SKILLEMALL.ai

AC sql

Writes, reviews, and optimizes SQL queries; designs schemas, indexes, and constraints; plans migrations for any relational database. Use when a query is slow, EXPLAIN shows a sequential scan, or an index is ignored; when rows come back duplicated, missing, or with inflated totals after a JOIN; on deadlocks, lock timeouts, "too many connections", or transactions that never commit; when designing tables, keys, and column types, normalizing or denormalizing a model, or deciding between a JSON column and real columns; for ALTER TABLE on a live table, expand-migrate-contract rollouts, backups and restores, replication lag, connection pooling, partitioning, bulk CSV imports, and moving data between engines; for window functions, CTEs, keyset pagination, upserts, full-text search, multi-tenancy, row-level security, and timezone handling in MySQL, SQLite, MariaDB, or SQL Server. Not for PostgreSQL server internals such as vacuum tuning and work_mem sizing, and not for ORM schema modeling inside a framework.

ClawHub Agent Skills author: Iván v1.0.4 MIT-0 21 files body ≈ 4 662 tokens Open the sourceclawhub.ai analyzed 2 d ago

Writes, reviews, and optimizes SQL queries; designs schemas, indexes, and constraints; plans migrations for any relational database.

As a process C 63/100 · Has gaps — weak spots: inputs and preconditions, running it twice

AnalyzerPostgreSQLMySQLData and analyticsInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
A
94/100
safety, quality, tests
Safety 60%
100
Quality 40%
85
Run on models
none yet
Process rating
C
63/100
Has gaps
Inputs and preconditions w 11
0
Running it twice w 4
30
When it triggers w 12
50
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: 21. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "slug"
    • note frontmatter-key unknown frontmatter key "homepage"
    • note frontmatter-key unknown frontmatter key "changelog"

    Process rating: all ten parameters 63/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 30Running it twice. 31 mutating operations with no state check
    • 50When it triggers. No condition that starts the skill
    • 60Tools and files. Uses tools (bash, web, node) that frontmatter does not declare
    • 60Result and completion. Output format stated, no completion criterion
    • 70Execution cost. Instruction body is 4662 tokens
    • 85Steps. 56 steps, 1 vague phrases
    • 100Failures and branches. 1 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Progress reporting. Reports progress
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
    • low 12 top-level sections: this looks like several domains in one skill

    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
    • +3Description length 1014: 120–800 characters recommended
    • +1No license
    • +2Single-language instructions
    • +4Description says when NOT to use the skill
    • +4Structure: 12 headings
    • +3Step-by-step instructions: 56 items
    • +3Output format is stated explicitly
    • +4Has examples (2 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 85.

    External checks

    ClawHub: clean
    This is a coherent SQL guidance skill with disclosed local preference memory and no hidden executable behavior, but users should review destructive SQL and redact sensitive query values while debugging.
    LLM: benign (high) · VirusTotal: · 10 Sept 2026