SKILLEMALL.ai

CB invassistant

Multi-asset investment portfolio management framework with A/B/C asset-class differentiated rules, 7 red-line portfolio risk controls, and 4-factor QMS quality scoring. Covers US, A-share (China), and HK stocks with disciplined entry/exit logic. 中文摘要:多资产投资组合管理框架——A/B/C 类资产差异化规则、7 条红线组合风险控制、四因 子质量管理,覆盖美股/A股/港股的纪律化进出场逻辑。触发词:投资组合管理、持仓复盘、 风险红线检查、仓位规则.

ClawHub Agent Skills author: haiyangchen v2.3.14 MIT-0 19 files body ≈ 7 126 tokens Open the sourceclawhub.ai analyzed 21 h ago

Multi-asset investment portfolio management framework with A/B/C asset-class differentiated rules, 7 red-line portfolio risk controls, and 4-factor QMS…

As a process B 71/100 · Nearly there — weak spots: inputs and preconditions

AnalyzerQuality controlFinanceData and analyticstype and topics are labelled automatically from the skill text
JSON
Technical rating
C
73/100
safety, quality, tests
Safety 60%
100
Quality 40%
33
Run on models
none yet
Process rating
B
71/100
Nearly there
Inputs and preconditions w 11
0
When it triggers w 12
50
Failures and branches w 10
50
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 · 0

✓ No critical or high findings

Files scanned: 19. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • error name-missing SKILL.md: frontmatter has no `name`
  • warning description-no-when description does not say WHEN to use the skill (no "use when")
  • warning body-long SKILL.md body ≈ 7126 tokens (recommended < 5000); move details to references/
  • note frontmatter-key unknown frontmatter key "slug"
  • note frontmatter-key unknown frontmatter key "displayName"
  • note frontmatter-key unknown frontmatter key "description_zh"
  • note frontmatter-key unknown frontmatter key "read_when"
  • note frontmatter-key unknown frontmatter key "not_for"
  • note frontmatter-key unknown frontmatter key "disable"
  • note edit-residue the text marks something as outdated (lines 401): check that old rules are not kept next to new ones — the full check reads the text for contradictions

Process rating: all ten parameters 71/100

  • 0Inputs and preconditions. Does not say what the process needs to start
  • 50When it triggers. No condition that starts the skill
  • 50Failures and branches. 0 branches, has a failure section
  • 60Result and completion. Output format stated, no completion criterion
  • 70Execution cost. Instruction body is 7126 tokens
  • 100Tools and files. Tools declared in frontmatter
  • 100Steps. 38 steps
  • 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 15 top-level sections: this looks like several domains in one skill
  • high The skill tells the model to perform an irreversible action with no human approval

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)
  • -38 of 9 scripts are never mentioned in SKILL.md
  • +1No license
  • +2Single-language instructions
  • +3Description length 349: enough signal without eating the budget
  • +4Structure: 29 headings
  • +3Step-by-step instructions: 38 items
  • +3Output format is stated explicitly
  • +4Has examples (1 code blocks)
  • +4Reference files are cited in the instructions (4 of 4)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 33.

External checks

ClawHub: suspicious
This investment assistant is mostly purpose-aligned, but it needs Review because it asks to persist portfolio rules into agent memory/automation prompts and can send sensitive portfolio reports to arbitrary webhooks.
LLM: suspicious (high) · VirusTotal: · 11 Sept 2026