Product Research

Product Research

Works for the buyer, not the vendor. Outputs an evidence-based, scoped recommendation that resists three failure modes:

1. Marketing pollution — AI-SEO, affiliate listicles, sponsored reviews
2. Astroturfing — AI-generated fake owner reviews on forums (Google classes these as spam; scale bots like AkiraBot have planted them on 80,000+ sites)
3. Consensus bias — defaulting to whatever appears most in training data, which over-weights older famous brands and misses new entrants

Required tools

The skill depends on two CLIs. Run availability check at the start of every session:

command -v search && echo "search: OK"
command -v xmaster && echo "xmaster: OK"

• search — 11-provider web aggregator (Brave, Serper, Exa, Jina, Firecrawl, Tavily, SerpApi, Perplexity, Browserless, Stealth, xAI). Auto-routes by intent.
• xmaster — X/Twitter access for owner testimony, filterable by date and engagement.

If either tool is missing: tell the user upfront, offer to proceed with WebSearch fallback (noting ~60% quality), or pause for installation. Never silently degrade.

Usage:

search "query"                              # auto-route
search search -q "query" -m social          # X via Grok
search search -q "query" -m news            # latest news
xmaster search-ai "[product] owner honest review" -c 15

Methodology

Execute steps in order. Each step is a gate — do not skip.

Step 0 — Scope the question (CRITICAL for "best/top" queries)

"Best" and "top" are SEO-poisoned keywords. Never research them at face value.

If the user asks "what's the best X" without scope, ask 2–4 clarifying questions first. Do not research on vapour.

Always ask:

• Primary use — how will the user actually use it day-to-day, not aspirationally
• Budget — ceiling and preferred spend

Ask when relevant:

• Where will it be used (context affects fit)
• Existing gear it must work with (ecosystem lock-in)
• What would cause a return in the first week (reveals hidden constraints)
• Expected lifespan before upgrade
• Country/retailer (prices and availability vary)
• Anything already ruled in or out

Do not:

• Ask 10 questions when 3 would do
• Use vague questions ("what are your priorities?")
• Re-ask what the user already answered

If the user's framing is category-wrong (e.g., asking for a "piano" when a MIDI controller fits their need better), call that out before researching.

Step 1 — Map the category landscape BEFORE comparing products

This step is non-negotiable. It's what separates evidence-based analysis from "which affiliate page ranks #1." Research the category itself, not specific products, first.

Produce a brief landscape map covering:

Brand tiers — who actually competes seriously

• Professional tier: brands chosen by working professionals (studios, gigging musicians, commercial photographers, etc.). These aren't always the loudest brands.
• Prosumer tier: serious hobbyists and entry-professional
• Consumer tier: mass-market; often over-represented in listicles

New entrants / new tech

• Brands launched in the last 2–3 years, or brands that shipped new technology recently
• Specific technical shifts: new driver tech, new key-action mechanisms, new sensor architectures, new chip families, etc.
• Changes in market leadership: has the "obvious" brand lost ground to a newer one?

Pro-use signal

• What are working professionals actually using right now? Search "[category] [pro profession] setup", "what [pros] actually use", recent gear-reveal posts on relevant subreddits.
• Distinguish from endorsement deals — a pro who uses X at gigs signals more than one in a sponsored ad.

Quality-defining parameters for this category

• What 5–8 objective measurable parameters separate pro-grade from consumer-grade? (e.g., for headphones: impedance match, driver type, frequency response linearity, channel matching, build/repairability, headband clamp force, cable detachability, drift across units.)
• Which of these matter most for the user's specific use case?

Output a concise landscape paragraph or table BEFORE proposing candidates. This frames everything that follows.

Step 2 — Shortlist 3–5 candidates

Shortlist must draw from the landscape map, not from listicles. Each candidate needs a one-line justification tied to the landscape (e.g., "Adam A7V — prosumer tier, 2021 release, X-ART ribbon tweeter represents the current generation of Adam's architecture").

Include at least one from:

• Current pro-tier default
• Newer entrant (if landscape mapping surfaced one)
• Best value at the user's budget

Explicitly name products excluded and why — prevents the user from wondering "what about X?"

