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Review Buying Advisor

作者 haidong · GitHub ↗ · v0.1.0 · MIT-0
cross-platform ✓ 安全检测通过
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在 OpenClaw 中安装
/install review-buying-advisor
功能描述
Analyze a product from a product name plus platform by locating the intended listing, reading public user reviews when accessible, and turning the evidence i...
使用说明 (SKILL.md)

Review Buying Advisor

Turn platform review evidence into a practical buying recommendation.

Use this skill when the input is a product name plus platform and the user wants help deciding whether to buy.

This skill operates in two modes:

  • Mode 1: Review-accessible mode — the platform exposes enough public review content to support analysis.
  • Mode 2: Review-limited mode — the platform blocks, weakens, or hides review content, so only a limited or inconclusive answer is possible.

Read these references as needed:

  • references/product-identification.md when the product or variant is unclear
  • references/review-sampling.md before collecting review evidence
  • references/review-signals.md when judging strengths, risks, and buyer fit
  • references/platform-notes.md when platform review quality may affect interpretation
  • references/category-playbooks.md when category-specific priorities matter
  • references/failure-modes.md when evidence is sparse, mixed, or inaccessible
  • references/output-patterns.md when preparing the final answer
  • references/examples.md when examples would help calibrate tone or structure

Workflow

  1. Identify the product.

    • Accept a product name plus platform.
    • Match the likely listing by brand, model, category, and variant cues.
    • If the product is ambiguous, ask one short clarifying question.
    • Do not guess across multiple plausible products or variants.
  2. Check review accessibility.

    • Try to access public review content on the specified platform.
    • Decide which mode applies:
      • Mode 1 if enough public review content is accessible.
      • Mode 2 if review content is blocked, too weak, or too incomplete.
  3. If Mode 1, analyze review evidence.

    • Use a representative sample rather than only the first visible comments.
    • Include positive, negative, mixed, and recent reviews when possible.
    • Prefer specific reviews over generic praise or blame.
    • Downweight generic praise, generic criticism, shipping-only comments, and suspiciously promotional wording.
    • Group signals into useful themes.
    • Separate repeated issues from isolated complaints.
    • Weigh severity as well as frequency.
  4. If Mode 2, do not fake completion.

    • State that public review evidence is not sufficiently accessible.
    • Explain the limitation briefly.
    • Give only a limited conclusion when still useful.
    • Do not pretend to have validated real review sentiment.
  5. Give the final answer. Cover:

    • verdict
    • what evidence was actually available
    • buyer fit when supportable
    • main positives and risks when supportable
    • what to verify before buying
    • confidence

Output

Use this structure unless the user asks for something else.

Mode 1 output

Use when public review evidence is available.

Overall Verdict

Choose one:

  • Recommend
  • Recommend with caveats
  • Depends on use case
  • Not recommended
  • Not enough evidence for a strong recommendation

Why

2-4 bullets with the strongest evidence.

Best For

Who is most likely to be satisfied.

Main Positives

Most credible repeated strengths.

Main Risks

Most important risks, including repeated issues or severe but less frequent ones.

Watch Before Buying

What the buyer should verify before ordering.

Final Advice

A direct recommendation in plain language.

Confidence

High / Medium / Low, with a brief reason.

Mode 2 output

Use when public review evidence is not sufficiently accessible.

Overall Verdict

Usually:

  • Not enough evidence for a strong recommendation
  • Depends on use case

Why

Explain what was and was not accessible.

Current Limitation

State whether the issue is platform blocking, weak public text, ambiguous product matching, or incomplete review visibility.

Watch Before Buying

State what remains unverified.

Final Advice

Give a cautious conclusion without pretending the reviews were validated.

Confidence

Usually Low.

