SKILLEMALL.ai

BD GCCEO

Global CEO Mastery System | 全球CEO帝王学技能体系 Beyond Excellence, Achieving Greatness | 超越优秀 成就伟大 Inspired by HKU Global CEO Programme 2026 & CEIBS Global CEO Programme Powered by Morgan Stanley Research × McKinsey Methodology × Top-Tier Investment Banking & Private Equity

ClawHub Agent Skills author: WANG DONG JIE v4.0.0 MIT-0 7 files · 1 script body ≈ 9 238 tokens Open the sourceclawhub.ai analyzed 4 d ago

As a process D 35/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureInfrastructureData and analyticsResearchtype and topics are labelled automatically from the skill text
JSON
Technical rating
B
80/100
safety, quality, tests
Safety 60%
98
Quality 40%
53
Run on models
none yet
Process rating
D
35/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
the three weakest of ten parameters · all ten

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
  2. The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
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 Risky intent intent-offensive-security SKILL.md:128
    Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
    - 季度网络攻防演练(Red Team/Blue Team),年度CISO向董事会汇报
  • low Risky intent intent-offensive-security SKILL.md:198
    Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
    - 每半年举行一次"红队演练"(Red Team Exercise),测试组织应变能力

Files scanned: 7. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning name-format name should be kebab-case (lowercase letters, digits, hyphens)
  • warning description-no-when description does not say WHEN to use the skill (no "use when")
  • warning body-long SKILL.md body ≈ 9238 tokens (recommended < 5000); move details to references/
  • note frontmatter-key unknown frontmatter key "creator"
  • note frontmatter-key unknown frontmatter key "date"
  • note frontmatter-key unknown frontmatter key "language"
  • note frontmatter-key unknown frontmatter key "skills_count"
  • note frontmatter-key unknown frontmatter key "repository"
  • note frontmatter-key unknown frontmatter key "clawhub"
  • note frontmatter-key unknown frontmatter key "skillhub"

Process rating: all ten parameters 35/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Failures and branches. Linear process with no failure handling
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 3 mutating operations with no state check
  • 40Consistency. Frontmatter name (GCCEO) differs from the folder (gceo-global-ceo-skill-system)
  • 40Execution cost. Instruction body is 9238 tokens: crowds the task out of the window
  • 60Tools and files. Uses tools (bash) that frontmatter does not declare
  • 100Steps. 65 steps
  • 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)
  • +3Output format is not stated: the model decides each time
  • -2460 emoji in the instructions: noise for the model
  • +2Single-language instructions
  • +3Description length 267: enough signal without eating the budget
  • +4Structure: 46 headings
  • +3Step-by-step instructions: 65 items
  • +4Has examples (12 code blocks)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 53.

External checks

ClawHub: suspicious
The skill is mostly static CEO-training material, but it bundles GitHub publishing steps that can use a user's credentials to create public content.
LLM: suspicious (high) · VirusTotal: · 29 May 2026