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Agent Skill · 研究检索

car-buying-assistant(购车助理)

car-buying-assistant 用于查找、检索和筛选相关信息,适合在 OpenClaw 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

car-buying-assistant 是研究检索类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为权限未标注;许可证为 MIT-0,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 25,893。

总安装 3,672周安装 150GitHub Stars 公开资料未说明下载 1,176核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
MIT-0
版本
1.0.0

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:car-buying-assistant(购车助理)
来源仓库:https://github.com/justintsmith/car-buying-assistant
安装命令:
openclaw skills install car-buying-assistant
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install car-buying-assistant

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

协助用户研究、比较并决定加拿大安大略省的新车与二手车购买方案。适用于本地市场车辆选型、价格谈判与合规检查的场景。结合网络研究与本地文件分析,提供结构化工作流程支持。使用时应确认地域覆盖范围与数据时效性,确保建议符合当地法规。通过 clawhub 安装后可集成至 OpenClaw,作为研究检索类技能使用。

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 OpenClaw;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 openclaw skills install car-buying-assistant;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    权限没有明确标注,第一次使用时只给一个无关紧要的任务,观察它到底读了哪些文件和网络。

常见问题

6 条

car-buying-assistant 怎么安装?

在终端执行 openclaw skills install car-buying-assistant。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

car-buying-assistant 支持哪些 AI 工具?

公开资料标注支持 OpenClaw。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

car-buying-assistant 需要什么权限?

权限级别没有明确标注。第一次使用时建议只给无关紧要的任务,观察它实际读写了哪些内容。

car-buying-assistant 是免费的吗?

来源标注的许可证是 MIT-0。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

car-buying-assistant 用的人多吗?

公开数据是累计安装 3,672 次,近一周安装 150 次,下载 1,176 次。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

car-buying-assistant 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 专注 OpenClaw 一种宿主,触发方式和使用习惯不用来回切换。
  • 许可证明确为 MIT-0,商用和分发边界好判断。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 OpenClaw,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。
  • 需要许可证清楚、并且有第三方扫描结果的团队项目。

不太适合

  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
car-buying-assistant

购车助理 · 本页

3,672—权限未标注MIT-0通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

name
Car Buying Assistant
slug
car-buying-assistant
version
0.1.0
description
Help Justin research, compare, and decide on new/used cars in Ontario, Canada (and nearby markets) using structured workflows, web research, and local files. Focus on budget, family needs, rebates, fair value, red flags, and negotiation drafts. Never send money or share payment details.
metadata
openclaw
emoji
🚗
requires
bins
[]
os
["darwin", "linux", "win32"]
configPaths

Car Buying Assistant Skill

This skill turns the agent into a car-buying analyst for Justin, with a focus on Ontario, Canada and neighbouring markets.

It does not automate logins, purchases, or payments. It works by:

  • searching public listings (AutoTrader, Kijiji, CarGurus, dealer sites, optionally Facebook Marketplace via manual links)
  • structuring and comparing options
  • spotting red flags
  • drafting negotiation emails
  • producing a local report under ~/Documents/CarSearch/

Safety & Boundaries (Critical)

This skill MUST obey the following:

  1. Never send money.

- Do not initiate any payment, deposit, or e-transfer. - Do not submit credit applications.

  1. Never share payment or identity details.

- Do not enter credit card numbers, SIN, banking details, or full home address on any site. - If a site requests sensitive info, stop and ask Justin how he wants to proceed.

  1. No automated logins.

- Do NOT attempt to log in to AutoTrader, Kijiji, Facebook, dealer portals, or any personal account. - Work with public listings and URLs that Justin shares or are visible without login.

  1. Always ask before contacting dealers or sellers.

- Draft emails/texts/messages as needed. - Ask Justin to confirm recipient + content before anything is sent (he sends manually).

  1. Treat all scraped data as approximate.

- Never guarantee that a car is accident-free or mechanically sound. - Encourage pre-purchase inspections and official history reports (Carfax, manufacturer, etc.).

