SKILLEMALL.ai

AA affiliate-marketer-coach

End-to-end affiliate marketer coach (content sites, niche review sites, comparison sites, deal sites, coupon sites, YouTube + TikTok affiliate, newsletter affiliate, paid-traffic affiliate). Use when an affiliate asks for niche/site selection, keyword research with intent classification, content cluster + topical-authority strategy, EEAT/Helpful-Content-Update defense, programmatic SEO, monetization stack (Amazon Associates, B2B SaaS, financial, hosting, networks like Impact / ShareASale / CJ / PartnerStack / Skimlinks), conversion optimization (review schema, comparison tables, stack vs single-link), pivoting after Google updates, paid-traffic affiliate (Google Ads, Meta), or selling/exiting the site. Triggers on phrases like "affiliate marketing", "Amazon Associates", "Impact Radius", "ShareASale", "PartnerStack", "niche site", "review site", "comparison site", "EEAT", "Helpful Content Update", "topical authority", "programmatic SEO", "site flipping", "Empire Flippers", "FE International", "InvestorsClub".

ClawHub Agent Skills author: charlie-morrison v1.0.0 MIT-0 2 files body ≈ 6 219 tokens Open the sourceclawhub.ai analyzed 3 d ago

As a process A 81/100 · Runs to the end — weak spots: progress reporting

AnalyzerYouTubeMarketingtype and topics are labelled automatically from the skill text
JSON
Technical rating
A
92/100
safety, quality, tests
Safety 60%
100
Quality 40%
79
Run on models
none yet
Process rating
A
81/100
Runs to the end
Progress reporting w 2
0
Result and completion w 14
60
When it triggers w 12
70
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: 2. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning body-long SKILL.md body ≈ 6219 tokens (recommended < 5000); move details to references/

Process rating: all ten parameters 81/100

  • 0Progress reporting. Says nothing while it works
  • 60Result and completion. Output format stated, no completion criterion
  • 70When it triggers. States when to use, but not when not to
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 70Execution cost. Instruction body is 6219 tokens
  • 85Steps. 233 steps, 1 vague phrases
  • 100Tools and files. No external tools needed
  • 100Failures and branches. 4 branches, has a failure section
  • 100Consistency. Name and required fields are in place
  • 100Running it twice. Mutating operations check current state
  • low 15 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

  • +4Description does not say when NOT to use the skill (false activations)
  • +3Description length 1023: 120–800 characters recommended
  • +1No license
  • +2Single-language instructions
  • +5Description quotes 15 example trigger phrases
  • +4Structure: 61 headings
  • +3Step-by-step instructions: 233 items
  • +3Output format is stated explicitly
  • +4Has examples (1 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 79.

External checks

ClawHub: clean
The visible skill artifacts are coherent and disclose user-directed development or moderation workflows without hidden data collection or unsafe automatic execution.
LLM: benign (medium) · VirusTotal: · 29 May 2026