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AB chief-data-officer-advisor

Data leadership advisor on data strategy, governance, quality, and platform decisions. Use when defining a data strategy, scoring data maturity, auditing data governance, evaluating a data platform, or designing the data org.

borghei/Claude-Skills Agent Skills author: borghei 7 files · 3 scripts body ≈ 2 805 tokens Open the sourcegithub.com↗ analyzed 7 h ago

Data leadership advisor on data strategy, governance, quality, and platform decisions.

As a process B 71/100 · Nearly there — weak spots: progress reporting

AnalyzerData and analyticstype 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
97/100
safety, quality, tests
Safety 60%
98
Quality 40%
95
Run on models
none yet
Process rating
B
71/100
Nearly there
Progress reporting w 2
0
When it triggers w 12
50
Failures and branches w 10
55
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 · 2

    ✓ No critical or high findings

    Medium and low: 2
    • low Secrets in code secret-high-entropy-token scripts/data_governance_audit.py:74
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      Control("GOV-…eme", "governance", "Classification scheme published and wired to access",
      quoted
    • low Secrets in code secret-high-entropy-token scripts/data_governance_audit.py:77
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      Control("GOV-…rly", "governance", "Internal audit runs at least quarterly",
      quoted

    Files scanned: 7. 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 71/100

    • 0Progress reporting. Says nothing while it works
    • 50When it triggers. No condition that starts the skill
    • 55Failures and branches. 1 branches
    • 60Tools and files. Uses tools (bash) that frontmatter does not declare
    • 60Result and completion. Output format stated, no completion criterion
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Steps. 72 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2805 tokens
    • 100Running it twice. No mutating operations
    • low 11 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
    • +4Description does not say when NOT to use the skill (false activations)
    • +2Single-language instructions
    • +3Description length 225: enough signal without eating the budget
    • +4Structure: 21 headings
    • +3Step-by-step instructions: 72 items
    • +3Output format is stated explicitly
    • +4Has examples (3 code blocks)
    • +4Reference files are cited in the instructions (3 of 3)
    • +3All 3 scripts are documented
    • +1License stated

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