ClawHub

City Rental Hunt

Other

Search and triage rental listings from Chinese social platforms, especially Xiaohongshu via TikHub and optionally Douyin, for apartment hunting. Use when a user wants to find currently listed rentals, compare neighborhoods, build area-specific search keywords, de-duplicate posts, shortlist viable options, or create a contact brief under constraints like budget, rooms, commute, pet policy, building age, elevator, and landlord-vs-agent preference.

Install

openclaw skills install city-rental-hunt

City Rental Hunt

Turn fuzzy apartment-hunting requests into a repeatable workflow:

  1. normalize constraints
  2. generate zone-aware search keywords
  3. search social listings
  4. extract listing facts
  5. filter red flags
  6. produce a shortlist and contact brief

Quick start

When the user asks to hunt rentals in a Chinese city:

  1. Normalize requirements first

    • city
    • target zones
    • budget range
    • room count
    • must-have constraints
    • one-vote vetoes

  2. Generate search keywords

    • Run: 
      python3 skills/city-rental-hunt/scripts/keyword_plan.py \
  --city 北京 \
  --zones "北苑,霍营,清河" \
  --budget "6000-9000" \
  --rooms "两居" \
  --must "整租,电梯,次新" \
  --optional "可养猫,房东直租,转租"
    • This produces reusable search phrases for each zone.

  3. Search platforms in this order

    • First: Xiaohongshu via TikHub
    • Second: Douyin via TikHub
    • Use the existing social-media skill/tooling instead of inventing new scraping flows.

  4. Collect only listing-relevant facts

    • platform
    • post id / URL
    • title / short summary
    • price if present
    • neighborhood / subway / zone
    • freshness
    • landlord / agent / unclear
    • pet policy if present
    • likely keep / maybe / discard

  5. Output a shortlist, not a dump

    • Keep the result decision-oriented.
    • Separate high-confidence leads from weak leads.

Workflow

Step 1 — Normalize the requirement brief

Use this compact schema:

city: 北京
zones: [北苑, 霍营, 清河]
budget: 6000-9000
rooms: 两居
must_have:
  - 整租
  - 电梯
  - 次新/不要老小区
soft_preferences:
  - 客厅大
  - 房东直租
  - 靠近地铁
  - 宠物友好
vetoes:
  - 老小区
  - 合租
  - 商住
  - 非民水民电

If the user gives vague input, infer only the search structure, not the final preference.

Step 2 — Build search buckets by zone

Do not search one giant keyword first. Split by zone.

For each zone, create 3 buckets:

  1. broad: 北苑 整租 两居
  2. quality: 北苑 次新 电梯 两居
  3. conversion: 北苑 房东直租 两居 / 北苑 转租 两居 / 北苑 可养猫

If a known neighborhood appears repeatedly, promote it into its own bucket.

Step 3 — Search Xiaohongshu first

Use TikHub endpoints exposed by the existing social-media skill. Typical flow:

  • check help
  • check list-endpoints xiaohongshu when needed
  • search notes with short, high-signal phrases

Prefer short Chinese queries over long natural-language queries. TikHub/XHS search often degrades on long keyword strings.

Step 4 — Search Douyin as a supplement

Use Douyin only after XHS has produced a first-pass pool.

Douyin is useful for:

  • video walk-throughs
  • “刚空出来” style posts
  • transfer/转租 leads

Do not let Douyin dominate the run unless XHS is thin in that city/zone.

Step 5 — Extract and classify leads

For every lead, classify:

  • keep: fresh, plausibly matches constraints, enough information to contact
  • maybe: missing price / pet policy / building age, but still promising
  • discard: clear red flag

Use the red-flag checklist in references/playbook.md.

Step 6 — Produce two outputs

Output A: analyst-facing search record

Include:

  • keywords used
  • leads found
  • keep/maybe/discard reasoning
  • repeated neighborhoods worth deeper follow-up

Output B: user-facing morning brief

Include only:

  • top leads
  • why they matter
  • what to contact first
  • key uncertainties to verify

Scoring heuristics

Use these dimensions:

  • freshness: today / yesterday / within 7 days / stale
  • constraint fit: rooms, budget, elevator, new-enough community
  • contactability: landlord direct > personal transfer > unclear > obvious agent spam
  • risk: old community, no elevator, price missing, ad tone, commercial apartment, shared rental smell
  • special upside: pet-friendly, unusually concrete price, exact move-in date, strong transit fit

A listing with incomplete price can still rank high if it is very fresh and structurally fits.

What to avoid

  • Do not treat every social post as a real listing.
  • Do not present stale posts as active inventory.
  • Do not bury the user in 30 weak links.
  • Do not confuse “cheap” with “good fit”.
  • Do not publish or embed private commute addresses or personal names when turning a private search workflow into a reusable skill.

Default report shape

Use this structure unless the user asks otherwise:

# Rental hunt brief

## Requirement snapshot

## Zones searched

## Top leads
- lead
- lead
- lead

## Backup leads

## Repeated neighborhoods worth deeper checking

## Risks / unknowns to verify
- price
- pet policy
- landlord vs agent
- building age / elevator

## Contact-first order
1. ...
2. ...
3. ...

When to read the reference

Read references/playbook.md when you need:

  • a fuller keyword-building pattern
  • a reusable evidence schema
  • a red-flag checklist
  • a morning-brief template