# Product Research > Researches, compares, and recommends products before a purchase — any physical product, SaaS, gadget, instrument, software, or service. Triggers on "should I buy X", "X vs Y", "recommend a [product]", "what's the best [category]", "before I buy", "is [product] worth it", "help me decide between X and Y", or any shopping-intent phrasing with a budget or use case. Does NOT trigger for post-purchase support, feature-only questions from existing owners, or abstract "best of all time" trivia with no user context. - Skill: `199-biotechnologies/product-research` (Agent Skill, multi-file: 4 files) - Install (CLI): `npx skillmds@latest add 199-biotechnologies/product-research` - Raw SKILL.md: https://api.skillmd.com/api/skills/199-biotechnologies/product-research/raw - Safety review: pending (external: skill-scanner PASS, skillspector PASS) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Research & Search - Author: 199-biotechnologies (https://skillmd.com/u/199-biotechnologies) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/199-biotechnologies/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: ```bash 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: ```bash 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.