SKILLEMALL.ai

AF product-review-panel

Convene a multi-expert panel to review a Product Requirements Document (PRD) and deliver a binding verdict (GO / NO-GO / CONDITIONAL GO) with dissenting opinions preserved as first-class output. Use when a Product Manager wants critical review of a written PRD, when deciding whether to build a specific feature, when stress-testing a proposal across product / UX / business-model dimensions, or for a structured "second opinion" before committing engineering resources. The panel adapts to the user's conversation language (Chinese → Cagan + 俞军 + 大厂 P9 产品总监 + situational like 张小龙; English/other → Cagan + Christensen + Senior PM Director + situational like Norman, Jobs, Hoffman, Torres). Every review ends with a verdict from "The Closer" (魔鬼裁判) plus observable failure signals to monitor. Do NOT use for pre-PRD idea brainstorming, purely technical architecture reviews, non-product strategy questions, or user research synthesis — use other skills for those.

ClawHub Agent Skills author: Sean Liu v1.0.1 MIT-0 18 files body ≈ 1 725 tokens Open the sourceclawhub.ai analyzed 2 d ago

Convene a multi-expert panel to review a Product Requirements Document (PRD) and deliver a binding verdict (GO / NO-GO / CONDITIONAL GO) with dissenting…

As a process F 39/100 · Will not run — References files that are not bundled: references/personas/experts-{cn|intl}.md

AnalyzerDesigntype 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
F
39/100
Will not run
References files that are not bundled: references/personas/experts-{cn|intl}.md
Tools and files w 18
0
Result and completion w 14
0
Failures and branches w 10
0
the three weakest of ten parameters · all ten

How to improve

  1. The text references files that are not there: add them or drop the references.
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: 17. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning missing-ref reference to a missing file: references/personas/experts-{cn|intl}.md

Process rating: all ten parameters 39/100

Will not run. References files that are not bundled: references/personas/experts-{cn|intl}.md
  • 0Tools and files. 1 referenced file(s) missing: references/personas/experts-{cn|intl}.md
  • 0Result and completion. Does not say what the result is
  • 0Failures and branches. Linear process with no failure handling
  • 30Running it twice. 2 mutating operations with no state check
  • 40Consistency. Frontmatter name (product-review-panel) differs from the folder (product-review-panel-skill)
  • 50When it triggers. No condition that starts the skill
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 85Steps. 51 steps, 2 vague phrases
  • 100Execution cost. Instruction body is 1725 tokens
  • 100Progress reporting. Reports progress

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

  • +3Description length 963: 120–800 characters recommended
  • +3Output format is not stated: the model decides each time
  • +2Single-language instructions
  • +5Description quotes 2 example trigger phrases
  • +4Description says when NOT to use the skill
  • +4Structure: 17 headings
  • +3Step-by-step instructions: 51 items
  • +4Has examples (0 code blocks)
  • +1License stated

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

External checks

ClawHub: clean
This skill is a disclosed PRD review workflow that reads user-provided product documents and does not install code, persist data, or take external actions on its own.
LLM: benign (high) · VirusTotal: · 31 Aug 2026