Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub Β· AI
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...
skill openclawclawhub Free
Evaluate hi-fi and audio gear options, build system recommendations, guide installation and tuning, and analyze used-market pricing/resale value. Use when us...
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0 stars Unverified but indexed
Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- SKILL.md, scripts/price_stats.py, references/workflows.md, references/checklists.md
Validation
- Use the Yavira download entry.
- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
Install with your agent
Agent handoff
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
- Download the package from Yavira.
- Extract it into a folder your agent can access.
- Paste one of the prompts below and point your agent at the extracted folder.
New install
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.
Upgrade existing
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run.
Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 1.0.0
Provenance
- Publisher
- Ruodong
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
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
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. Gather minimum inputs (ask only missing essentials): Budget range Listening distance / room size Existing gear and connection constraints Music preference and loudness target Return output in this order: Recommendation Why it fits Risks / caveats Next action checklist
Review + Matching Workflow
Capture system context: room, source, use-case (music/movie/desk), volume habits. Match core chain first: transducer (speaker/headphone) -> amp power/current -> source/DAC. Penalize mismatch risks: Low-sensitivity speakers with underpowered amps Bright speaker + bright amp in reflective room Nearfield setup with large floorstanders in tiny rooms Produce 2-3 ranked options: Best value Balanced Stretch option Give upgrade path that preserves resale liquidity. Use references/workflows.md for the detailed template.
Setup + Tuning Workflow
Start with placement before EQ: Symmetry, toe-in, listener triangle, wall distance Solve biggest acoustic problems first: First reflections, bass boom/nulls, desk bounce (for nearfield) Apply light EQ only after physical setup is reasonable. Validate with repeatable test tracks and one objective check (if available). 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)
Normalize listing data by region, condition, accessories, and shipping inclusion. Build a fair-price band using robust statistics (median + IQR). Apply adjustments: No box/accessories: discount Cosmetic issues: discount Recent service with proof: premium Local pickup vs shipped risk: adjust confidence, not only price 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.
Category context
Agent frameworks, memory systems, reasoning layers, and model-native orchestration.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
3 Docs1 Scripts
- SKILL.md
- references/checklists.md
- references/workflows.md
- scripts/price_stats.py