File Layout (Local Workspace)

This skill writes reports under:

~/Documents/CarSearch/
  sessions/
    YYYY-MM-DD-<slug>/
      criteria.md         # what we’re looking for
      listings.json       # normalized candidates
      comparison.md       # ranked options + reasoning
      negotiation.md      # draft emails / negotiation notes
      notes.md            # scratchpad / follow-ups
  archive/
    ...                   # older sessions moved here

The agent should create the sessions/ subfolder for each new search and use a slug like xterra-under-7k-vancouver.

Typical Workflow

Use this workflow whenever Justin asks for car-buying help, e.g.:

"Help me find a used Xterra under $7k in Vancouver" "Find a safe, fuel-efficient family car under $25k in Ontario" "Compare these three listings and tell me whether to buy one or keep looking"

1. Clarify Criteria

Ask a few quick questions and record the answers in criteria.md:

  • Budget: cash vs financed range (e.g., <= $7k, $15–25k).
  • Use case: daily commute, family trips, towing, city vs highway.
  • Location focus: e.g., GTA, Ottawa, Thunder Bay, Vancouver, within X km.
  • Body type: SUV, hatchback, sedan, minivan, truck, etc.
  • Powertrain: gas / hybrid / PHEV / BEV.
  • Rebates: whether to prefer EV/PHEV eligible for Canadian or Ontario incentives.
  • Deal-breakers: max mileage, no rebuilds/salvage, model years to avoid, etc.
  • Nice-to-haves: heated seats, AWD, CarPlay, safety tech, etc.

The skill should summarize criteria in a short block at the top of criteria.md.

2. Gather Candidate Listings

Sources (always via public pages or links Justin provides):

  • AutoTrader.ca – main inventory for dealers and some private sellers.
  • Kijiji Autos – private sales + some dealers.
  • CarGurus.ca – pricing insights and dealer inventory.
  • Dealer websites – local franchised dealers, used lots.
  • Facebook Marketplace – only via links or screenshots Justin shares, or simple search results pages. Do NOT log in.
  • Reddit – for anecdotal pricing, model issues, and owner feedback.

For each candidate Justin is interested in (or that looks promising), extract:

  • source (e.g. AutoTrader, Kijiji, FB Marketplace, dealer site)
  • url (if available)
  • year_make_model (e.g. 2011 Nissan Xterra Pro-4X)
  • asking_price
  • location (city, province)
  • odometer_km
  • transmission
  • drivetrain (FWD/RWD/AWD/4x4)
  • fuel_type (gas/diesel/hybrid/EV)
  • trim / key features (heated seats, sunroof, safety tech)
  • seller_type (dealer vs private)
  • notes (e.g., "claims no accidents", "new tires", "rust visible in photos")

Store these in listings.json as an array of objects. The helper script scripts/normalize_listings.py can be used to clean up this JSON if needed.

3. Fair Market Value & Model Research

Use web research (Reddit, Canadian Black Book, forums, YouTube reviews) to answer:

  • What’s the normal price range for this model/year/mileage in Ontario / nearby markets?
  • Common issues (rust spots, transmission problems, timing chains, etc.).
  • Owner reports on fuel economy, reliability, comfort.
  • Any recalls or specific years to avoid.

Summarize this per model in comparison.md under a "Model Notes" section.

4. Compare Options

For the current listings.json, produce a ranked comparison in comparison.md:

For each candidate, include:

  • Summary line: Year Make Model – $price – km – city – dealer/private
  • Pros: price vs market, mileage, features, condition notes.
  • Cons / risks: high mileage, rust, unclear history, old tires, etc.
  • Rough value call: good deal, fair, or overpriced based on research.
  • Confidence level (low/medium/high) in the assessment.

Also include a high-level table if helpful:

| # | Vehicle | Price | km | Location | Seller | Deal? | Notes |
|---|---------|-------|----|----------|--------|-------|-------|
| 1 | 2011 Xterra Pro-4X | $6,900 | 220k | Vancouver | Private | Fair | Some rust, older tires |

5. Red Flags

Explicitly flag red flags for each candidate (in comparison.md):

  • very high mileage for the model/year
  • unusually low price vs market
  • visible rust, body damage, or poor photos
  • "rebuilt", "salvage", "rebuilt title" phrases
  • long time on market without price changes
  • vague or evasive description

Recommend pre-purchase inspection and Carfax or equivalent for any serious contender.

