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AB tableau

Expert on modern configuration converter — converts Excel/CSV/XML/YAML into Protobuf-backed JSON/Text/Bin configs via protogen + confgen pipeline. Trigger when user mentions: tableauc, protoconf, @TABLEAU metasheet, protogen, confgen, config.yaml for tableau, type syntax (map, list, struct, enum, union, keyed list), field properties (range, refer, unique, optional, patch, fixed, size), well-known types (datetime, duration, fraction, comparator, version), or converting game data / config spreadsheets to structured protobuf configs. Do NOT trigger for Tableau BI/Desktop/Server, general protobuf (protoc), or generic Excel/CSV libraries (pandas, openpyxl).

LeoYeAI/openclaw-master-skills Agent Skills author: LeoYeAI MIT 22 files body ≈ 4 594 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Expert on modern configuration converter — converts Excel/CSV/XML/YAML into Protobuf-backed JSON/Text/Bin configs via protogen + confgen pipeline.

As a process B 67/100 · Nearly there — weak spots: result and completion

GeneratorTableauExcelGitHubData and analyticstype 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%
88
Run on models
none yet
Process rating
B
67/100
Nearly there
Result and completion w 14
0
Tools and files w 18
60
Failures and branches w 10
60
the three weakest of ten parameters · all ten

How to improve

    For the model run — optional
    • 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: 20. 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

    • 0Result and completion. Does not say what the result is
    • 60Tools and files. Uses tools (bash, python) that frontmatter does not declare
    • 60Failures and branches. 2 branches
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 70Execution cost. Instruction body is 4594 tokens
    • 85Steps. 30 steps, 1 vague phrases
    • 100When it triggers. States when to use and when not to
    • 100Consistency. Name and required fields are in place
    • 100Running it twice. Mutating operations check current state
    • 100Progress reporting. Reports progress
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
    • low 16 top-level sections: this looks like several domains in one skill
    • low The response is described with custom markup (4 tags): a typed call is more reliable
    • medium 19 test cases, all positive: not one "should refuse" or "should ask first"
    • low No test case covers injection arriving through data

    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 660: enough signal without eating the budget
    • +4Structure: 24 headings
    • +3Step-by-step instructions: 30 items
    • +4Has examples (7 code blocks)
    • +4Reference files are cited in the instructions (4 of 4)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 88.