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AB text-to-sql

Support generating SQL queries through natural language; use when users need to configure Text-to-SQL database, manage data topics, or generate SQL with natural language questions

ClawHub Agent Skills author: AskSqlAI v1.0.2 MIT-0 7 files body ≈ 4 866 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process B 70/100 · Nearly there — weak spots: when it triggers, consistency, progress reporting

GeneratorInfrastructuretype and topics are labelled automatically from the skill text
Runs in: OpenClaw Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) Goose OpenCode
JSON
Technical rating
A
97/100
safety, quality, tests
Safety 60%
99
Quality 40%
93
Run on models
none yet
Process rating
B
70/100
Nearly there
Progress reporting w 2
0
When it triggers w 12
20
Consistency w 8
40
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 · 1

    ✓ No critical or high findings

    Medium and low: 1
    • low Secrets in code secret-high-entropy-token scripts/query_sql.py:54
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      boundary = b'----…0gW'
      quoted

    Files scanned: 7. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "dependency"

    Process rating: all ten parameters 70/100

    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 40Consistency. Frontmatter name (text-to-sql) differs from the folder (text2sql)
    • 60Result and completion. Output format stated, no completion criterion
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 70Failures and branches. 7 branches
    • 70Execution cost. Instruction body is 4866 tokens
    • 100Tools and files. No external tools needed
    • 100Steps. 164 steps
    • 100Running it twice. Mutating operations check current state
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing

    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)
    • +1No license
    • +2Single-language instructions
    • +3Description length 179: enough signal without eating the budget
    • +4Structure: 32 headings
    • +3Step-by-step instructions: 164 items
    • +3Output format is stated explicitly
    • +4Has examples (14 code blocks)
    • +4Reference files are cited in the instructions (1 of 1)
    • +3All 4 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 93.

    External checks

    ClawHub: suspicious
    This text-to-SQL skill may be useful, but it handles database credentials and sends database or spreadsheet contents to asksql.ai in ways that are not clearly disclosed or tightly scoped.
    LLM: suspicious (high) · 8 Jul 2026