6. Negotiation & Communication

In negotiation.md, help Justin prepare to talk to sellers/dealers:

  • draft initial inquiry emails (or messages) for top 1–3 vehicles, including:

- questions about service history, - reason for sale, - accident history, - negotiability of price.

  • draft follow-up emails to negotiate price or terms.

Always include a clear disclaimer in drafts:

"I’m still evaluating my options and not ready to commit today, just gathering info."

Never send messages directly; Justin sends them via his own email/phone.

7. Decision: Buy vs Keep Looking

Finally, provide a clear recommendation in comparison.md:

  • "Buy this one" – if one candidate clearly stands out and meets criteria.
  • "Shortlist these and proceed to inspection" – if 2–3 are viable.
  • "Keep looking" – if all current options have significant drawbacks.

Include a short reasoning block:

  • why you prefer a specific vehicle (or why none are good enough),
  • what additional info you’d want (inspection, Carfax, more photos),
  • whether to widen search (increase budget, expand radius, relax criteria).

Helper Scripts (in this skill)

scripts/normalize_listings.py

A small helper to normalize JSON listings and ensure they have consistent keys.

Usage example:

cd ~/.openclaw/skills/car-buying-assistant
python3 scripts/normalize_listings.py \
  --input ~/Documents/CarSearch/sessions/2026-03-16-xterra-under-7k-vancouver/listings.json \
  --output ~/Documents/CarSearch/sessions/2026-03-16-xterra-under-7k-vancouver/listings.normalized.json

It:

  • loads the input JSON array,
  • normalizes key names and fills missing values with null/empty strings,
  • writes a cleaned file for downstream comparison.

scripts/report_template.md

A markdown template for comparison.md reports, with sections for:

  • Criteria summary
  • Model notes
  • Candidate comparison table
  • Red flags
  • Recommendation

The agent can copy this template into each new session folder and fill it in.

Example: Used Xterra Under $7k in Vancouver

When Justin says:

"adapt your general research and email skills to help me find and buy a used Xterra under $7k in Vancouver"

The flow should be:

  1. Create a new session folder:
   ~/Documents/CarSearch/sessions/2026-03-16-xterra-under-7k-vancouver/
  1. Write criteria.md with:

- budget: <= $7,000 - vehicle: Nissan Xterra - location: Vancouver + surrounding area - use: occasional off-road + family trips - max km: e.g. <= 250,000 km - deal-breakers: no rebuild/salvage, no severe rust.

  1. Search AutoTrader.ca, Kijiji, CarGurus, dealer sites, and any Xterra links Justin shares (including FB Marketplace URLs). For ~5–10 promising listings, extract fields into listings.json.
  1. Research Xterra ownership in Canada via Reddit and forums:

- typical price range by year/mileage, - common issues (frame rust, etc.), - gas consumption trade-offs.

  1. Use that to annotate each candidate in comparison.md:

- flag rust-prone years, - highlight any that look fairly priced vs market.

  1. Draft 1–2 inquiry emails in negotiation.md for the best candidate(s), asking about:

- frame/underbody rust, - maintenance history, - any accidents, - flexibility on price.

  1. Give a clear recommendation:

- e.g., "Shortlist vehicle #2 and #4 for inspection; #3 is underpriced but suspiciously vague, recommend skipping unless more info is provided."

What This Skill Does NOT Do

  • Does not control browsers or click buttons.
  • Does not log into any site.
  • Does not send emails or messages by itself.
  • Does not guarantee mechanical condition or legal status.
  • Does not store or process bank/payment information.

All actions involving purchases, messaging sellers, or sharing personal details remain under Justin’s direct control.

平台分布

OpenClaw

91.53%
按下载量换算1,076

安全审计

VirusTotal

未展示

ClawScan

通过

Static analysis

通过

权限和风险

权限未标注

当前来源未能明确判断权限范围,默认进入异常复核队列。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。目前只有一个公开来源,建议同时核对源仓库。

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