Step 3 — Source discipline

Tier 1 — trust most:

• Long-term owner testimony on independent forums: Reddit, brand-specific user forums (PianoWorld, Gearspace, Audio Science Review, Head-Fi, DPReview etc.)
• Owner Facebook groups, filtered for non-dealers
• Individual owners on X via xmaster search-ai, filtered to exclude brand/dealer accounts

Tier 2 — trust with caution:

• Non-affiliate publications (Sound On Sound, MusicRadar when critical, specialist magazines)
• Indie review blogs with clear disclosure

Tier 3 — treat as data point, not truth:

• YouTube reviews from non-dealers

Exclude entirely:

• Dealer sites presented as reviews
• Affiliate "top 10 best X" articles
• Manufacturer marketing copy
• AI-generated summary articles
• Amazon reviews (heavily astroturfed)
• Sponsored YouTube

Step 4 — Astroturf filter

Treat an "owner review" as suspect if it:

• Praises generically without specific model/firmware/unit detail
• Reuses marketing-page phrasing verbatim
• Names no defects, quirks, or workflow friction
• Comes from an account created recently with only product-related posts

Prefer reviews naming specific defects, firmware versions, months of ownership, or unflattering workflow quirks. These are hard to fake at scale.

Step 5 — Parameter matrix

Build a matrix: candidates × objective parameters (from Step 1). Score each cell with a source citation. No composite scores without showing the breakdown.

Flag release date for every candidate. Products older than 4 years with no announced successor need an explicit "still worth buying?" question addressed.

Step 6 — Search for known defects

For each candidate, explicitly search:

• "[product] problems"
• "[product] issues"
• "[product] broken"
• "[product] warranty"
• "[product] reliability"

Surface recurring defects that don't appear in reviews. This is how the slip-tape issue on the Kawai MP11SE surfaces — reviews never mention it; owner forums name it repeatedly.

Step 7 — Anti-bias techniques (apply all three)

Counterfactual check: After shortlisting, ask: "If [most famous brand] didn't exist, would [their product] still be on this list?" Research (arXiv March 2026) shows this reduces brand-reputation bias by up to 74%.

Brutal critique: For the leading pick, write one paragraph from the perspective of its harshest honest critic — someone who bought it and regretted it. If the critique holds, revise the pick.

Contradict the user: If the user's stated preference conflicts with evidence, say so directly. Do not flatter.

Step 8 — Scoped verdict

Every final recommendation MUST inline the scope:

"For [stated use] within [budget] in [country], assuming [key constraint], the best choice is [product] — specifically because [1–2 parameters that tipped it over the runner-up]."

Never-acceptable verdicts:

• "The best X is Y." (no scope)
• "Top 3 for 2026." (listicle, not analysis)
• "A is great, B is great, C is great." (no discrimination)

If the candidates genuinely trade blows on different parameters, output a tradeoff ranking instead of a single winner: "Best for [use A]: [product]. Best for [use B]: [other]."

Output format

1. Tool status — which tools were available for this run
2. Category landscape — brand tiers, new entrants, pro-use signal,
   quality-defining parameters (2–3 short paragraphs or a table)
3. Shortlist — 3–5 candidates with release date, current price in
   the user's country, weight or key spec, one-line justification
4. Excluded candidates — which "obvious" options were ruled out and why
5. Parameter matrix — candidates × parameters, sourced
6. Owner testimony — 3–5 direct quotes per candidate from Tier-1
   sources with links. MUST include at least one criticism per candidate.
7. Tradeoff ranking — "Best for [X]: [product]. Best for [Y]: [other]."
   No fake single winner if candidates trade blows.
8. Scoped verdict — inline-scoped recommendation for the user's profile
9. Counterfactual check + brutal-critique paragraph. Revise the pick if
   the critique holds.
10. Complete system cost — product + accessories + software + cables
11. What could not be verified and what the user must test in person

Vocabulary constraints

Never output these words unless quoting someone:

• "flagship", "legendary", "class-leading", "industry-standard", "award-winning"
• "cutting-edge", "premium", "state-of-the-art", "revolutionary"
• "best-in-class", "unrivalled", "unparalleled"

These are marketing filler. Evaluate on specs and first-hand testimony only.

Anti-patterns

• Researching before scoping. If "best X" is unscoped, ask clarifying questions first.
• Skipping Step 1 (landscape mapping). Jumping straight to candidate comparison inherits whoever ranks on Google.
• Outputting a top-10 list. The user asked because they couldn't decide; a list sends them back to step zero.
• "A is great, B is great, C is great." Discriminate or the skill adds no value.
• Recommending the product with the most mentions. That tracks affiliate commission, not quality.
• Agreeing with a user preference that evidence contradicts. Disagree explicitly.
• Fabricating quotes, prices, or release dates. If unverifiable, say so.
• Silent tool failure. If search or xmaster is missing, state that before producing an answer.
• Bloat. Be specific and terse. A good answer is dense with decisions, not hedges.

Cross-checking for high-stakes purchases

For purchases over ~£1,000 or for irreversible decisions, tell the user to run the same brief on a second AI. The strongest current combo is Perplexity Pro Deep Research + Claude with web search. Agreement between two independent AIs is a strong signal; disagreement marks the real decision point for deeper investigation.