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AB chief-ai-officer-advisor

AI leadership advisor on AI strategy, governance, risk, investment, and org design. Use when defining an AI strategy, building an AI governance program, scoring AI maturity, or drafting an AI risk register.

borghei/Claude-Skills Agent Skills author: borghei 7 files · 3 scripts body ≈ 2 866 tokens Open the sourcegithub.com↗ analyzed 8 h ago

AI leadership advisor on AI strategy, governance, risk, investment, and org design.

As a process B 74/100 · Nearly there — weak spots: running it twice, progress reporting

ProcedureInfrastructureData 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
96/100
safety, quality, tests
Safety 60%
96
Quality 40%
95
Run on models
none yet
Process rating
B
74/100
Nearly there
Progress reporting w 2
0
Running it twice w 4
30
When it triggers w 12
50
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 · 4

    ✓ No critical or high findings

    Medium and low: 4
    • low Risky intent intent-offensive-security references/ai-org-and-talent.md:73
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      - **Red team lead**
    • low Risky intent intent-offensive-security references/ai-org-and-talent.md:113
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      ### AI Red Team Lead
    • low Risky intent intent-offensive-security references/ai-org-and-talent.md:238
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      - **Penetration testing and red-teaming** (engaged quarterly)
    • low Risky intent intent-offensive-security references/ai-risk-and-governance.md:192
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      Plus working groups as needed: red team, AI policy, vendor management.

    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 74/100

    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 11 mutating operations with no state check
    • 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
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Steps. 78 steps
    • 100Result and completion. Output format and completion criterion are stated
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2866 tokens
    • 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 206: enough signal without eating the budget
    • +4Structure: 21 headings
    • +3Step-by-step instructions: 78 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.