SKILLEMALL.ai

AA reviewlens

Review intelligence skill for mainland China shopping that compresses large volumes of marketplace reviews into a decision card, surfaces repeated praise and complaint patterns, distinguishes concentrated product flaws from seller or logistics noise, identifies who the product fits and who is likely to regret it, and tells the user whether the route is cheap with flaws or pricier but steadier. Use when the user says things like "别看评分,告诉我大家到底在骂什么", "差评是不是集中在同一个问题", "这东西适合谁", "谁最容易踩坑", or "便宜版能买吗还是贵一点更稳".

ClawHub Agent Skills author: haidong v1.0.0 MIT-0 9 files · 1 script body ≈ 2 490 tokens Open the sourceclawhub.ai analyzed 4 d ago

As a process A 80/100 · Runs to the end — weak spots: running it twice

AnalyzerCustomer supporttype and topics are labelled automatically from the skill text
JSON
Technical rating
A
96/100
safety, quality, tests
Safety 60%
100
Quality 40%
89
Run on models
none yet
Process rating
A
80/100
Runs to the end
Running it twice w 4
30
Failures and branches w 10
55
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: 9. 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 80/100

    • 30Running it twice. 3 mutating operations with no state check
    • 55Failures and branches. 1 branches
    • 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
    • 100Tools and files. No external tools needed
    • 100Steps. 152 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2490 tokens
    • 100Progress reporting. Reports progress
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
    • low 14 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)
    • +4No input/output examples
    • -31 of 1 scripts are never mentioned in SKILL.md
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 5 example trigger phrases
    • +3Description length 508: enough signal without eating the budget
    • +4Structure: 25 headings
    • +3Step-by-step instructions: 152 items
    • +3Output format is stated explicitly
    • +4Reference files are cited in the instructions (4 of 4)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 89.

    External checks

    ClawHub: clean
    The skill's requests, files, and runtime instructions align with its stated purpose (compressing and analyzing product reviews) and it does not ask for credentials, unusual binaries, or perform hidden installs.
    LLM: benign (high) · VirusTotal: benign · 4 Apr 2026