaps — weak spots: inputs and preconditions, consistency, running it twice

AnalyzerPostgreSQLSoftware developmentInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
A
97/100
safety, quality, tests
Safety 60%
100
Quality 40%
92
Run on models
none yet
Process rating
C
60/100
Has gaps
Inputs and preconditions w 11
0
Progress reporting w 2
0
Running it twice w 4
30
the three weakest of ten parameters · all ten

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: 2. 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 60/100

    • 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
    • 40Consistency. Frontmatter name (cm-postgres-query-optimizer) differs from the folder (postgres-query-optimizer)
    • 60Tools and files. Uses tools (node) that frontmatter does not declare
    • 60Result and completion. Output format stated, no completion criterion
    • 70When it triggers. States when to use, but not when not to
    • 70Failures and branches. 4 branches
    • 100Steps. 27 steps
    • 100Execution cost. Instruction body is 3306 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

    • +4Description does not say when NOT to use the skill (false activations)
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 12 example trigger phrases
    • +3Description length 699: enough signal without eating the budget
    • +4Structure: 18 headings
    • +3Step-by-step instructions: 27 items
    • +3Output format is stated explicitly
    • +4Has examples (13 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 92.

    External checks

    ClawHub: clean
    This is a plain PostgreSQL tuning advice skill with no executable code, though its SQL recommendations can affect a database if a user chooses to run them.
    LLM: benign (high) · VirusTotal: · 29 May 2026