SKILLEMALL.ai

AB amazon-serp-analysis

Analyze Amazon market tracks/niches for go/no-go investment decisions. Given a seed keyword, scrapes SERP, expands related keywords to map the full track landscape, scores competition intensity, estimates demand, and delivers a structured recommendation with entry strategy. Trigger on: 赛道分析, 赛道研究, 值不值得做, 市场分析, 竞争分析, niche analysis, track analysis, 亚马逊赛道, 切入点, 要不要做这个品, 这个类目怎么样.

ClawHub Agent Skills author: Handa v1.0.0 MIT-0 2 files body ≈ 1 867 tokens Open the sourceclawhub.ai analyzed 27 h ago

Analyze Amazon market tracks/niches for go/no-go investment decisions.

As a process B 78/100 · Nearly there — weak spots: progress reporting

AnalyzerData and analyticsAI and agentsMarketingtype and topics are labelled automatically from the skill text
JSON
Technical rating
A
94/100
safety, quality, tests
Safety 60%
100
Quality 40%
85
Run on models
none yet
Process rating
B
78/100
Nearly there
Progress reporting w 2
0
Tools and files w 18
60
Result and completion w 14
60
the three weakest of ten parameters · all ten

How to improve

    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

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 78/100

    • 0Progress reporting. Says nothing while it works
    • 60Tools and files. Uses tools (web) that frontmatter does not declare
    • 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
    • 100Steps. 48 steps
    • 100Failures and branches. 3 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1867 tokens
    • 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)
    • -223 emoji in the instructions: noise for the model
    • +1No license
    • +2Single-language instructions
    • +3Description length 379: enough signal without eating the budget
    • +4Structure: 21 headings
    • +3Step-by-step instructions: 48 items
    • +3Output format is stated explicitly
    • +4Has examples (9 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 85.

    External checks

    ClawHub: clean
    This skill is a disclosed Amazon market-analysis workflow that may call scraping and analytics tools, with no hidden persistence or destructive behavior found.
    LLM: benign (high) · VirusTotal: · 29 May 2026