Skill: ruodong/hifi-advisor (version 1.0.0)

--- name: hifi-advisor description: Evaluate hi-fi and audio gear options, build system recommendations, guide installation and tuning, and analyze used-market pricing/resale value. Use when users ask for speaker/amp/DAC matching, room setup, placement, EQ/tuning checklists, buying advice, scam-risk checks, or fair-price analysis for second-hand audio gear (Hi-Fi, headphone rigs, home stereo). --- # HiFi Advisor ## 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: ```bash 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.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: suspicious | SkillSpector: clean (LOW)