Quality bar

Do:

  • lead with the verdict
  • use representative evidence
  • explain trade-offs clearly
  • say who the product is for
  • lower confidence when evidence is weak

Do not:

  • summarize without recommending
  • rely on only one review slice
  • overreact to one dramatic review
  • overstate certainty
  • invent facts not supported by visible evidence

Limitation handling

If the product cannot be identified confidently:

  • ask one short clarifying question

If the platform does not expose enough public review content:

  • switch to Mode 2
  • state the limitation briefly
  • keep confidence low

If public review evidence is sparse or mixed but still partially usable:

  • stay in Mode 1 only if a limited review-based judgment is still supportable
  • lower confidence
  • narrow the claim

If reviews may mix multiple variants:

  • mention that risk
  • avoid overly specific claims unless the variant is clear
安全使用建议
This skill appears coherent and low-risk: it only describes how to find and summarize publicly visible product reviews. Before installing, consider: (1) confirm you are comfortable with the agent visiting public product pages (the agent may need browsing capability); (2) the skill relies on public review text—platforms that require login or block scraping will yield limited results (the skill accounts for this via Mode 2); (3) check platform Terms of Service if you are concerned about automated scraping; and (4) because the skill can be invoked autonomously by default, limit or review its use policies if you do not want unattended browsing or web requests. If you want additional assurance, ask the publisher for provenance (homepage or source) or request explicit rate/endpoint controls.
功能分析
Type: OpenClaw Skill Name: review-buying-advisor Version: 0.1.0 The 'review-buying-advisor' skill is a well-structured set of instructions designed to help an AI agent analyze e-commerce product reviews and provide buying recommendations. The logic focuses on evidence-based reasoning, distinguishing between high-quality and low-quality reviews, and handling platform limitations (e.g., anti-bot measures) gracefully without fabricating data. There are no indicators of data exfiltration, malicious execution, or prompt injection attacks within the SKILL.md or the extensive reference documentation.
能力评估
Purpose & Capability
The name and description (reading platform reviews to produce buying advice) align with the SKILL.md workflow: identifying a product, sampling reviews, weighing signals, and producing a verdict. There are no unrelated credential requests, binaries, or install steps that would be disproportionate for this purpose.
Instruction Scope
The runtime instructions focus on identifying listings and reading publicly available review text, sampling positive/negative/mixed reviews, and producing a structured recommendation. The skill explicitly handles cases where review text is inaccessible (Mode 2) and instructs not to fake review access. It does not instruct the agent to read local files, request secrets, or send data to external endpoints beyond visiting the target platforms.
Install Mechanism
This is an instruction-only skill with no install spec and no code files, so nothing is written to disk or fetched at install time. That minimizes install-time risk.
Credentials
The skill declares no required environment variables, credentials, or config paths. Its behavior (scraping/reading public reviews) does not require additional secrets or unrelated service keys.
Persistence & Privilege
always is false and the skill is user-invocable; it does not request persistent system-level privileges or modify other skills. Autonomous invocation is allowed by platform default but not combined with other red flags.
如何使用
  1. 确保已安装 OpenClaw(本地或 Docker 部署)
  2. 在对话框中输入安装命令:/install review-buying-advisor
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /review-buying-advisor 触发
  4. 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
v0.1.0
Initial release: dual-mode product review buying advisor with platform-aware review analysis and review-limited fallback.
元数据
Slug review-buying-advisor
版本 0.1.0
许可证 MIT-0
累计安装 0
当前安装数 0
历史版本数 1
常见问题

Review Buying Advisor 是什么?

Analyze a product from a product name plus platform by locating the intended listing, reading public user reviews when accessible, and turning the evidence i... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 212 次。

如何安装 Review Buying Advisor?

在 OpenClaw 或 Claude Code 对话框中运行命令「/install review-buying-advisor」即可一键安装,无需额外配置。

Review Buying Advisor 是免费的吗?

是的,Review Buying Advisor 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。

Review Buying Advisor 支持哪些平台?

Review Buying Advisor 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。

谁开发了 Review Buying Advisor?

由 haidong(@harrylabsj)开发并维护,当前版本 v0.1.0。

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