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AB kwdb-text2sql-aiot

Convert natural language queries to KWDB SQL for time series data, relational data and cross-model analysis. Use this skill whenever users ask to query KWDB databases, write SQL for KWDB, or convert natural language to KWDB-specific SQL syntax. Supports: CREATE DATABASE/TABLE, downsampling, interpolation, latest value queries, aggregation analysis, cross-model queries, window/session/event analysis.

ClawHub Agent Skills author: KWDB v1.2.1 MIT-0 14 files body ≈ 2 144 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process B 78/100 · Nearly there — weak spots: result and completion, running it twice, 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
95/100
safety, quality, tests
Safety 60%
100
Quality 40%
87
Run on models
none yet
Process rating
B
78/100
Nearly there
Result and completion w 14
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: 14. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "triggers"

    Process rating: all ten parameters 78/100

    • 0Result and completion. Does not say what the result is
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 6 mutating operations with no state check
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Tools and files. No external tools needed
    • 100Steps. 74 steps
    • 100When it triggers. States when to use and when not to
    • 100Failures and branches. 9 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2144 tokens
    • 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)
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +3Description length 402: enough signal without eating the budget
    • +4Structure: 19 headings
    • +3Step-by-step instructions: 74 items
    • +4Has examples (5 code blocks)
    • +4Reference files are cited in the instructions (11 of 11)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 87.

    External checks

    ClawHub: suspicious
    The skill is mostly coherent as a KWDB SQL assistant, but it can execute broad write and administrative SQL against a live database with only a generic confirmation step.
    LLM: suspicious (high) · 28 Aug 2026