HiFi Advisor

Evaluate hi-fi and audio gear options, build system recommendations, guide installation and tuning, and analyze used-market pricing/resale value. Use when us...

Overview

Deliver practical, decision-ready guidance for hi-fi purchase, setup, tuning, and pricing tasks. Prioritize low-risk recommendations, explicit trade-offs, and actionable next steps.

Quick Workflow Decision

1. Identify user intent:
   • Buy/compare gear -> run Review + Matching workflow.
   • Install or improve sound -> run Setup + Tuning workflow.
   • Check second-hand deal value -> run Price Analysis workflow.
2. Gather minimum inputs (ask only missing essentials):
   • Budget range
   • Listening distance / room size
   • Existing gear and connection constraints
   • Music preference and loudness target
3. Return output in this order:
   • Recommendation
   • Why it fits
   • Risks / caveats
   • Next action checklist

Review + Matching Workflow

1. Capture system context: room, source, use-case (music/movie/desk), volume habits.
2. Match core chain first: transducer (speaker/headphone) -> amp power/current -> source/DAC.
3. Penalize mismatch risks:
   • Low-sensitivity speakers with underpowered amps
   • Bright speaker + bright amp in reflective room
   • Nearfield setup with large floorstanders in tiny rooms
4. Produce 2-3 ranked options:
   • Best value
   • Balanced
   • Stretch option
5. Give upgrade path that preserves resale liquidity.

Use references/workflows.md for the detailed template.

Setup + Tuning Workflow

1. Start with placement before EQ:
   • Symmetry, toe-in, listener triangle, wall distance
2. Solve biggest acoustic problems first:
   • First reflections, bass boom/nulls, desk bounce (for nearfield)
3. Apply light EQ only after physical setup is reasonable.
4. Validate with repeatable test tracks and one objective check (if available).
5. End with a short "do not change all at once" iteration plan.

Use references/checklists.md for step-by-step checklists.

Price Analysis Workflow (Used Market)

1. Normalize listing data by region, condition, accessories, and shipping inclusion.
2. Build a fair-price band using robust statistics (median + IQR).
3. Apply adjustments:
   • No box/accessories: discount
   • Cosmetic issues: discount
   • Recent service with proof: premium
   • Local pickup vs shipped risk: adjust confidence, not only price
4. Output:
   • Fair range
   • Strong-buy threshold
   • Walk-away threshold
   • Risk flags

If user provides tabular listing data, run:

python3 scripts/price_stats.py listings.csv

Expected columns: price plus optional platform,condition,model,date,notes.

Output Quality Standard

Always provide:

• A clear recommendation (not just raw data)
• 3-5 bullet rationale
• Top risk factors
• Concrete next steps the user can execute today

Use concise language. Avoid mystical audiophile claims. Prefer testable, practical guidance.