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car-buying-assistant购车助理

Agent Skill

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。

总安装

3,672

周安装

150

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下载量

1,176
OpenClaw

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

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

简介

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。

SKILL.md

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.

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

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能力 3

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能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

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按下载量换算1,076

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