run-house-hunt

name	run-house-hunt
description	Run the full house-hunting AI agent pipeline for a buyer brief — intake, ranking, explanation, comparison, affordability, and tour prep. No HTTP server, browser, or external model API key needed.
metadata	{"tags":"house-hunt, harness, ranking, comparison, buyer-agent"}

Run House Hunt Skill

Run the full buyer-agent pipeline from a plain-English brief: parse preferences, rank listings, explain matches, compare homes, and prepare next steps.

Prerequisites (check before running)

• Python 3.10+ and uv
• Run commands from the harness root
• No model provider key is required when an LLM agent is using this skill; the agent supplies the reasoning. If ANTHROPIC_API_KEY is exported, the standalone harness may use its optional Anthropic adapter for intake and explanations.
• No HomesToCompare API key is required for this skill; it uses local harness functions and mock or provided listing data.

Your task

When the user invokes this skill:

Step 1 — Identify the buyer brief

Extract it from the user's message. If none was given, ask for one. A good brief includes:

• Location or commute destination
• Budget (e.g. £650k)
• Bedrooms (e.g. 3-bed)
• Key priorities (garden, quiet street, schools, parking, etc.)

Example: "3-bed near Surbiton, budget £650k, max 45 min commute to Waterloo, need a garden"

Step 2 — Run the pipeline

Call the orchestrator directly via Python (run from the harness root directory):

Note: Use uv run --extra dev so the repo dependencies are available without separately installing the package.

uv run --extra dev python - <<'EOF'
from src.app import build_app

brief = "BRIEF_GOES_HERE"

app = build_app()
profile = app.intake(brief)
ranked = app.triage(limit=5)
explanations = app.explain_top_matches()
comparison = app.compare_top(count=min(3, len(ranked)))
next_steps = app.prep_next_steps()

print("## Profile")
print(f"  Location: {profile.location_query}")
print(f"  Budget:   £{profile.max_budget:,}")
print(f"  Bedrooms: {profile.min_bedrooms}+")
if profile.max_commute_minutes:
    print(f"  Commute:  {profile.max_commute_minutes} min max")
print(f"  Must-haves: {', '.join(profile.must_haves) or 'none'}")

print("\n## Ranked listings")
for i, item in enumerate(ranked, 1):
    listing = item.listing
    print(
        f"  {i}. {listing.title}  [{item.score:.0f}/100]  "
        f"£{listing.price:,}  {listing.bedrooms}bed  {listing.location}"
    )
    if item.warnings:
        print(f"     ! {', '.join(item.warnings)}")

print("\n## Explanations")
for explanation in explanations:
    print(f"  {explanation}")

print("\n## Comparison")
print(comparison)

affordability = next_steps["affordability"]
print("\n## Affordability estimate")
print(f"  Deposit:  £{affordability.deposit:,}")
print(f"  Loan:     £{affordability.loan_amount:,}")
print(f"  Monthly:  ~£{affordability.monthly_payment:,}/month")
print(f"  Note:     {affordability.assumptions[-1]}")

print(f"\n{next_steps['boundary']}")

trace = app.tracer.flush("skill-run")
print(f"\nTrace: {trace}")
EOF

Replace BRIEF_GOES_HERE with the actual buyer brief.

Step 3 — Export results (optional)

Via Python, pass ExportOptions directly to app.export() after ranking results.

Step 4 — Report back

Show the user:

• The parsed BuyerProfile (confirm the brief was understood correctly)
• The ranked listing table (title, score, price, location, warnings)
• The comparison summary (top 3 head-to-head)
• The affordability estimate for the top match (monthly payment, deposit, loan)
• The trace file path for inspection

Troubleshooting

Error	Cause	Fix
ModuleNotFoundError: src	Not running from harness root	cd house-hunting-ai-agent-harness first
No listings matched	Location query too specific for mock data	Try London/King's Cross, Manchester, Bristol, or Leeds examples
Regex-style intake/explanations	No standalone harness LLM adapter configured	Expected for agent-driven use; the calling LLM agent can interpret and summarize results

Key files

File	Purpose
src/app.py	build_app() — wires config, LLM, listings, orchestrator
src/harness/orchestrator.py	Full pipeline: intake → triage → explain → compare → next steps
src/skills/intake.py	Brief → BuyerProfile (regex or LLM)
src/skills/ranking.py	Score listings against profile
evals/datasets/listings_small.jsonl	Mock listing data across London, Manchester, Bristol, and Leeds
.env.example	All supported environment variables