SKILLEMALL.ai

CD Financial Industry Intelligent Marketing Assistant

AI-powered campaign design and compliance review assistant for bank and securities marketing teams — drafts customer segmentation, campaign plans, compliant copy, A/B tests and ROI analysis, all as pre-publication drafts requiring compliance sign-off. Scope: regulated financial-marketing planning only; not general advertising, not consumer goods marketing. Keywords: compliant campaign drafting, financial customer segmentation, prohibited-claims check, A/B test design, campaign ROI attribution, bank marketing, 金融营销活动设计, 银行客户分群, 违禁词校验, 金融物料合规改写, 营销ROI归因, 触达频控.

ClawHub Agent Skills author: lingfeng-19 v3.0.4 MIT-0 2 files body ≈ 5 070 tokens Open the sourceclawhub.ai↗ analyzed 7 d ago

AI-powered campaign design and compliance review assistant for bank and securities marketing teams — drafts customer segmentation, campaign plans, compliant…

As a process D 47/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

AnalyzerMarketingtype and topics are labelled automatically from the skill text
JSON
Technical rating
C
78/100
safety, quality, tests
Safety 60%
100
Quality 40%
46
Run on models
none yet
Process rating
D
47/100
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 · 0

✓ No critical or high findings

Files scanned: 2. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning frontmatter-yaml SKILL.md: the frontmatter is not valid YAML (YAML parse error: Nested mappings are not allowed in compact mappings at line 3, column 14: description: AI-powered campaign design and compliance review assistant for ban… ^ ); fields were read line by line. The usual cause is a colon inside an unquoted value
  • 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 ≈ 5070 tokens (recommended < 5000); move details to references/
  • note frontmatter-key unknown frontmatter key "slug"

Process rating: all ten parameters 47/100

  • 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
  • 40Consistency. Frontmatter name (Financial Industry Intelligent Marketing Assistant) differs from the folder (security-intelligent-marketing)
  • 70Execution cost. Instruction body is 5070 tokens
  • 100Tools and files. No external tools needed
  • 100Steps. 22 steps
  • 100Running it twice. No mutating operations

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
  • +1No license
  • +2Single-language instructions
  • +3Description length 564: enough signal without eating the budget
  • +4Structure: 13 headings
  • +3Step-by-step instructions: 22 items
  • +4Has examples (4 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 46.

External checks

ClawHub: clean
This skill is a financial-marketing drafting aid with clear no-execution and no-customer-data boundaries, though users must avoid pasting real customer records into it.
LLM: benign (high) · VirusTotal: · 8 Oct 2026