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AB product-expert-review-enhanced

Generate deep product experience reviews for AI tools, agent products, SaaS products, or consumer-facing software based on a website URL, screenshots, or both. Use when the user asks to evaluate a product, write a product review, analyze UX, assess first-time user experience, compare with competitors, or produce a structured experience report and optionally upload it to a Feishu doc.

ClawHub Agent Skills author: Jelly6661 v1.0.0 MIT-0 2 files body ≈ 1 889 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process B 70/100 · Nearly there — weak spots: inputs and preconditions, consistency, running it twice

AnalyzerData and analyticsInfrastructureAI and agentstype and topics are labelled automatically from the skill text
Runs in: OpenClaw Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) Goose OpenCode
JSON
Technical rating
A
93/100
safety, quality, tests
Safety 60%
100
Quality 40%
83
Run on models
none yet
Process rating
B
70/100
Nearly there
Inputs and preconditions w 11
0
Running it twice w 4
30
Consistency w 8
40
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 70/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 30Running it twice. 4 mutating operations with no state check
    • 40Consistency. Frontmatter name (product-expert-review-enhanced) differs from the folder (product-expert-review)
    • 60Result and completion. Output format stated, no completion criterion
    • 70When it triggers. States when to use, but not when not to
    • 85Steps. 97 steps, 2 vague phrases
    • 100Tools and files. No external tools needed
    • 100Failures and branches. 17 branches, has a failure section
    • 100Execution cost. Instruction body is 1889 tokens
    • 100Progress reporting. Reports progress
    • 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

    • +5Description has no quoted example phrases that should trigger the skill
    • +4Description does not say when NOT to use the skill (false activations)
    • +4No input/output examples
    • +1No license
    • +2Single-language instructions
    • +3Description length 386: enough signal without eating the budget
    • +4Structure: 28 headings
    • +3Step-by-step instructions: 97 items
    • +3Output format is stated explicitly

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 83.

    External checks

    ClawHub: clean
    This is an instruction-only product review skill whose browsing, screenshot analysis, competitor research, and optional Feishu sharing are disclosed and aligned with its purpose.
    LLM: benign (high) · VirusTotal: · 29 May 2026