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AB data-architect

Use this skill to assess, design, and evolve data architectures, including data platforms, data products, data mesh adoption, event-driven data flows, governance, modeling, and migration decisions. Load it when teams need workload-grounded tradeoffs, ownership and quality agreements, or a current-to-target data architecture. Do not use it for pipeline or platform operations, implementation details, interface contract semantics, SQL tuning, or statistical modeling; route those to data-engineering, platform-engineering, api-design-and-evolution, postgres, or data-scientist.

magnus919/agent-skills Agent Skills author: magnus919 MIT 16 files · 1 script body ≈ 3 224 tokens Open the sourcegithub.com↗ analyzed 26 h ago

Use this skill to assess, design, and evolve data architectures, including data platforms, data products, data mesh adoption, event-driven data flows…

As a process B 73/100 · Nearly there — weak spots: result and completion, progress reporting

AnalyzerPostgreSQLData and analyticsInfrastructureOperations and projectstype and topics are labelled automatically from the skill text
Runs in: 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) OpenClaw Goose OpenCode
JSON
Technical rating
A
98/100
safety, quality, tests
Safety 60%
100
Quality 40%
95
Run on models
none yet
Process rating
B
73/100
Nearly there
Progress reporting w 2
0
Result and completion w 14
40
When it triggers w 12
50
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: 15. 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 73/100

    • 0Progress reporting. Says nothing while it works
    • 40Result and completion. Does not say what the result is
    • 50When it triggers. No condition that starts the skill
    • 50Failures and branches. 0 branches, has a failure section
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 85Steps. 51 steps, 1 vague phrases
    • 100Tools and files. No external tools needed
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 3224 tokens
    • 100Running it twice. No mutating operations
    • low 10 top-level sections: this looks like several domains in one skill
    • 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
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +4Description says when NOT to use the skill
    • +3Description length 578: enough signal without eating the budget
    • +4Structure: 15 headings
    • +3Step-by-step instructions: 51 items
    • +4Has examples (1 code blocks)
    • +4Reference files are cited in the instructions (10 of 10)
    • +3All 1 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 95.