SKILLEMALL.ai

BB Growth

Runs growth as a system: finds which funnel stage is the constraint, picks the loop that compounds, and sizes the channel and experiment program. Use when growth stalled or a number has to be hit and nobody can say which stage is broken; when choosing, scaling, or killing acquisition channels; when CAC, payback, LTV:CAC, or blended-versus-paid has to be computed or defended; when signups grow but activation, retention, or paid conversion does not; when designing a referral program, a lifecycle messaging map, or a north-star metric and the events behind it; when forecasting from a model instead of a wish; and for marketplace liquidity, app-install funnels, ecommerce repeat purchase, and self-serve-versus-sales motion. Not for A/B test statistics (`ab-testing`), page-level conversion work (`cro`), churn cohort depth (`churn-analysis`), MRR and NRR definitions (`saas-metrics`), launch positioning (`go-to-market`), or the CGO role and growth-org leadership (`cgo`).

ClawHub Agent Skills author: Iván v1.0.2 MIT-0 21 files body ≈ 6 106 tokens Open the sourceclawhub.ai analyzed 3 d ago

As a process B 79/100 · Nearly there — weak spots: inputs and preconditions, running it twice, progress reporting

AnalyzerData and analyticsMarketingtype and topics are labelled automatically from the skill text
JSON
Technical rating
B
86/100
safety, quality, tests
Safety 60%
100
Quality 40%
66
Run on models
none yet
Process rating
B
79/100
Nearly there
Inputs and preconditions w 11
0
Progress reporting w 2
0
Running it twice w 4
30
the three weakest of ten parameters · all ten

How to improve

  1. 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: 21. 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 body-long SKILL.md body ≈ 6106 tokens (recommended < 5000); move details to references/
  • note frontmatter-key unknown frontmatter key "slug"
  • note frontmatter-key unknown frontmatter key "homepage"
  • note frontmatter-key unknown frontmatter key "changelog"

Process rating: all ten parameters 79/100

  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Progress reporting. Says nothing while it works
  • 30Running it twice. 13 mutating operations with no state check
  • 65Failures and branches. 3 branches
  • 70Execution cost. Instruction body is 6106 tokens
  • 100Tools and files. No external tools needed
  • 100Steps. 44 steps
  • 100Result and completion. Output format and completion criterion are stated
  • 100When it triggers. States when to use and when not to
  • 100Consistency. Name and required fields are in place
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • low 12 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
  • +3Description length 975: 120–800 characters recommended
  • +4No input/output examples
  • +1No license
  • +2Single-language instructions
  • +4Description says when NOT to use the skill
  • +4Structure: 12 headings
  • +3Step-by-step instructions: 44 items
  • +3Output format is stated explicitly

Quality base 70; lint remarks subtract, signals add up to 100. Result: 66.

External checks

ClawHub: suspicious
This growth-planning skill is coherent and not deceptive, but it can automatically modify local growth, budget, project, and contact records without asking first.
LLM: suspicious (high) · 27 Jul 2026